Hospital-based specialist palliative care compared with usual care for adults with advanced illness and their caregivers: a systematic review

Article history

The research reported in this issue of the journal was funded by the HS&DR programme or one of its preceding programmes as project number 16/02/17. The contractual start date was in May 2017. The final report began editorial review in January 2019 and was accepted for publication in November 2020. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HS&DR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.

Copyright statement

Copyright © 2021 Oluyase et al. This work was produced by Oluyase et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This is an Open Access publication distributed under the terms of the Creative Commons Attribution CC BY 4.0 licence, which permits unrestricted use, distribution, reproduction and adaption in any medium and for any purpose provided that it is properly attributed. See: https://creativecommons.org/licenses/by/4.0/. For attribution the title, original author(s), the publication source – NIHR Journals Library, and the DOI of the publication must be cited.

2021 Oluyase et al.

Rationale

The global burden of disease has increased because of a number of factors such as increased longevity, reduced childhood and infant infectious disease mortality and global demography of lowered fertility. This increase has taken its toll on health-care systems worldwide. 1 Most adults develop chronic morbidities with which they may live for many years before they die. As well as increased clinical complexity, an ageing population has further led to increasing health-care costs internationally. This has occurred in spite of measures aimed at reducing health-care resource use and cost in many developed countries, including the UK2 and the USA. 3

Arguably, the introduction or expansion of new services in hospitals, such as specialist palliative care, and rising staff costs contribute to this increased expenditure. Specialist palliative care in hospital is likely to keep growing because most older people (i.e. aged ≥ 65 years) still die in hospitals (71% of all hospital deaths in the USA),4 with most deaths resulting from terminal illnesses,5 and also because deaths in institutional care persist into older stages of life, with one in five centenarians dying in hospital. 6 By 2040, it is estimated that, in the UK, roughly 160,000 more people will have palliative care needs, including pain control and end-of-life care in hospitals, hospices and at home. 7 Cost-effective commissioning of end-of-life resources is now a priority globally and also in the UK. 8 Available evidence suggests that hospital-based specialist palliative care (HSPC) may improve clinical outcomes and quality of care and may potentially reduce hospital care expenditure. 9 In addition, specialist palliative care, which includes bereavement care and preparatory grief work, could assist unpaid caregivers to access the care they need following the death of a loved one. 10

Generally, inpatient hospital palliative care teams are increasing. 11,12 From 2000 to 2016, palliative care prevalence in hospitals with ≥ 50 beds in the USA increased by 178%,13 yet there is a lack of clarity on the effective components of HSPC. This review will provide clarity regarding the effectiveness and cost-effectiveness of HSPC. Five different models of HSPC were specified because it is an evolving area and also to make this review more relevant to clinical practice. The models of HSPC that were eligible were ward-based models, inpatient consulting models, outpatient models, hospital at home or hospital outreach models (hereafter outreach model), and service provision across multiple settings that included hospital.

The rationale for undertaking this systematic review is as follows: first, there is increasing evidence that aggressive, and sometimes futile, treatments are being used with patients in acute hospitals at the end of life. 14 These treatments may lead to negative clinical, financial and utilisation outcomes,15 and may not be what the patient wants. 16 Consequently, this review is important in order to determine how to improve care and also reduce costs. Second, given that the number of HSPC teams is increasing without a robust evidence base, this review addresses the gap by providing clarity on the effectiveness, and optimal components and models of HSPC.

A previous systematic review9 showed that HSPC improved clinical outcomes and quality of care and can reduce hospital costs. However, this review was small (nine studies) and included only cancer patients. A 2017 review17 in hospital, hospice or community settings found that specialist palliative care led to an improvement in quality of life with significant benefits for patients with cancer receiving specialist palliative care early. Results for pain and other outcomes were inconclusive. The 2017 Cochrane review18 found that early palliative care interventions led to significantly better quality of life and reduced symptom intensity, compared with the control group. Depression levels and survival were not significantly different between the early palliative care group and the control group. To our knowledge, no review had been carried out on specialist palliative care provided in hospital inpatient, outpatient and outreach settings, as well as multiple settings that include hospital.

The UK government10 and commissioning guidance19 have recommended that 24/7 palliative care service should be provided. However, the recent End of Life Care Audit 201620 showed that, of the 142 acute NHS trusts in England that participated, only 37% had provision for out-of-hours specialist palliative care services, and that there was variation in the health professionals involved and the level of contact (telephone or on-site visiting). The James Lind Alliance further highlighted the need for research into identifying the core palliative care services needed and the best way of providing out-of-hours palliative care. 21 This systematic review addressed these important priorities.

Description of the condition

Population-based estimates of specialist palliative care have highlighted the types of patients who require this service. 22 They include those with malignant neoplasms and non-malignant and other health-related conditions, specifically heart disease, including cerebrovascular disease; liver disease; renal disease; respiratory disease; neurodegenerative disease (Huntington’s disease, Parkinson’s disease, multiple sclerosis, motor neurone disease, multisystem degeneration, progressive supranuclear ophthalmoplegia, dementia due to Alzheimer’s disease, and senility); and human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS).

Description of the intervention

The intervention in this systematic review is HSPC. HSPC refers to care that is provided with the input of specialist palliative care providers to patients while they are admitted as inpatients to acute care hospitals, outpatients or patients receiving care from hospital outreach teams at home. It includes interventions delivered to patients with advanced,23 life-limiting24 or life-threatening illness,25 which is likely to affect their quality of life adversely. 26 The intervention aims to prevent or alleviate physical, social, psychological and spiritual problems. Patients receiving the intervention may have malignant and/or non-malignant conditions and they may or may not be at the end of their life. 27

In this review, HSPC has the following important features:

• care co-ordinated by a multiprofessional or multidisciplinary team (MDT)

• collaboration between specialist palliative care providers and generalist providers

• holistic care. 25

Specialist palliative care is not the same as generalist palliative care. Specialists are likely to have specialist training in palliative care, and the services they provide are mainly for those with palliative care needs; conversely, generalists provide palliative care as part of wider services. 28 Recipients of specialist care are mostly patients with advanced, life-limiting or life-threatening illness who present with complex needs. 24 Complex needs encompasses clinical complexity and its interaction with the confidence or ability of the lead clinical team (generalists) to address the presenting need. Complexity could be as a result of the disease, ethical complexity or both. Complexity usually involves multiple factors, related to the serious nature of illness, age, social or familial backgrounds, and/or the nature of a symptom (e.g. the usualness or intractable nature of the symptom). 24,29 The way in which specialist palliative care is defined differs between countries and there is sometimes little or no detail on the training of the palliative care team. Consequently, this review included studies for which training/clinical experience in specialist palliative care was clearly stated, as well as those that simply stated the involvement of a palliative care team with eligibility informed by activity of delivering specialist palliative care, rather than level of specialist training. 30 Specialist training in palliative care was accepted if the authors stated that the professionals were palliative care experts or specialists (e.g. palliative care physician or nurse) or if they had obtained clinical competencies and professional characteristics required for the delivery of specialist palliative care through clinical experience. 19 The intervention should be delivered to patients receiving hospital inpatient, outpatient, outreach or HSPC as part of wider services, and their caregivers/families. Recognising the importance of the informal caregiver, palliative care also aims to meet the psychological, social and spiritual needs of caregivers. 31

Specialist palliative care provided to unpaid caregivers in any of the previously mentioned settings was also included in this review. Unpaid caregivers may be seen by hospital staff to address their pre-bereavement needs. Pre-bereavement interventions are specialist palliative care interventions provided to address bereavement-related physical, psychosocial and spiritual problems experienced by unpaid caregivers before a patient’s death. However, not all services provide pre-bereavement interventions. 32–34 Specialist palliative care interventions involving pre-bereavement interventions delivered to the unpaid caregiver alone or together with the patient were included.

Models of hospital-based specialist palliative care

Five different models of HSPC were specified because of their varied nature and also to cover different types of services. They were as follows:

1. ward-based models comprising care provision to patients and their caregivers on a palliative care ward in hospital

2. inpatient consulting models comprising care provision by an inpatient consult team to patients and their caregivers when admitted as inpatients to hospitals

3. outpatient models comprising care provision to hospital outpatients and their caregivers

4. hospital at home or hospital outreach into the community comprising care provision by hospital outreach teams in a patient’s home

5. models involving multiple settings including hospital.

How the intervention might work

Although HSPC can lead to benefits, such as improved quality of care, symptom control and care co-ordination, and to a reduction in hospital expenditure, qualitative methods such as interviews and empirical testing have yet to clarify how HSPC might work. Consequently, proposed mechanisms by which HSPC may work are only speculative. HSPC may work with patients through the following means:

• directly improving symptoms through specialist interventions and holistic care35

• improving care quality and the tenor of care through assisting patients, unpaid caregivers and staff by delivering or facilitating improved care co-ordination and person-centred holistic care36,37

• reducing futile medical interventions and enabling patient dignity and autonomy38

• reducing unnecessary hospital costs by decreasing medication, laboratory and intensive care unit (ICU) costs39

• addressing holistic needs, including multimorbidity. 40

The results from a systematic review41 and randomised controlled trials (RCTs)42,43 further highlighted that the intervention may support caregivers prior to a patient’s death through emphasising the positive aspects of caregiving by providing information and guidance, increasing caregiving competencies and knowledge, helping caregivers to understand their circumstances and supporting their emotional reactions to the demands of caregiving, and improving involvement in care planning. 43,44 Involving both patients and caregivers in life review in consultations may help to decrease the stress caregivers experience. 42 The intervention may also help caregivers to see problems in a new light, improving coping and planning, and providing them with access to expert information. This has been shown to improve their quality of life overall, while also decreasing caregiver burden and tasks. 45

Objective

The objective was to assess the effectiveness and cost-effectiveness of HSPC for adults with advanced illness and their unpaid caregivers.

Changes from the protocol

There were some changes from the published protocol46 in the review.

Data analysis and assessments

We added early versus late palliative care as a subgroup analysis. This was recommended for inclusion in the review by clinical experts because of its relevance to practice. Although we had initially specified in the published protocol that pain and other outcomes presented as binary data would be treated as binary outcomes, this was not possible, as most studies presented their outcomes as continuous data. The only outcome for which we were able to calculate odds ratios and 95% confidence intervals (CIs), in addition to standardised mean differences (SMDs), was patient depression. These changes were carried out before data extraction and analysis.

We expanded the risk-of-bias methods by carrying out separate assessments for all subjective outcomes (e.g. HRQoL) and all objective outcomes (e.g. mortality). When studies did not include either subjective or objective outcomes, we left the domain that was not included blank. We added the domain ‘other’ in the full review.

We had planned to use either a fixed-effects or a random-effects model for meta-analysis. Owing to the different models of HSPC in our review, we presented only random-effects models, as we are estimating the average effect across HSPC, rather than any single true effect. We had planned to estimate an intracluster correlation coefficient (ICC) when the authors of cluster RCTs did not carry out adjustment or provide an ICC. However, we decided to use an estimate of ICC that we obtained from a previous study in adjusting for clustering in McCorkle et al. 48 We contacted the authors of McCorkle et al. 48 for their ICC, but, at the time of writing, they had not responded. In the protocol,46 we stated that we will contact the original investigators for missing data and that we will describe any strategy used for imputing missing data. We decided to contact authors for missing data only without carrying out imputations, as this is the preferred method for dealing with missing data. 49 We initially wanted to explore reasons for heterogeneity in sensitivity analyses. However, Cochrane editors recommended the use of subgroup analysis for assessing heterogeneity. Consequently, we explored heterogeneity using subgroup analysis, whereas we used sensitivity analysis to test the estimate we used in adjusting for clustering in the cluster RCT. As we did not include non-randomised studies, we did not have to pay particular attention to selection bias and reporting bias in such studies. We did not carry out a subgroup analysis assessing provision of single or few components of HSPC because very few studies provided a single component of HSPC.

In the published protocol,46 we stated that we were going to search two health economic databases to identify additional studies. However, we could search the NHS Economic Evaluation Database (NHS EED) only, because it was not possible for us to access the European Network of Health Economic Evaluation Databases (EURONHEED). We contacted the authors of the EURONHEED project, but did not receive any response.

Given that combining end-point scores and change scores is not recommended when using SMDs, and also that Cochrane does not recommend pooling adjusted and unadjusted estimates together, we pooled studies presenting adjusted end-point scores as our main meta-analysis, and we carried out sensitivity analyses with studies reporting unadjusted end-point scores, adjusted change scores and unadjusted change scores. This was a change from the protocol, based on advice from Cochrane editors.

We decided to present only one summary of findings table, rather than three, for the comparison of HSPC versus usual care, as experts in the project advisory group advised that this comparison alone would be the most informative for decision-makers. Compared with the protocol, which included only cost-effectiveness in the summary of findings table, we report the results for both cost and cost-effectiveness in the summary of findings table in this review (see Table 2).

Inclusion and exclusion criteria

Studies were assessed for eligibility based on the criteria described in the subsequent sections.

Identification of literature

Synthesis

Search results

The number of records identified through searches of databases and other sources was 10,774, excluding duplicates. On screening the titles and abstracts, we excluded 10,132 records and selected 642 for full-text reading (see Figure 1 for the PRISMA flow diagram). We excluded 536 records for various reasons (see Figure 1). We included 42 studies reported in 106 records (91 full papers and 15 abstracts), ranging from one to 10 records per study (see Figure 1 for the PRISMA flow diagram).

Excluded studies

A total of 536 records assessed for eligibility were excluded for various reasons (see Figure 1). See Appendix 2 for the list of excluded studies. The study awaiting classification is an abstract by Aljohani69 that had insufficient information on the palliative care team and its setting. The author could not be contacted.

Unit-of-analysis issues

Two studies were cluster randomised trials,48,70 of which one was a cluster randomised crossover trial. 70 Adjustment was made for clustering by Ma et al. 70 In McCorkle et al. ,48 the authors did not adjust for clustering. Therefore, we adjusted the data entered into the meta-analysis using 0.02 as the ICC. We obtained this estimate from a previous Cochrane review. 71 We opted to use this estimate because we contacted the authors for an estimate of the ICC, but did not receive it.

Characteristics of included studies

All included studies were RCTs, comprising one cluster RCT,48 one cluster randomised crossover trial,70 eight fast-track RCTs72–79 and 32 RCTs with parallel design. Table 1 presents the characteristics of included studies (see Report Supplementary Material 1, table 1, for more details on the characteristics of included studies). Appendix 3 provides further descriptions of the intervention and control conditions in each study and the outcomes measured.

Design

All included studies were RCTs. They included one cluster RCT,48 one cluster randomised crossover trial70 and eight fast-track RCTs. 72–79 The remaining 32 studies were parallel-designed RCTs. The HSPC models were offered in different ways, namely:

• ward-based services, provided by Jingfen et al. 80 only

• inpatient consult or advisory services, provided by 10 studies70,81,82,84,85,88,89,93,95,96

• outpatient services, provided by six studies35,97,101,103,106,116

• hospital outreach services, provided by five studies72,79,118,123,126

• models involving multiple settings including hospital, provided by 20 studies. 48,73–78,129,139,142,147,148,156,160,161,163,165,167,168,170

One of the criteria for inclusion of studies in this review is that care should be co-ordinated by a multiprofessional or multidisciplinary team. All included studies either had a MDT as the core team delivering the intervention or included a MDT as needed. HSPC teams were also divided into two based on whether or not the intervention was led by a single professional or by a MDT. Seven studies72,97,103,106,129,160,168 were led by nurses (nurse-led MDTs); no study was physician led. Thirty-four studies were led by MDTs; in one study,101 it was unclear. There was provision for out-of-hours care in five studies. 79,88,118,142,160 In McCaffrey et al. ,160 services traversed multiple settings including hospital, and there was provision for nursing care for up to 24 hours per day for 5 days. The hospital outreach service by McWhinney et al. 79 included 24-hour on-call service, whereas another hospital outreach service, by Brännström et al. ,118 involved close co-operation with out-of-hours palliative advanced home care. Brumley et al. 142 involved service provision across multiple settings including hospital, and also 24-hour on-call service. Gade et al. 88 included a palliative care physician on call after hours in their inpatient consult service.

Intervention

Effects of hospital-based specialist palliative care

Table 2 provides the summary of findings of the intervention on the key outcomes in this review.

Primary outcomes

Patient symptom burden (as a collection of two or more symptoms)

As our main meta-analysis, we pooled data from six studies that presented adjusted end-point values. We found significant improvement in patient symptom burden with HSPC, compared with usual care (six studies, 761 participants, SMD –0.26, 95% CI –0.41 to –0.12; I² = 0%, random effects) (Figure 12). Negative SMDs indicate benefit (lower symptom burden) and positive SMDs reflect greater symptom burden.

FIGURE 12.

Effect of HSPC on patient symptom burden: adjusted end-point values. a, Data from the severity subscale of the Memorial Symptom Assessment Scale was used in meta-analysis. df, degrees of freedom; IV, inverse variance; random, random-effects model.

We carried out sensitivity analyses with unadjusted end-point values, adjusted change values and unadjusted change values, and also assessed the impact of using an ICC of 0.02 in adjusting for clustering in McCorkle et al. 48

A sensitivity analysis using unadjusted end-point values showed a pooled effect of SMD –0.17 (six studies, 833 participants, 95% CI –0.54 to 0.20; I² = 83%) (Figure 13).

FIGURE 13.

Effect of HSPC on patient symptom burden: unadjusted end-point values. a, 95% CI estimated from graph. df, degrees of freedom; IV, inverse variance; random, random-effects model.

When we excluded McCorkle et al. ,48 we had similar results (five studies, 769 participants, SMD –0.19, 95% CI –0.62 to 0.24; I² = 87%) (Figure 14).

FIGURE 14.

Effect of HSPC on patient symptom burden: unadjusted end-point values (excluding McCorkle et al. 48). a, 95% CIs were estimated from graph. df, degrees of freedom; IV, inverse variance; random, random-effects model.

When we considered adjusted change values, the pooled effect was a SMD of –1.31 (four studies, 353 participants, 95% CI –3.27 to 0.64; I² = 98%) (Figure 15).

FIGURE 15.

Effect of HSPC on patient symptom burden: adjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

When we excluded McCorkle et al. ,48 we found a pooled effect of SMD –1.79 (three studies, 289 participants, 95% CI –4.29 to 0.70; I² = 98%) (Figure 16).

FIGURE 16.

Effect of HSPC on patient symptom burden: adjusted change values (excluding McCorkle et al. 48). df, degrees of freedom; IV, inverse variance; random, random-effects model.

When we pooled unadjusted change values, we had a SMD of –0.44 (six studies, 641 participants, 95% CI –0.94 to 0.06; I² = 88%) (Figure 17).

FIGURE 17.

Effect of HSPC on patient symptom burden: unadjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Across the studies in the meta-analyses, we combined different generalised measures of patient symptom burden using SMDs. Studies assessed patient symptom burden using the following scales: POS, or a modified form of it;72,74,77,78,126 African POS;97 Edmonton Symptom Assessment Scale (ESAS), or a modified form of it;85,95,96,129 symptom impact subscale of the Quality of Life at End of life;73 General Symptom Distress Scale;139 physical area scale of the MCOHPQ;88 Symptom Distress Scale;48,170 Rotterdam Symptom Checklist (Physical Symptoms Score);106 lung cancer subscale of the FACT-L;35 and Memorial Symptom Assessment Scale. 163 Only the severity subscale of the Memorial Symptom Assessment Scale, reported by Rodin et al. ,163 was used in the meta-analysis.

Of the remaining 25 studies that were not in any of the meta-analyses, 20 did not report on patient symptom burden;70,75,76,79–82,84,89,93,101,104,116,142,147,148,160,165,167,168 two studies reported that there were ‘no significant differences’ between the intervention and control groups, but they did not present data;118,156 and O’Riordan et al. 161 did not present data from the ESAS. Wallen et al. 170 did not present analysable data, and Janssens et al. 123 assessed symptom burden using the ESAS in the intervention group only.

Given that there were fewer than 10 included studies in the main meta-analysis of studies that presented adjusted end-point values, we did not use funnel plots or carry out tests for funnel plot asymmetry. We also did not carry out subgroup analysis because of a lack of heterogeneity (I² = 0%) in the main meta-analysis.

Secondary outcomes

Caregiver satisfaction with care

Four studies assessed the effect of HSPC on family satisfaction with care. 82,84,95,156 We excluded Cheung et al. 84 and Ozcelik et al. 95 from the meta-analysis because they used non-validated family satisfaction measures.

Two studies82,156 used validated measures with a total of 408 participants. Carson et al. 82 was the only study that presented adjusted end-point values, with family satisfaction assessed using the Family Satisfaction in the Intensive Care Unit (FS-ICU) survey (range 0–100, 100 = best caregiver satisfaction). They found no between-group difference between the HSPC and usual-care groups. The mean satisfaction was 81.1 (95% CI 78.3 to 83.9) in the HSPC group and 84.3 (95% CI 81.3 to 87.3) in the usual-care group, with a difference of –3.1 (95% CI –7.3 to 1.0) between groups (p = 0.13).

Kane et al. 156 did not present their data. They reported only p-values in favour of the HSPC group in two of the five cohorts they assessed. Kane et al. 156 assessed caregiver satisfaction with care using the interpersonal care scale adapted from the Ware scale,184 a physical environment scale based on that of McCaffree and Harkins185 and involvement-in-care questions adapted from the National Cancer Institute’s hospice study. 186

Achieving patient preferred place of death (measured by number of patients with home death)

We decided to use number of home deaths as a proxy measure for achieving preferred place of death, because most people in developed countries prefer to die at home. 47

Achieving patient preferred place of care

Only Bajwah et al. 72 (47 participants) reported on this outcome. Bajwah et al. 72 was a fast-track RCT. Patients in the intervention group received HSPC immediately after randomisation, whereas the control group received HSPC 4 weeks after randomisation. Consequently, both the intervention and control groups received HSPC. Results at the end of the study showed that all eight patients (100%) who died in the intervention group achieved their preferred place of care, and 11 patients (84%) in the control group who received HSPC after 4 weeks achieved this.

Mortality/survival

Thirty six studies, with 7103 participants, reported on mortality/survival35,48,70,72–79,81,82,84,85,88,89,93,96,97,106,116,118,123,126,129,139,142,147,148,156,160,161,165,167,168 (see Appendix 9). We decided against pooling their hazard ratios (HRs) in a meta-analysis because of methodological limitations in the included studies.

Three studies did not report on the number of deaths,95,101,170 and Nottelmann et al. 104 reported the number of deaths in the HSPC group only. There were no deaths in the study by Rodin et al. ,163 and this was unclear in the foreign language study by Jingfen et al. 80 because it was not stated in the study.

Ten studies reported on deaths in the HSPC and control groups without presenting survival time, and they found no between-group difference in the number of deaths. 70,77,81,84,93,118,139,147,160,165 Sidebottom et al. 96 found no association between study group assignment and death within 6 months, after adjustment for age, sex and marital status (HR 1.90, 95% CI 0.88 to 4.09; p = 0.101). Sidebottom et al. 96 reported 14 deaths (12.1%) in the HSPC group and five deaths (4.3%) in the control group.

In 11 studies, it was unclear if there was any significant difference in mortality because the p-values were not presented. 48,72,74–76,79,85,97,126,161,167 McWhinney et al. 79 presented the total number of deaths at 1 month only [n = 36 (24.7%)], but did not report the numbers in the HSPC and control groups.

In the studies that reported survival time, there was no significant difference between HSPC and usual care on survival. 73,82,88,89,116,123,129,148,156,168 In Bakitas et al. ,129 the median survival time was 14 months (95% CI 10.6 to 18.4 months) in the HSPC group and 8.5 months (95% CI 7 to 11.1 months) in the control group, with a p-value of 0.14. There were 112 deaths (69.6%) in the HSPC group and 119 deaths (73.9%) in the control group. The Cox proportional hazards model estimate demonstrated a reduced relative risk of death (HR 0.67, 95% CI 0.496 to 0.906; p = 0.009) in the HSPC group during the first year of the study and a greater relative risk after 1 year (HR 1.56, 95% CI 0.908 to 2.655). In Bakitas et al. ,73 a fast-track RCT in which the intervention group was offered HSPC immediately, whereas the control group received HSPC after 3 months, the median survival time by the end of data collection in the intervention group was 18.3 months, and it was 11.8 months in the control group, which began HSPC 3 months later. Kaplan–Meier curves illustrate a 15% difference in survival at 1 year (HSPC, 63% vs. control, 48%; p = 0.038). However, the overall log-rank test p-value was 0.18, suggesting a convergence in overall survival after 12 months. At 1 year, there were 109 deaths (52.7%), but numbers in intervention and control groups were not stated. Carson et al. 82 reported a median survival time of 19 days (95% CI 12 to 37 days) in the HSPC group and 23 days (95% CI 12 to 39 days) in the control group (p = 0.51). There was no difference in 90-day survival (HR 0.95, 95% CI 0.65 to 1.38; p = 0.96). Post hoc adjustment for baseline activities of daily living and study site did not alter the outcome (HR 1.01, 95% CI 0.69 to 1.47; p = 0.96). In Grudzen et al. ,89 the median survival time was 289 days (95% CI 128 to 453 days) in the HSPC group and 132 days (95% CI 80 to 302 days) in the control group, with a p-value of 0.2. At 1 year, 41 participants (59.4%) had died in the HSPC group and 44 (65.7%) had died in the control group. However, there was no difference between the groups (p = 0.20). Janssens et al. 123 was not clear about whether they were reporting mean or median survival. Survival was 454 days (95% CI 382 to 525 days) in the HSPC group and 425 days (95% CI 339 to 509 days) in the control group (log-rank test, p-value of 0.91). In the follow-up period in Janssens et al. ,123 there were four deaths (15.4%) in the HSPC group and four deaths (17.4%) in the control group. Kane et al. 156 reported no difference in survival time between the HSPC and control groups, as the survival curves were similar. In Gade et al. ,88 the median survival was 30 days [interquartile range (IQR) 6–104 days] in the HSPC group and 36 days (IQR 13–106 days) in control group (p = 0.08). There were 173 deaths (63%) in the HSPC group and 132 deaths (56%) in control group during the study period. Groenvold et al. 148 reported that survival time did not differ between the HSPC and control groups. The median survival time was 323 days in the HSPC group and 364 days in the control group (p = 0.16, but in the adjusted analysis p = 0.39). There were 25 deaths (27%) in the HSPC group and 22 deaths (23%) in the control group. Woo et al. 116 reported that there was no difference in survival between the HSPC and usual-care groups, but did not present any data. Vanbutsele et al. 168 found the median survival time to be 312 days (95% CI 190 to 434 days) in the HSPC group and 343 days (95% CI 253 to 433 days) in the control group (p = 0.97). Sidebottom et al. 96 reported no association between study group assignment and death within 6 months after adjusting for age, sex and marital status (p = 0.10).

Two studies35,78 found significantly longer survival in the HSPC group than in the usual-care group. Higginson et al. 78 was a fast-track RCT in which the intervention group received HSPC immediately, whereas those in control group were offered HSPC after 6 weeks. Survival was calculated from the time of randomisation to the time of death, if death occurred during the study period, or to the time of censoring. The median survival time from randomisation to the time of censoring was 745 days (range 338–1075) days in the intervention group and 711 days (range 345–1045 days) in the control group, which received HSPC after 6 weeks (p = 0.048). In a subgroup analysis, this pattern was not recorded for patients with cancer (p = 0.97), but it became more marked for patients with diseases other than cancer (p = 0.01). Temel et al. 35 reported that median survival time was 11.6 months (95% CI 6.4 to 16.9 months) in the HSPC group and 8.9 months (95% CI 6.3 to 11.4 months) in the control group (log rank p = 0.02). After adjustment for age, sex and baseline Eastern Cooperative Oncology Group performance status, the group assignment remained a predictor of survival (HR for death in the standard care group 1.70, 95% CI 1.14 to 2.54; p = 0.01).

On the other hand, Brumley et al. 142 and Tattersall et al. 106 reported greater survival in the control group than in the HSPC group. Brumley et al. 142 reported a mean survival of 242 days (SD 200 days) in the control group, compared with 196 days (SD 164 days) in the HSPC group (p = 0.03). However, results of the Kaplan–Meier survival analysis did not show differences in survival time between study groups (p = 0.08). The authors also highlighted 75% death among participants, but the percentages in the HSPC and control groups were not stated. In Tattersall et al. ,106 there were 39 (65%) deaths in the HSPC group and 31 (51.7%) in control group at 12 months. Tattersall et al. 106 found the median survival time in the HSPC group to be 7 months (95% CI 5.2 to 9.8 months), compared with 11.7 months (95% CI 9.8 to 18.8 months) in control group (log rank p = 0.014). The estimated HR was 1.6 (95% CI 1.1 to 2.3; p = 0.015). This estimate changed to 1.5 (95% CI 0.99 to 2.2; p = 0.06) when adjusted for the oncologist’s baseline estimate of likely survival, diagnosis, months since diagnosis and sex.

Pain

We pooled data from four studies that reported adjusted end-point values as our main meta-analysis and found no significant difference between HSPC and usual care (four studies, 525 participants, SMD –0.16, 95% CI –0.33 to 0.01; I² = 0%) (Figure 20). Positive SMDs indicate more pain; negative SMDs indicate less pain (benefit).

FIGURE 20.

Effect of HSPC on pain: adjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

We carried out sensitivity analyses with studies that reported adjusted change values and unadjusted change values. Only Woo et al. 116 presented unadjusted end-point values and they found no difference in mean pain scores on the Brief Pain Inventory (BPI) between the HSPC and usual-care groups (p = 0.22).

A sensitivity analysis using adjusted change values showed a significant improvement in pain with HSPC (two studies, 218 participants, SMD –0.47, 95% CI –0.74 to –0.20, I² = 0%) (Figure 21).

FIGURE 21.

Effect of HSPC on pain: adjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

When we pooled unadjusted change values, we found no significant difference between HSPC and usual care (two studies, 291 participants, SMD –0.93, 95% CI –3.05 to 1.19; I² = 97%) (Figure 22).

FIGURE 22.

Effect of HSPC on pain: unadjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

In the protocol,46 we had initially specified that we would treat pain as a binary outcome. However, this was not possible because most studies presented pain as a continuous outcome. Studies such as Tattersall et al. 106 reported on the percentage of patients with pain, whereas Lowther et al. 97 presented pain data as median values. Kane et al. 156 reported that there was no difference in pain between the intervention and control groups over time, but did not present data. Furthermore, McWhinney et al. 79 stated that there were ‘no clinically or statistically significant differences’ between the intervention and control groups, but did not report their data.

The remaining 30 studies did not report on pain. We combined different scales assessing pain by calculating SMDs. Across the studies in these meta-analyses, we combined different measures for assessing pain [the Pain, Enjoyment of Life and General Activity (PEG) scale, derived from the BPI, in Bekelman et al. ;139 pain item of the EORTC QLQ-C30 in Groenvold et al. 148 and Vanbutsele et al. ;168 pain item of the POS in Higginson et al. ;77 pain severity on the BPI in O’Riordan et al. ,161 Rodin et al. 163 and Woo et al. ;116 and pain item of the ESAS in Ozcelik et al. 95 and Sidebottom et al. 96].

Given that there were fewer than 10 included studies in the main meta-analysis on pain using adjusted end-point values, we did not use funnel plots or carry out tests for funnel plot asymmetry. In addition, we could not carry out subgroup analysis because of a lack of heterogeneity (I² = 0%) in the main meta-analysis with adjusted end-point values.

Patient anxiety

We pooled data from five studies that reported adjusted end-point values as the main meta-analysis and found no significant difference between HSPC and usual care [five studies, 384 participants, mean difference (MD) –0.63, 95% CI –2.22 to 0.96; I² = 76%] (Figure 23). All five studies assessed anxiety using the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS), the Hospital Anxiety and Depression Scale-Anxiety (HADS-A) (seven items; 0–21 scale, 21 = maximum distress). Negative MD indicates benefit (lower levels of patient anxiety) and positive MD reflects harm (higher levels of patient anxiety).

FIGURE 23.

Effect of HSPC on patient anxiety: adjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

We carried out sensitivity analyses with studies that presented unadjusted end-point values and unadjusted change values, and also assessed the impact of using an estimate of 0.02 in adjusting for clustering in the cluster RCT by McCorkle et al. 48 Only Sidebottom et al. 96 (167 participants) reported adjusted change values; they found that anxiety scores improved by a mean of 1.27 points in the HSPC group and by 0.89 points in the control group on the anxiety subscale of the ESAS (using a visual scale line, 0–10, 10 = worst possible) at 3 months (difference 0.38; p = 0.017) after adjusting for age, sex and marital status differences between trial groups. This difference was already evident at 1 month (p = 0.007).

When we removed McCorkle et al. 48 in the sensitivity analysis with adjusted end-point values, we found significant improvement in patient anxiety with HSPC (four studies, 320 participants, MD –1.60, 95% CI –2.56 to –0.65; I² = 17%) (Figure 24).

FIGURE 24.

Effect of HSPC on patient anxiety: adjusted end-point values (excluding McCorkle et al. 48). df, degrees of freedom; IV, inverse variance; random, random-effects model.

A sensitivity analysis with unadjusted end-point values showed no significant difference between HSPC and usual care (four studies, 273 participants, MD –0.90, 95% CI –2.52 to 0.71; I² = 67%) (Figure 25). All the studies measured anxiety using the HADS-A.

FIGURE 25.

Effect of HSPC on patient anxiety: unadjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

When we removed McCorkle et al. ,48 the MD was –1.48 (three studies, 209 participants, 95% CI –3.52 to 0.56; I² = 71%) (Figure 26).

FIGURE 26.

Effect of HSPC on patient anxiety: unadjusted end-point values (excluding McCorkle et al. 48). df, degrees of freedom; IV, inverse variance; random, random-effects model.

Studies that presented unadjusted change values showed an effect in favour of HSPC (four studies, 496 participants, SMD –0.62, 95% CI –1.02 to –0.21; I² = 74%) (Figure 27).

FIGURE 27.

Effect of HSPC on patient anxiety: unadjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Standardised MD was used in pooling the estimates because the four studies that reported unadjusted change values used different scales for measuring anxiety: Bajwah et al. 72 and El-Jawahri et al. 85 used the HADS-A, Bekelman et al. 139 used the Generalised Anxiety Disorder-7 and Ozcelik et al. 95 used the anxiety subscale of the ESAS.

Five studies also assessed patient anxiety, but we could not include them in the meta-analysis for a number of reasons: Kane et al. 156 stated the p-values for the difference between the intervention and control groups only; Temel et al. 35 presented the percentage of patients with anxiety at the primary point of analysis only; Temel et al. 167 did not provide data, but stated that scores did not differ between the intervention and control groups at 12 or 24 weeks; Solari et al. 126 reported no difference between groups for change at 3 or 6 months, but did not present usable data; and Vanbutsele et al. 168 presented ORs at 12, 18 and 24 weeks. This study168 did not find any difference between groups at these different time points.

The remaining 26 studies did not report on patient anxiety. Given that there were fewer than 10 included studies in the main meta-analysis on patient anxiety using adjusted end-point values, we did not use funnel plots or carry out tests for funnel plot asymmetry.

Subgroup analysis on patient anxiety

We carried out the following subgroup analyses on patient anxiety with studies that reported adjusted end-point values.

Caregiver anxiety

The Carson et al. 82 study (312 participants) was the only study that presented adjusted end-point values. Carson et al. 82 assessed caregiver anxiety using the HADS-A (seven items; 0–21 scale, 21 = maximum distress). Carson et al. 82 reported higher mean levels of caregiver anxiety in the HSPC group than in the control group at 3 months on adjusting for baseline and multiple respondents: mean 7.2 (95% CI 6.6 to 7.9) vs. 6.4 (95% CI 5.7 to 7.1) in the HSPC and control groups, respectively; the MD was 0.8 (95% CI –0.1 to 1.8; p = 0.09). Adjustments for three variables (baseline, multiple respondents and study sites) and six variables (baseline, multiple respondents, study sites, race, sex and primary/additional surrogate) also produced similar results, with p-values of 0.11 and 0.12, respectively.

Two studies72,82 with 351 participants reported unadjusted end-point data with a pooled estimate of MD of –0.71 (95% CI –4.27 to 2.85; I² = 77%) (Figure 35). Both studies used the HADS-A in assessing caregiver anxiety. Negative MDs indicate benefit (lower levels of caregiver anxiety) and positive MDs reflect harm (higher levels of caregiver anxiety).

FIGURE 35.

Effect of HSPC on caregiver anxiety: unadjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Four studies recorded this outcome, but did not present analysable data. 75,76,85,156 El-Jawahri et al. 85 and Farquhar et al. 76 did not present the numbers of participants in the intervention and control groups at the primary point of analysis. Farquhar et al. 75 reported that there was little change in carer outcomes, but did not present data, and Kane et al. 156 found differences in favour of HSPC in three of the five cohorts examined, but did not present usable data.

The remaining 37 studies did not report on caregiver anxiety. Given that we had only one study that presented adjusted end-point values, we could not carry out any further analysis.

Patient depression

For the main meta-analysis on patient depression, we pooled data from eight studies (1096 participants) that presented adjusted end-point values. The results showed that HSPC led to improvement in depression, compared with usual care (eight studies, 1096 participants, SMD –0.22, 95% CI –0.34 to –0.10; I² = 0%) (Figure 36). Negative SMDs indicate benefit (lower levels of depression) and positive SMDs reflect higher levels of depression.

FIGURE 36.

Effect of HSPC on patient depression: adjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

We carried out sensitivity analyses with unadjusted end-point values, adjusted change values and unadjusted change values, and also assessed the impact of adjusting for clustering using an ICC of 0.02 in the cluster RCT by McCorkle et al. 48

Five studies (350 participants) presented unadjusted end-point values and found a pooled estimate of SMD of –0.25 (95% CI –0.55 to 0.04; I² = 47%) (Figure 37).

FIGURE 37.

Effect of HSPC on patient depression: unadjusted end-point values. a, Data approximated from graph. df, degrees of freedom; IV, inverse variance; random, random-effects model.

We carried out a sensitivity analysis to assess the impact of using an estimate of 0.02 in adjusting for clustering in McCorkle et al. ,48 and found evidence in favour of HSPC (four studies, 286 participants, SMD –0.34, 95% CI –0.65 to –0.03; I² = 42%) (Figure 38).

FIGURE 38.

Effect of HSPC on patient depression: unadjusted end-point values (excluding McCorkle et al. 48). a, Data approximated from graph. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Two studies48,96 with 231 participants contributed data to the sensitivity analysis using adjusted change values with a pooled estimate of MD –0.32 (95% CI –1.10 to 0.45; I² = 92%) (Figure 39).

FIGURE 39.

Effect of HSPC on patient depression: adjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

The sensitivity analysis using unadjusted change values showed evidence in favour of HSPC (four studies, 488 participants, SMD –0.38, 95% CI –0.58 to –0.18; I² = 12%) (Figure 40).

FIGURE 40.

Effect of HSPC on patient depression: unadjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Three studies also presented binary data and were pooled using ORs. 35,85,116 We found evidence of lower odds of patient depression with HSPC than with usual care (three studies, 338 participants, OR 0.38, 95% CI 0.21 to 0.68; I² = 32%) (Figure 41). The OR of 0.38 translates to a risk ratio of 0.55, implying that the risk of patient depression was 0.55 times lower with HSPC than with usual care.

FIGURE 41.

Effect of HSPC on patient depression as a binary outcome. M–H, Mantel–Haenszel; random, random-effects model.

Four studies assessed patient depression but we excluded them from the main meta-analysis because they did not present analysable data. 126,156,168,170 Kane et al. 156 determined that there was no between-group difference between the intervention and control groups, but did not provide the data. Solari et al. 126 reported that they found no difference between groups at 3 and 6 months, but did not present analysable data; Vanbutsele et al. 168 presented only ORs and their corresponding 95% CIs for the two measures they used in assessing depression [Hospital Anxiety and Depression Scale-Depression (HADS-D) and Patient Health Questionnaire-9 items (PHQ-9)]. There was no difference between the intervention and control groups at 12, 18 and 24 weeks in Vanbutsele et al. 168 Wallen et al. 170 assessed depression, but did not present data on it at baseline and follow up. The remaining 21 studies did not report on patient depression.

Studies included in the meta-analyses used different scales in assessing depression: Beck Depression Inventory, version 2;163 depression subscale of the HADS (HADS-D);72,75,76,78,85,161,165,167 PHQ-9;48,85,89,96,139,167 depression subscale of the ESAS;95 and Center for Epidemiologic Studies Depression Scale (CES-D). 73,116,129

Given that there was no heterogeneity in the main meta-analysis (I² = 0%), we did not carry out any subgroup analyses. There were fewer than 10 studies that reported adjusted end-point values in the main meta-analysis, and we did not use funnel plots or carry out tests for funnel plot asymmetry.

Caregiver depression

As the main meta-analysis on caregiver depression, we pooled data from two studies82,139 that presented adjusted end-point values. We found that HSPC had little or no effect on caregiver depression (two studies, 413 participants, SMD –0.02, 95% CI –0.21 to 0.18; I² = 0%) (Figure 42). Negative SMDs indicate benefit (lower levels of depression) and positive SMDs reflect harm (higher levels of depression).

FIGURE 42.

Effect of HSPC on caregiver depression: adjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

We carried out sensitivity analyses with unadjusted end-point values and found a SMD of –0.29 (three studies, 420 participants, 95% CI –0.70 to 0.12; I² = 63%) (Figure 43).

FIGURE 43.

Effect of HSPC on caregiver depression: unadjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Bajwah et al. 72 (35 caregiver participants) was the only study that presented unadjusted change values on the HADS-D (seven items; 0–21 scale, 21 = maximum distress). It found a 0.3-point mean decrease in caregiver depression scores from baseline at 4 weeks for the HSPC group, whereas, for controls, caregiver depression increased by 1 point. The effect size at 4 weeks was –0.7 (95% CI –1.3 to 0.0). Between the period when the control group received HSPC (4 weeks) and 8 weeks, mean depression improved in the control group from 9.6 (SD 4.9) to 7.2 (SD 3.9) points.

Four studies reported on caregiver depression, but did not present usable data. 75,76,85,156 In the El-Jawahri et al. 85 study, the numbers of participants in the intervention and control groups at the primary point of analysis were not stated. Farquhar et al. 75,76 and Kane et al. 156 did not present their data. The remaining 34 studies did not report on caregiver depression.

Studies included in the meta-analyses used different scales in assessing caregiver depression: Bajwah et al. 72 and Carson et al. 82 used the depression subscale of the HADS (HADS-D), Bakitas et al. 73 used the CES-D and Bekelman et al. 139 used the Patient Health Questionnaire-8 items.

We could not carry out a subgroup analysis because of the lack of heterogeneity in the main meta-analysis (I² = 0%). Given that there were fewer than 10 included studies in the meta-analysis on caregiver depression, we did not use funnel plots or carry out tests for funnel plot asymmetry.

Patient breathlessness

We combined data from five studies75,76,148,161,168 (616 participants) reporting adjusted end-point values for our main meta-analysis on breathlessness, with a pooled estimate of SMD of –0.04 (95% CI –0.19 to 0.12; I² = 0%) (Figure 44). Negative SMDs indicate benefit (reduced breathlessness) and positive SMDs reflect harm (worsened breathlessness).

FIGURE 44.

Effect of HSPC on patient breathlessness: adjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

The five studies used different instruments and reported on different breathlessness domains. For instance, both Farquhar et al. 75,76 studies assessed distress due to breathlessness and breathlessness mastery using a Numeric Rating Scale (NRS) and the mastery domain of the CRQ, respectively; Groenvold et al. 148 and Vanbutsele et al. 168 assessed breathlessness intensity using the dyspnoea item of the EORTC QLQ-C30; and O’Riordan et al. 161 assessed breathlessness intensity using the Borg scale. For both Farquhar et al. 75,76 studies, we used only data for distress due to breathlessness assessed with the NRS in the meta-analysis because it was the primary outcome. We did not differentiate between different breathlessness domains in the meta-analysis because of small numbers.

We carried out sensitivity analyses with unadjusted end-point values and unadjusted change values.

A sensitivity analysis carried out with the two studies72,78 (128 participants) presenting unadjusted end-point values showed a pooled estimate in favour of HSPC (SMD –0.35, 95% CI –0.70 to –0.00; I² = 0%) (Figure 45).

FIGURE 45.

Effect of HSPC on patient breathlessness: unadjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

A sensitivity analysis with the two studies95,139 (292 participants) that reported unadjusted change values showed a pooled estimate of SMD of –0.47 (95% CI –1.55 to 0.61; I² = 90%) (Figure 46).

FIGURE 46.

Effect of HSPC on patient breathlessness: unadjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Only Sidebottom et al. 96 presented adjusted change values. They assessed breathlessness using the dyspnoea item of the ESAS (using a visual scale line, 0–10, 10 = worst possible), and found that breathlessness scores improved by a mean of 2.8 points in the HSPC group and by 1.7 points in the control group at 3 months (difference 1.08 points; p < 0.001) after adjusting for age, sex and marital status differences between trial groups. This difference was evident at 1 month, with a MD of 1.10 points (p < 0.001).

A study by Tattersall et al. 106 also recorded this outcome, but did not present analysable data. The remaining 31 studies did not report on breathlessness.

Studies included in the meta-analyses used different scales in assessing breathlessness: Dyspnoea-12 questionnaire;72 Memorial Symptom Assessment Scale;139 NRS for distress due to breathlessness;75,76 dyspnoea item of the EORTC QLQ-C30;148,168 breathlessness mastery domain of the CRQ (CRQ mastery);78 Borg scale;161 and dyspnoea item of the ESAS. 95,96

Owing to lack of heterogeneity (I² = 0%) in the main meta-analysis, we could not carry out a subgroup analysis. Given that there were fewer than 10 included studies in the main meta-analysis on breathlessness using adjusted end-point values, we did not use funnel plots or carry out tests for funnel plot asymmetry.

Adverse events in patients and caregivers

Eight studies, with 1252 participants, reported on adverse events72,78,97,106,126,139,148,163 (see Appendix 10).

Two of these studies involved caregivers. 72,78 Six studies (976 participants) reported no harmful effect. 72,78,97,139,148,163 One study106 (120 participants) found that more participants in the HSPC group had poorer appetite (p = 0.04) than in the control group. Solari et al. 126 (156 participants) reported 15 serious adverse events in 13 patients in the HSPC group, and seven adverse events in seven participants in the control group (p = 0.78). Serious adverse events reported included aspiration pneumonia, generalised anxiety, breathing difficulty, urine retention/infection, anarthria, contact dermatitis, dysphagia, vomiting, bladder catheter malfunctioning, fever, arrhythmia, necrotising fasciitis, traumatic wound, macrohaematuria, constipation, abdominalgia and bronchitis. Three participants in the HSPC group died, but this was considered to be unrelated to the intervention.

Caregiver burden

Two studies with 170 participants presented adjusted end-point values: Dionne-Odom et al. 136 (linked to Bakitas et al. 73) and Bekelman et al. 139 However, we could not pool them together in a meta-analysis because of how they presented their data. Dionne-Odom et al. 136 assessed caregiver burden using the Montgomery–Borgatta Caregiver Burden (MBCB) scale and presented results for three different subscales of the MBCB scale, namely the objective burden scale (range 6–30; 30 indicates worst level of interference with the caregiver’s private, social and recreational time and normal daily routine), stress burden scale (range 4–20; 20 indicates worst level of strained emotional demands related to caregiving) and the demand scale (range 4–20; > 15 indicates worst level of caregiver strain by his or her caregiving demands). Bekelman et al. 139 assessed caregiver burden using the Zarit Burden Interview (ZBI) (range 0–88; 88 indicates greatest burden).

On the objective burden scale of the MBCB scale, the mean caregiver burden score for the HSPC group was 0.3 points higher (range 6–30; 30 indicates worst) than that of the control group, with adjustment for patient death (p = 0.64). On the stress burden scale of the MBCB scale, the mean caregiver burden score for the HSPC group was 0.5 points lower (range 4–20; 20 indicates worst) than that for the control group, with adjustment for patient death (p = 0.29). There was no difference between the groups in the mean caregiver burden score, with adjustment for patient death, on the demand scale of the MBCB scale (p = 0.97). Bekelman et al. 139 reported a mean caregiver burden of 12.9 [standard error (SE) 1.3] in the HSPC group and 14.8 (SE 1.4) in control group at 12 months (p = 0.30).

Two studies (108 participants) reported unadjusted end-point data, but we could not pool them in a meta-analysis [Bajwah et al. 72 and Dionne-Odom et al. 136 (linked Bakitas et al. 73)]. Dionne-Odom et al. 136 reported the following results: on the objective burden scale of the MBCB scale, the mean caregiver burden score for the HSPC group was 0.3 points higher (range 6–30; 30 indicates worst) than that of the control group (p = 0.62). On the stress burden scale of the MBCB scale, the mean caregiver burden score for the HSPC group was 0.6 points lower (range 4–20; 20 indicates worst) than that of the control group. There was no difference between the HSPC and control groups in the mean caregiver burden score on the demand scale of the MBCB scale (p = 0.99). Bajwah et al. 72 assessed caregiver burden using the ZBI (range 0–88; 88 indicates highest burden), and reported a mean caregiver burden of 22.3 (SD 15.3) in the fast-track group and of 31.7 (SD 17.3) in the control group at 4 weeks. After the control group was offered HSPC between 4 and 8 weeks, the mean caregiver burden reduced to 25.4 (SD 13.4).

Three studies72,77,126 reported adjusted change values and found evidence in favour of HSPC (128 participants, MD –3.88, 95% CI –5.95 to –1.80; I² = 0%) (Figure 47). All three studies assessed caregiver burden using the ZBI.

FIGURE 47.

Effect of HSPC on caregiver burden: adjusted change values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

Bajwah et al. 72 (39 participants) was the only study that presented unadjusted change values. It found a 0.1 mean increase in caregiver burden score from baseline to 4 weeks for 16 intervention caregivers, whereas, for 23 caregivers in the control group, caregiver burden decreased by 0.1 points. The effect size at 4 weeks was –0.6 (95% CI –1.2 to 0.1).

Bakitas et al. 129 reported on caregiver burden, but did not present usable data for the meta-analysis. The remaining 36 studies did not report on caregiver burden. We did not carry out any further analysis on caregiver burden because of limited number of studies.

Caregiver grief

Only Dionne-Odom et al. 137 (linked to Bakitas et al. 73), with 44 participants, provided usable data for caregiver grief. Dionne-Odom et al. 137 assessed caregiver grief using the Prigerson Inventory of Complicated Grief-Short Form (PG-13) and reported a mean caregiver grief score in the HSPC group that was 2.2 points lower (range 11–55; 55 indicates highest grief) than that of the control group (p = 0.21). There was no evidence of a difference on adjusting for religious preference (p = 0.40), baseline depression levels (p = 0.51) or patient hospice use (p = 0.51).

Caregiver quality of life

Only Dionne-Odom et al. 136 (linked to Bakitas et al. 73) reported adjusted end-point data on caregiver quality of life, and there was no evidence of benefit of HSPC over usual care. Dionne-Odom et al. 136 assessed caregiver quality of life using the Caregiver Quality of Life Index (CQOL) (range 0–140; 140 indicates worse quality of life), and found a mean CQOL score in the HSPC group that was 2 points better than that of the control group at 3 months with adjustment for patient death (p = 0.39). In decedents’ caregivers, a terminal decline analysis indicated a MD of –4.9 points between the HSPC and control groups (p = 0.07).

We carried out a sensitivity analysis with unadjusted end-point values. A sensitivity analysis in the two studies (105 participants) that reported unadjusted end-point values showed a pooled effect in favour of HSPC (MD 6.11, 95% CI 0.42 to 11.81; I² = 0%) (Figure 48). Positive MD indicates better caregiver quality of life and negative MD reflects worse caregiver quality of life. The two studies assessed caregiver quality of life using the CQOL (range 0–140; 140 indicates worse quality of life).

FIGURE 48.

Effect of HSPC on caregiver quality of life: unadjusted end-point values. df, degrees of freedom; IV, inverse variance; random, random-effects model.

In addition, Bajwah et al. 72 also presented unadjusted change values and assessed caregiver quality of life using the CQOL. Bajwah et al. 72 found a 2.5-point mean improvement (range 0–140; 140 indicates worse quality of life) in caregiver quality of life from baseline at 4 weeks for the HSPC group, while, for controls, caregiver quality of life improved by 0.7 points. The effect size at 4 weeks was –0.4 (95% CI –1.1 to 0.2). At 8 weeks, the mean score was 58.3 points (SD 15.6 points) for the HSPC group and 60.2 points (SD 23.9 points) for the control group. The remaining 39 studies did not report on caregiver quality of life.

We could not perform any further analysis because of the limited number of studies.

Impact of hospital-based specialist palliative care on resource use

We could not carry out a meta-analysis on resource use and costs as a result of the differences in the measurement and reporting, such as type of analysis, tools used, assessment time points or time horizon and statistics reported. Consequently, we provided a narrative synthesis on the economic studies.

Thirty-one studies compared resource use and/or costs between the intervention and control groups. Three studies81,129,156 collected information on resource use and/or costs through chart review. The Client Service Receipt Inventory (CSRI), or a modified form of it, was used in four studies75–78 to collect resource use data. Medical/health records were used by eight studies,35,70,89,96,106,161,165,168 and four studies73,123,139,163 used a combination of methods. Bekelman et al. 139 collected data from medical records and supplemented these with patient or family self-report. Bakitas et al. 73 used patient self-report for hospital and ICU days and ED visits, whereas decedents’ data for the period between the last patient-reported assessment and death, and chemotherapy use in previous 14 days, were obtained from medical records. Janssens et al. 123 collected data from medical records, as well as from contact with patients and their GPs. Rodin et al. 163 collected data from patients and their medical charts. Ozcelik et al. 95 used a patient expenditure record form to capture resources and their costs, whereas Brumley et al. 142 obtained resource use for each patient retrospectively from the non-profit health maintenance organisation’s mainframe database. Gade et al. 88 used standard data extract protocols to extract information from the managed care organization’s database. The methods used in collecting resource use information were unclear in nine RCTs. 82,84,85,101,116,118,148,160,167

We considered resource use in the following areas: institutional care services use, outpatient clinic services use, community care services use, unpaid caregiver’s care, and medications and other resources.

Costs and cost-effectiveness of hospital-based specialist palliative care

Thirteen economic studies (2103 participants) reported on cost. Resources included were ED or A&E visits, inpatient and outpatient hospital care, home and community care, care in nursing homes (or skilled nursing homes), inpatient stay and day care in hospice, hospice care at home, informal care, drugs and equipment. Four studies75–77,160 reported the results of cost-effectiveness analyses using outcome measures that were relevant to their research questions (palliative outcome, carer’s burden, QALYs) and hospital costs or total costs. Results of cost-effectiveness analyses were reported by ICERs and/or costs per QALY (point estimates or cost-effectiveness planes).

Two studies found evidence of reduced cost with HSPC. 88,142 When compared with usual care, Mendoza-Galindo et al. 101 reported a reduction in the cost of hospitalisation days in the HSPC group. However, no difference was found between groups in the cost of emergency room visits. In Brännström et al. ,118 this was unclear, as no p-value was presented for the difference in cost between HSPC and usual care. We identified four full economic studies. 75–77,160 The evidence on the cost-effectiveness of the HSPC, compared with usual care, was equivocal.

The first relevant study that we identified was carried out by Kane et al. 156 Kane et al. 156 was a US study that provided services across multiple settings. It compared the cost of hospice care provided across multiple settings with that of conventional care among cancer patients. Participants in the hospice care group had lower total costs when compared with those receiving conventional care. However, this was not statistically significant. The estimated mean expenditure per patient was US$15,263 (£29,058 at 2018 conversion rates) in the HSPC group and US$15,493 (£29,496 at 2018 conversion rates). Resource use was measured in hospice stays, hospital stays, surgical procedures, chemotherapy and radiotherapy, and costs were calculated using different assumptions. However, difference in survival (days since enrolment in the study), as well as other factors (e.g. age, severity of diseases) that might be associated with costs, was not adjusted for.

Brumley et al. 142 compared resource use and costs between the HSPC and usual-care group and the usual-care only group among terminally ill patients with cancer and terminally ill patients with non-cancer diagnoses (i.e. mixed diagnoses) in the USA. A wider range of resource use was included from the health insurance database: hospital days, number of ED visits, physician office visits, skilled nursing facility days, home health and palliative visits, palliative physician home visits and days in hospice care. Service use was significantly lower in the intervention group than in the usual-care group, even after adjusting for age, survival and severity of illness, measured using the Palliative Performance Scale. Hospital stay decreased by 4.36 days and the number of ED visits decreased by 0.35. Owing to the difference in the survival (days on service), mean costs per patient were adjusted using regression analysis, controlling for survival, age, severity of illness and primary disease. The mean cost per patient was lower in the intervention group [AU$12,670 (SD AU$12,523), which converts to £8383 (SD £8285) at 2018 rates] than in the usual-care group [AU$20,222 (SD AU$30,026), which converts to £13,379 (SD £19,866) at 2018 rates]. The average daily cost per patient was also significantly lower in intervention group (AU$95.30, which converts to £63.05 at 2018 rates) than in the usual-care group (AU$212.80, which converts to £140.76 at 2018 rates) (p = 0.02).

Gade et al. 88 used the health insurance database to extract resource use and unit cost of services of hospitalised patients with life-limiting illnesses (mixed cancer and non-cancer diagnoses), who were randomly assigned to a HSPC intervention or usual care. Resources included were ED visits, clinic and hospital outpatient visits, home health visits, hospital admission, skilled nursing facility admissions and prescriptions filled. The cost of the palliative care team was calculated as the intervention cost. Patients in the HSPC group stayed longer in hospice after the index hospitalisation (24 days) than usual-care patients (12 days) (p = 0.08), had significantly shorter ICU stays on re-admission (12 times vs. 21 times, p = 0.04) and had significantly lower total health-care costs [US$14,486 (£15,013 at 2018 rates) vs. US$21,252 (£22,025 at 2018 rates); p = 0.001]. Gade et al. 88 was a US study that involved an inpatient consult model of HSPC.

Temel et al. 35 compared the effectiveness of the early palliative care integrated with standard oncologic care (HSPC) with that of standard oncologic care only among patients with newly diagnosed metastatic non-small cell lung cancer. It was a US study that involved an outpatient model of HSPC. Data on health resource use and end-of-life care were collected from patients’ medical records: anticancer therapy, medication prescriptions, referral to hospice, hospital admissions and ED visits. Patients in the standard-care group received more aggressive end-of-life care [54% (30/56) vs. 33% (16/49); p = 0.05], and had longer stays in hospice care (median 11 days vs. 4 days; p = 0.09) than those in the intervention group. Patients in the HSPC group had less aggressive care, and quality of life and survival improved more in this group than in the control group. However, this was not conclusive as the sample size of the study did not allow the statistical power to test the differences in service use. Detailed analyses of costs and cost-effectiveness were conducted and reported later, although lacking in statistical power to detect the difference in Greer et al. 108 Comparisons of costs per day alive and costs for the previous 30 days were made between the HSPC and usual-care groups and the cost-effectiveness per life-year saved was calculated. The total costs per day were, on average, lower in the HSPC group than in the control group [MD US$117 (SE US$74), which is £103 (SE £65) at 2018 rates; p = 0.13], and total costs for the last 30 days were also reduced [MD US$2527 (SE US$3311), which is £2230 (SE £2922) at 2018 rates; p = 0.44]. The cost-effectiveness ratio was US$41,938 per life-year saved. More use of hospice care [MD –US$1053 (SE US$538), which is –£929 (SE £475) at 2018 rates; p = 0.07] and less use of chemotherapy [MD US$757 (SE US$365), which is £668 (SE £322) at 2018 conversion rates; p = 0.03] for the last 30 days implied that the cost savings might come from shifting care from inpatient to outpatient settings.

Higginson et al. 78 assessed the effectiveness of early introduction of palliative care among patients with chronic breathlessness in the UK. The intervention (HSPC) was provided across multiple settings and included patients with mixed cancer and non-cancer diagnoses. Patients were randomly allocated to the HSPC group or to the usual-care group. Resource use data, such as health, voluntary and social care received, were collected using the CSRI over the previous 3 months at baseline and since the last interview at 6 weeks’ follow-up. Limited results on resource use and costs were reported: hospital inpatient stays [mean 4.5 (SD 6.8) in the HSPC group and 4.6 (SD 7.6) in the control group] and costs of formal care use [mean £1422 (95% CI £897 to £2101), which is £1611 (95% CI £1016 to £2380) in 2018 prices, in the HSPC group, and mean £1408 (95% CI £899 to £2023), which is £1595 (95% CI £1018 to £2292) in 2018 prices, in the control group]. There was no difference between the two groups.

Brännström et al. 118 compared service use between patients randomised to the Palliative advanced home caRE and heart FailurE caRe (PREFER) intervention and patients randomised to usual care among patients with severe chronic heart failure. The advanced home care unit was based in a county hospital. The PREFER intervention was an outreach model of HSPC. Use of the following resources was assessed: inpatient days, hospital admissions, physician and nurse visits, telephone calls and drug prescriptions. The intervention group had significantly fewer hospitalisations than the control group (0.42 ± 0.60 vs. 1.47 ± 1.81, respectively; p = 0.009) and the length of stay in hospital was also significantly lower among patients in the intervention group than in the control group [mean 2.9 (SD 8.3) vs. mean 8.5 (SD 12.4), respectively; p = 0.011]. The number of total days or total contacts per trial group were compared between the intervention and control groups, and an additional cost analysis was reported in Sahlen et al. 121 QALY gain was 0.25 years between baseline and end of the intervention across the palliative advance home care group and usual-care group (p = 0.025). Over 6 months, the total cost was Swedish krona (SEK) 1.4M (€140,000, converts to £126,132 in 2018 prices) in the HSPC group and SEK2.0M (€205,000, converts to £180,188 in 2018 prices) in the control group, and the difference, SEK600,000 (€61,000), was the savings achieved by providing the palliative advance home care in addition to the usual heart failure care.

Ozcelik et al. 95 compared duration of hospitalisation and direct cost between the HSPC and usual-care groups. It was an inpatient consult model of HSPC. A patient cost record form was used to document cost and it consisted of all expenses incurred while in hospital. Direct expenses assessed were consultations, professional care, medicines used from the start of a patient’s stay in hospital, medical equipment, laboratory and diagnosis tests, and hospital stay expenses (including those of companions). After discharge from hospital, costs were recorded on the form by obtaining the expenses list from the clinic secretary. In the HSPC group, the mean direct cost was US$68.869 (SD US$48.522) [converts to £60.154 (SD £42.382) in 2018 prices]; in the control group, it was US$81.076 (SD US$72.70) [converts to £70.816 (£63.500) in 2018 prices] (p = 0.76). There was no difference in duration of hospitalisation (p = 0.07), with a mean length of stay in hospital of 9.4 days (SD 6.27 days) in the intervention group and 13.9 days (SD 11.5 days) in the control group.

Among included studies, Higginson et al. 77 was the first study to use a robust cost-effectiveness analysis method. The cost-effectiveness analysis was carried out alongside a feasibility trial of a new palliative care service among patients with multiple sclerosis, randomised to either fast-track of the new palliative care intervention or usual care. Costs of health, social and voluntary services were measured; informal care provided by family or friends was also included in the analysis from a broad perspective. As the usual unit costs were applied for the formal services, ‘shadow price’ was used for informal care. The cost-effectiveness analysis used the differences in costs and outcomes (POS-8 and ZBI) between baseline and follow-up at 12 weeks. The total costs for 12 weeks, measured at follow-up, were lower in the fast-track intervention group than in the usual-care group by £1789 (95% CI –£5224 to £1902), which converts to £2424 (95% CI –£7077 to £2577) in 2018 prices. After excluding inpatient care and informal care, mean service costs for 12 weeks were £1195 lower for the intervention group (95% CI –£2916 to £178), which converts to £1619 (95% CI –£3950 to £241) in 2018 prices. Cost-effectiveness planes showed that 33.8% of the replications for POS-8 indicated that patients in the intervention group had lower costs and better outcomes than patients in the control group, and 54.9% had lower costs but worse outcomes. For ZBI, 47.3% of the replications showed lower costs and better outcomes, whereas 48% indicated higher costs and better outcomes.

The McCaffrey et al. 160 study was an Australian study that estimated incremental net monetary benefit (INMB) and cost-effectiveness acceptability curves for 1 extra day at home among patients with mixed cancer and non-cancer diagnoses with complex or unstable symptom management and a high level of care needs. McCaffrey et al. 160 provided services across multiple settings. The data on resource use that were collected included days at home, specialist palliative care service, acute hospital and palliative care unit inpatient days, and outpatient visits. Intervention costs were calculated based on staff administration, travel and direct patient contact time, overheads, and consumables. Analysis was conducted from a health-care provider perspective and bootstrapping was used to calculate the CIs around the INMB and cost-effectiveness acceptability curves. Total costs were AU$6452 (95% CI AU$4469 to AU$8586) [converts to £5750 (95% CI £3983 to £7652) at 2018 rates] in the HSPC group and AU$5425 (95% CI AU$2404 to AU$8531) [converts to £4835 (95% CI £2142 to £7602) at 2018 rates] in the control group. The incremental cost between the two groups was AU$1027 (95% CI –AU$2612 to AU$4738) [converts to £915.22 (95% CI –£2327.71 to £4222.32]. When the INMB of 1 more day at home was compared with varying threshold values, the intervention was preferred to usual care at > AU$1068. Sensitivity analyses with different inclusion ranges of costs (using hospital inpatient costs only and excluding high cost outliers) indicated that home-based palliative care was preferred at > AU$2547 (converts to £2270 at 2018 rates) and AU$846 (converts to £754 at 2018 rates). It was concluded that the intervention had a potential to be cost-effective, especially in trials with longer follow-up. The meaning of the threshold value for 1 extra day at home remains for future research.

Both Farquhar et al. 75,76 studies reported the cost-effectiveness of the Breathlessness Intervention Service (BIS), a multidisciplinary complex intervention underpinned by a palliative care approach for patients with advanced cancer and advanced non-malignant disease separately. The BIS was a model of HSPC in which service provision traversed multiple settings in the UK.

In Farquhar et al. ,75 data from patients with advanced cancer were analysed from a societal perspective by including costs of informal care. Total health/social costs, including informal care for 8 weeks prior to the baseline assessment, were £6137 (SD £6099) [or £6952 (SD £6909) in 2018 prices] in the HSPC group and £5461 (SD £6099) [or £6186 (SD £6909) in 2018 prices] in the usual-care group. Costs between baseline and follow-up at 2 weeks were £794 (SD £866) [or £899 (SD £981) in 2018 prices] for HSPC and £1121 (SD £1635) [or £1270 (SD £1852) in 2018 prices] for usual care.

The intervention cost for HSPC was £119 (SD £62), or £135 (SD £70) in 2018 prices. Total costs were £354 lower for HSPC (95% CI –£1020 to £246) [or £401 (95% CI –£1155 to £279) in 2018 prices] and incremental QALY gain was 0.0002 years (95% CI −0.001 to 0.002), after controlling for baseline. The chance of HSPC having lower total costs and providing better outcomes in terms of reduced distress due to breathlessness was 80.9% according to cost-effectiveness planes, and the chance of HSPC having higher costs and better outcomes was 16.4%. The chance of HSPC having lower total costs and greater QALY gains was 50.9%, and the chance of HSPC having higher costs and greater QALY gains was 11%.

An NHS perspective was taken in the analysis of data from patients with advanced non-malignant disease. In Farquhar et al. ,76 total health/social costs for 8 weeks prior to the baseline assessment were £1952 (SD £3290) [or £2211 (SD £3727) in 2018 prices] for the HSPC group and £3630 (SD £5588) [or £4112 (SD £6330) in 2018 prices] for the usual-care group. Costs between baseline and follow-up at 4 weeks were £1371 (SD £2948) [or converts to £1553 (SD £3339) in 2018 prices] for HSPC and £659 (SD £1253) [or £746 (SD £1419) in 2018 prices] for usual care.

The intervention cost for HSPC was £156 (SD £80), or £177 (SD £91) in 2018 prices. On adjusting for baseline, the total cost was £799 higher for HSPC (95% CI –£237 to £1904) [or £905 (95% CI –£268 to £2157) in 2018 prices] than for usual care, and the HSPC group gained 0.003 extra QALYs (95% CI –0.001 to 0.007). The cost per QALY for HSPC was £266,333 (£301,692 in 2018 prices). The chance of the BIS having lower total costs and greater QALYs was 7% according to cost-effectiveness planes. There was an 86.5% likelihood of HSPC having higher total costs and greater QALY gains. The HSPC intervention appeared to be more cost-effective among patients with cancer, but not among patients with non-malignant disease.

Mendoza-Galindo et al. 101 compared resource use and costs between the early palliative care group and usual-care group in patients with a cancer diagnosis in Mexico. The study involved an outpatient model of HSPC and assessed number/days of hospitalisation and emergency room, visits as well as their costs. The number of emergency room visits in the early palliative care group was 39, whereas, in the control group, it was 50 (p = 0.074). There was also no difference in the number of hospitalisations (48% vs. 51%) or in days of hospitalisation (78 vs. 90 days; p = 0.808) between the groups. The median cost associated with emergency room visits was lower in the early palliative care group (US$21.99, which converts to £16.97 at 2018 rates) than in the usual-care group (US$46.35, which converts to £35.76 at 2018 rates) (p = 0.081). The authors further reported a lower median cost of hospitalisation days for the early palliative care group (US$167.57, which converts to £129.30 at 2018 rates) than for the usual-care group (US$295.05, which converts to £227.66 at 2018 rates) (p = 0.015).

Ma et al. 70 assessed resource use and operating costs between an early palliative care intervention and usual care for patients in the ICU setting. It was an inpatient consult model of HSPC. Resources used were extracted from patients’ electronic medical records, and included mechanical ventilation, vasopressors, haemodialysis, tracheostomy, cardiopulmonary resuscitation, ED visit, hospital re-admission, duration of hospital stay and ICU duration. Early palliative care patients had fewer ventilator days (median 4 vs. 6; p = 0.042), fewer tracheostomies performed (1% vs. 7.8%; p = 0.035), fewer post-discharge ED visits (1.3% vs. 12.5%; p = 0.007), fewer days on mechanical ventilation [median 4 (IQR 3–7) vs. 6 (IQR 3–13); p = 0.042] and fewer hospital re-admissions (17.3% vs. 33.3%; p = 0.0024) than usual care patients. There was no difference between the intervention and control groups in ICU length of stay (median 5 days vs. 5.5 days, respectively), numbers on mechanical ventilation (53.6% vs. 56.9%, respectively; p = 0.64), numbers on vasopressors (48.5% vs. 50%, respectively; p = 0.83), days on vasopressors (median 3 vs. 3, respectively; p = 0.91), numbers on haemodialysis (15.5% vs. 23.5%, respectively; p = 0.15), numbers receiving cardiopulmonary resuscitation (5.2% vs. 6.9%, respectively; p = 0.61) or hospital length of stay (median 10 days vs. 11 days, respectively). An analysis of operating costs was conducted, although it lacked statistical power to detect the difference. Intervention patients had lower medical ICU costs [US$9860 (converts to £7608.08 at 2018 rates) vs. US$15,660 (converts to £12083.42 at 2018 rates); p = 0.004] and lower pharmacy costs [US$3430 (converts to £2646.62 at 2018 rates) vs. US$5850 (converts to £4513.92 at 2018 rates); p = 0.016] per patient than the control group. However, the total operating cost per patient was not different between the intervention and control groups [US$37,310 (converts to £28,788.78 at 2018 rates) vs. US$45,790 (converts to £35,332.04 at 2018 rates), respectively; p = 0.14]. An estimated US$880 (£679.02 in 2018 prices) of the intervention group’s per-patient total operating cost was due to the added cost of the palliative care consultation.

Synthesis of nested or embedded qualitative studies that explored stakeholders’ views and experiences of hospital-based specialist palliative care

Ten studies, with a total of 322 participants [245 patients, 20 carers, 9 HSPC team members, 29 physicians (including oncologists), 14 oncology nurse practitioners, 1 consultant in interstitial lung disease, 1 clinical nurse specialist in interstitial lung disease, 1 community matron, 1 community palliative care nurse and 1 GP] also had qualitative components that were used to explore stakeholders’ views and experiences of HSPC [Bajwah et al. ,72 Farquhar et al. ,75,76 Hopp et al. ,93 Veron et al. 187 (linked to Janssens et al. 123), Lowther et al. 100 (linked to Lowther et al. 97), Maloney et al. 132 (linked to Bakitas et al. 129), Giovannetti et al. 127 (linked to Solari et al. 126), Talabani et al. 122 (linked to Brännström et al. 118) and Wallen et al. 170] (see Report Supplementary Material 1, table 12). The number of patients interviewed by Wallen et al. 170 was unclear. However, a study171 reporting the same data by the authors stated that 34 patients were involved in the qualitative analysis.

Four studies had HSPC models that involved service provision across multiple settings [Farquhar et al. ,75,76 Maloney et al. 132 (linked to Bakitas et al. 129) and Wallen et al. 170], and another four used hospital outreach services [Bajwah et al. ,72 Talabani et al. 122 (linked to Brännström et al. 118), Veron et al. 187 (linked to Janssens et al. 123) and Giovannetti et al. 127 (linked to Solari et al. 126)]. Only Lowther et al. 100 (linked to Lowther et al. 97) used an outpatient HSPC model, whereas Hopp et al. 93 used an inpatient consult model.

Four studies used framework analysis72,75,76,127 and three studies used thematic analyses130,132,171 as their analyses methods. Three studies described the use of content analysis/thematic content analysis;100,122,187 this was unclear in Hopp et al. 93 Semistructured interviews were carried out in all the studies except Slota et al. 171 and Hopp et al. 93 The method of data collection in Slota et al. 171 was open-ended, qualitative questions on a questionnaire, whereas Hopp et al. 93 involved qualitatively reviewing clinical records.

Data from the studies were synthesised into two themes: valued components and challenges to HSPC provision.

Patient health-related quality of life

The results of the 10 studies35,48,73,85,106,129,161,163,167,168 that reported adjusted end-point values, including a total of 1344 participants, showed that HSPC may improve patient HRQoL, on average, by 0.26 SMD over usual care (95% CI 0.15 to 0.37; I² = 3%; low-quality evidence). Positive SMDs indicate better patient HRQoL, whereas negative SMDs indicate lower patient HRQoL. Owing to the low quality of the evidence, we are uncertain about the effect of HSPC on patient HRQoL; the true effect may be substantially different. The result obtained from the adjusted end-point values was supported from sensitivity analyses using unadjusted end-point values (SMD 0.41; nine studies with 1201 participants) and unadjusted change values (SMD 0.67; nine studies with 1278 participants). Sensitivity analyses evaluating the use of an estimate of 0.02 in adjusting for clustering in the cluster RCT (McCorkle et al. 48) with adjusted end-point data (SMD 0.29; nine studies with 1280 participants) and unadjusted end-point data (SMD 0.46; eight studies with 1137 participants) were also in favour of HSPC.

Patient symptom burden

Data from the six studies,35,73,85,106,129,163 including a total of 761 participants, in the main analysis suggested that HSPC may reduce patient symptom burden, on average, by –0.26 SMD over usual care (95% CI –0.41 to –0.12; I² = 0%; very low-quality evidence). Negative SMDs indicate benefit (lower level of symptom burden) and positive SMDs reflect a higher level of symptom burden. Again, we are uncertain about the effect of HSPC on symptom burden, and the true effect may be substantially different. Sensitivity analyses using unadjusted end-point values, adjusted change values and unadjusted change values, as well as sensitivity analyses evaluating the use of an estimate of 0.02 in adjusting for clustering in the cluster RCT by McCorkle et al. ,48 showed little to no difference between HSPC and usual care.

Patient satisfaction with care

Data from two studies,88,163 including a total of 337 participants, in the main analysis suggest that HSPC may improve patient satisfaction with care, on average, by 0.36 SMD over usual care (95% CI 0.14 to 0.57; I² = 0%; low-quality evidence). Positive SMDs indicate a higher level of patient satisfaction whereas negative SMDs indicate a lower level of patient satisfaction. We are uncertain about the effect of HSPC on patient satisfaction with care; the true effect is likely to be substantially different.

Caregiver satisfaction with care

Carson et al. 82 was the only study that presented adjusted end-point values. Family satisfaction with care was assessed using the FS-ICU survey (range 0–100, 100 = best unpaid caregiver satisfaction). It found no between-group difference between the HSPC and usual-care groups. The mean satisfaction in the HSPC group was 81.1 (95% CI 78.3 to 83.9), whereas that in the usual-care group was 84.3 (95% CI 81.3 to 87.3), with a difference of –3.1 (95% CI –7.3 to 1.0) between groups (p = 0.13). Due to the very low quality of the evidence, we are uncertain about the effect of HSPC on family satisfaction with care; the true effect is likely to be substantially different.

Achieving patient preferred place of death (measured by number of patients with home death)

The number of home deaths was used as a proxy measure for achieving preferred place of death. Results from the seven studies,35,72,73,106,129,142,160 including a total of 861 participants, showed that HSPC may enable people to die in their preferred place, which is reflected in 1.63-times higher odds of home death (OR 1.63, 95% CI 1.23 to 2.16; I² = 0%; low-quality evidence). The OR of 1.63 translates to a risk ratio of 1.22 (95% CI 1.08 to 1.39). This means that those who had HSPC had a 22% increase in the relative risk of home deaths. Given the low quality of the evidence, the effects of HSPC on achieving preferred place of death are uncertain, and the true effect may be substantially different.

Achieving patient preferred place of care

One study, by Bajwah et al. ,72 with 47 participants reported on this outcome. Results at the end of the study showed that, in the intervention group that received HSPC immediately after randomisation, all eight patients (100%) who died achieved their preferred place of care, compared with 11 patients (84%) in the control group, who received HSPC after 4 weeks. Owing to the very low quality of the evidence, we are uncertain about the effects of HSPC on this outcome; the true effect is likely to be substantially different.

Mortality/survival

Results from the 36 studies35,48,70,72–79,81,82,84,85,88,89,93,96,97,106,116,118,123,126,129,139,142,147,148,156,160,161,165,167,168 (7103 participants) that reported on this outcome were of very low quality, and suggested that the effect of HSPC on mortality is inconsistent. Consequently, we are uncertain about the result.

Pain (patients)

Data from four studies148,161,163,168 (525 participants) suggest that there is little to no effect of HSPC on pain (SMD –0.16, 95% CI –0.33 to 0.01; I² = 0%; very low-quality evidence). Results from the sensitivity analysis using unadjusted change values also showed no difference (two studies with 291 participants). However, a sensitivity analysis using adjusted change values was in favour of HSPC (SMD –0.47; two studies with 218 participants). Given the very low quality of the evidence, we are uncertain about the effect of HSPC on pain; the true effect may be substantially different.

Patient anxiety

The main analysis on patient anxiety suggests that the effect of HSPC on patient anxiety is inconsistent (MD –0.63, 95% CI –2.22 to 0.96; I² = 76%; five studies48,75,76,85,161 with 384 participants; very low-quality evidence). A negative MD indicates benefit (lower level of anxiety) and a positive MD reflects harm (higher level of anxiety). Owing to the very low quality of the evidence, we are uncertain about the effect of HSPC on patient anxiety, and the true effect is likely to be substantially different. A sensitivity analysis using unadjusted end-point values, and also using an estimate of 0.02 in adjusting for clustering in McCorkle et al. 48 with unadjusted end-point data, showed no difference between HSPC and usual care. Sensitivity analyses with unadjusted change values, and also using an estimate of 0.02 in adjusting for clustering in McCorkle et al. 48 with adjusted end-point data, showed evidence in favour of HSPC. Given the high level of heterogeneity observed (I² = 76%) in the main analysis, we carried out subgroup analyses. In the studies presenting adjusted end-point data, subgroup analysis by different patient populations did not fully explain heterogeneity, and there was no subgroup effect. Subgrouping by early versus late palliative care and also by countries also did not fully explain heterogeneity and there were no subgroup effects. The validity of the subgroup analysis is uncertain because of the small number of studies and heterogeneity.

Caregiver anxiety

Only one study, by Carson et al. 82 (312 participants), presented adjusted end-point data. Carson et al. 82 reported a higher level of mean caregiver anxiety in the HSPC group (HADS: seven items; 0–21 scale, 21 = maximum distress) than in the control group at 3 months on adjusting for baseline and multiple respondents [mean 7.2 (95% CI 6.6 to 7.9) vs. 6.4 (95% CI 5.7 to 7.1), respectively; MD 0.8 (95% CI –0.1 to 1.8); p = 0.09]. Adjustments for three variables (baseline, multiple respondents and study sites) and six variables (baseline, multiple respondents, study sites, race, sex and primary/additional surrogate) also produced similar results, with p-values of 0.11 and 0.12, respectively. Owing to the very low quality of the evidence, we are uncertain about the effect of HSPC on caregiver anxiety, and the true effect is likely to be substantially different. A sensitivity analysis with unadjusted end-point data also showed no difference between the HSPC and usual-care groups.

Patient depression

The results of eight studies73,75,76,85,129,161,163,167 that reported adjusted end-point data, including a total of 1096 participants, indicate that HSPC may improve patient depression, on average, by –0.22 SMD over usual care (95% CI –0.34 to –0.10; I² = 0%; very low-quality evidence). Negative SMDs indicate benefit (lower level of depression) and positive SMDs indicate harm (higher level of depression). As a result of the very low quality of the evidence, we are uncertain about the effect of HSPC on patient depression; the true effect may be substantially different. Sensitivity analyses using unadjusted end-point values and adjusted change values found no difference between HSPC and usual care. By contrast, a sensitivity analysis using unadjusted change data (SMD –0.38, 95% CI –0.58 to –0.18; I² = 12%; four studies with 488 participants), and a sensitivity analysis testing an estimate of 0.02 in adjusting for clustering in McCorkle et al. 48 with unadjusted end-point data (SMD –0.34, 95% CI –0.65 to –0.03; I² = 42%; four studies with 286 participants) were in favour of HSPC.

Caregiver depression

The results of the studies that presented adjusted end-point data suggest that HSPC has little to no effect on caregiver depression (SMD –0.02, 95% CI –0.21 to 0.18; I² = 0%; two studies82,139 with 413 participants; very low-quality evidence). Negative SMDs indicate benefit (lower level of depression) and positive SMDs indicate harm (higher level of depression). Owing to the very low quality of the evidence, we are uncertain about the effect of HSPC on caregiver depression; the true effect is likely to be substantially different. A sensitivity analysis using unadjusted end-point values showed similar results.

Patient breathlessness

The data that we pooled from studies that reported adjusted end-point values indicate that HSPC may make little to no difference to breathlessness, when compared with usual care (SMD –0.04, 95% CI –0.19 to 0.12; I² = 0%, five studies75,76,148,161,168 with 616 participants; very low-quality evidence). Negative SMDs indicate benefit (reduced breathlessness) and positive SMDs reflect harm (worsened breathlessness). As a result of the very low quality of the evidence, we are uncertain about the effect of HSPC on breathlessness; the true effect is likely to be substantially different. A sensitivity analysis with unadjusted change values also showed that an increase or decrease in breathlessness is possible with HSPC. On the other hand, a sensitivity analysis with unadjusted end-point values was in favour of HSPC.

Adverse events in patients and caregivers

Of the eight studies72,78,97,106,126,139,148,163 (1252 participants) that reported on adverse events, there was no evidence of serious harm. Only one study reported a non-significant increase in adverse events in the HSPC group: 15 serious adverse events in 13 patients in the HSPC group (seven in seven patients in the control group), whereas another study found that the mild adverse event of poorer appetite was higher in the HSPC group.

Caregiver burden

We could not pool data from the two studies (170 participants) that reported adjusted end-point data [Bekelman et al. 139 and Dionne-Odom et al. 136 (linked to Bakitas et al. 73)]. Both studies suggest that HSPC may make little to no difference to caregiver burden (very low-quality evidence). As a result of the very low quality of the evidence, we are uncertain about the effect of HSPC on caregiver burden; the true effect is likely to be substantially different. Dionne-Odom et al. 136 assessed caregiver burden using the MBCB scale, comprising objective burden (range 6–30; 30 indicates highest burden), demand burden (range 4–20; 20 indicates highest burden) and stress burden (range 4–20; 20 indicates highest burden) scales. On the objective burden scale of the MBCB scale, the mean caregiver burden score for the HSPC group was 0.3 points higher than that of the control group, with adjustment for patient death (p = 0.64). On the stress burden scale of the MBCB scale, the mean caregiver burden score for the HSPC group was 0.5 points lower than that of the control group, with adjustment for patient death (p = 0.29). There was no difference in the mean caregiver burden score, with adjustment for patient death, on the demand scale of the MBCB scale (p = 0.97). Bekelman et al. 139 assessed caregiver burden using the ZBI (range 0–88; 88 indicates highest burden) and reported a mean caregiver burden of 12.9 (SE 1.3) in the HSPC group and 14.8 (SE 1.4) in the control group at 12 months (p = 0.30). Only the sensitivity analysis with adjusted change values could be pooled in a meta-analysis, and the result was in favour of HSPC (MD –3.88, 95% CI –5.95 to –1.80; I² = 0%; three studies with 128 participants). Two studies reported unadjusted end-point data, but we also could not pool them in a meta-analysis [Bajwah et al. 72 and Dionne-Odom et al. 136 (linked to Bakitas et al. 73)]. They both found no between-group differences between the HSPC and usual-care groups.

Caregiver grief

Only Dionne-Odom et al. 137 (linked to Bakitas et al. 73) provided usable data on caregiver grief, with no evidence of a difference between the HSPC and usual-care groups (low-quality evidence). Owing to the low quality of the evidence, we are uncertain about the effect of HSPC on caregiver grief; the true effect may be substantially different. Dionne-Odom et al. 137 assessed caregiver grief using the PG-13 (range 11–55; 55 indicates highest level of grief), and reported a mean caregiver grief score in the HSPC group that was 2.2 points lower than that of the control group (p = 0.21). On adjusting for religious preference (p = 0.40), baseline depression levels (p = 0.51) and patient hospice use (p = 0.51), there was still no between-group difference.

Caregiver quality of life

Only Dionne-Odom et al. 136 (linked to Bakitas et al. 73) reported adjusted end-point data on caregiver quality of life, with no evidence of benefit of HSPC over usual care (low-quality evidence). Owing to the low quality of the evidence, we are uncertain about the effect of HSPC on caregiver quality of life; the true effect may be substantially different. Dionne-Odom et al. 136 assessed caregiver quality of life using the CQOL (range 0–140; 140 indicates worse quality of life), and found a mean caregiver quality-of-life score in the HSPC group that was 2 points higher than that of the control group at 3 months, with adjustment for patient death (p = 0.39). A sensitivity analysis with unadjusted end-point data suggests that HSPC may improve caregiver quality of life (MD 6.11, 95% CI 0.42 to 11.81; I² = 0%; two studies with 105 participants).

Evidence from the qualitative studies that explored stakeholders’ views and experiences of HSPC suggested that HSPC was beneficial as it ensured personalised and holistic care for patients and their families, while also fostering open communication and improved understanding of illness. Patients found the specialist expertise and multidisciplinary nature of the HSPC teams to be helpful, and there was oncologist support for early palliative care for patients with newly diagnosed advanced-stage cancer.

Resource use and costs

Very low-quality evidence suggests that the effect of HSPC, compared with that of usual care, on resource use, cost and cost-effectiveness is inconclusive. The evidence on resource use was varied across the different areas assessed. Two studies88,142 found reduced cost with HSPC, when compared with usual care, whereas one study101 found a reduction in the cost of hospitalisation days, but no difference in the cost of emergency room visits. The difference in cost was unclear in one study,118 and the remaining nine studies35,70,75–78,95,156,160 indicated no difference between HSPC and usual care. It was hard to tell if the costs were shifted to other settings (e.g. from acute sector to community) when data on resource use were limited to hospital. Regarding cost-effectiveness, the evidence from the full economic studies was also inconsistent. One study77 reported cost-effectiveness planes of the POS-8 and unpaid caregiver burden (ZBI) against total costs, and found that 34% and 47% of bootstrapped differences in costs and outcomes indicated lower costs and better outcomes for the intervention. Another study75 also presented cost-effectiveness planes with bootstrapping, whereby 66% of replicated combinations of costs and outcomes of distress due to breathlessness (NRS) against total cost indicated lower costs and better outcomes. However, another study76 found that the intervention was not cost-effective: the ICER was £266,333 per QALY, and there was only about a 7% likelihood of lower cost and more QALYs. The last cost-effectiveness study160 calculated the INMB of HSPC and found that the intervention was cost-effective when the willingness-to-pay threshold was > AU$1027 (£915 in 2018 prices) for 1 extra day at home.

Overall completeness and applicability of evidence

The electronic search strategy was highly sensitive to ensure that we captured the breadth of evidence on the topic. We also contacted 15 experts for grey literature and unpublished studies. Consequently, we had a large number of references to screen, and included 42 relevant RCTs, including one study published in Chinese. Importantly, the number of studies reporting on different outcomes varied, especially as we decided to report adjusted end-point values as the main meta-analysis. We presented adjusted values as the main meta-analysis because they control for differences, and also provide the most precise and least biased estimates of treatment effects. Although we had indicated that we would be carrying out subgroup analyses by disease type, HSPC team composition (e.g. physician-led vs. nurse-led vs. MDT-led services and 24 hours’ access vs. temporarily restricted access), models of HSPC and country of origin, in order to explain heterogeneity, we could carry out subgroup analyses on patient anxiety only because of the lack of heterogeneity or limited heterogeneity in other studies. Owing to the small number of studies available for the subgroup analyses we carried out, their findings are uncertain. In the published protocol, we initially had stated that we would be carrying out a subgroup analysis using frailty associated with advanced age. However, no study reported on frailty. In addition, there is a need for better reporting of the findings of studies. Some studies could not be included in the meta-analysis because they did not present analysable data.

The main domains of care addressed in the studies that included either certified experts in palliative care or those described as palliative care clinicians were symptom control, coping and support, and decision-making. Many of the studies also addressed care co-ordination and future-planning. With the exception of future-planning, studies that were unclear about palliative care training of those delivering the HSPC intervention had less focus on these domains.

Most studies were carried out in hospitals with specialised palliative care teams and were largely based in the USA and UK. Palliative care, health policy and resources in these developed countries differ from those of low- and middle-income countries where resources are limited. Recent evidence suggests that, when compared with other countries, European countries and the USA tend to have the highest level of palliative care development. 172 The results obtained from these developed health-care systems cannot be extrapolated to settings with few resources. Furthermore, regulatory environment can have a significant impact on the provision and impact of HSPC on hospitals, patients and unpaid caregivers. For example, in the USA, non-hospital palliative care is provided through a large number of varied private for-profit and non-profit entities, whose effectiveness and success may vary significantly. This aspect of the service also makes the hospital to home-based care transition difficult and lacking in continuity of care. This review has shown that HSPC is expanding to other patient populations besides those with cancer.

Potential biases in the review process

Given that the decisions taken during the process of conducting a systematic review and meta-analysis may be affected by subjective decisions,188 it is important to consider potential biases that may have occurred. Generally, the methods of a meta-analysis provide for transparency and standardisation, thereby enhancing reproducibility of the process. The aim was to bring together the evidence on effectiveness and cost-effectiveness of a complex intervention. For continuous outcomes such as patient HRQoL and patient symptom burden, we combined studies that presented adjusted end-point values as our main meta-analyses. We pooled heterogeneous outcome measures using SMDs. Restricting the main meta-analyses to studies reporting adjusted end-point values reduced the number of studies we could pool together.

We could not include some studies in the meta-analyses because they did not present analysable data. Outcomes that were not reported in a usable format may be systematically different from those that were included in the meta-analyses, thereby introducing selective outcome reporting bias. 58 We followed the GRADE approach in assessing the quality of the evidence for different outcomes. Although the GRADE approach may not always ensure consistency of conclusions, we believe that it offers the advantage of a systematic and transparent process of judging the quality of the evidence. 189

An important step in preventing bias in systematic reviews is to address publication bias. Publication bias has implications for the validity and generalisability of the findings of a meta-analysis. 190 To reduce the possibility of publication bias, we searched different sources, such as electronic databases, carried out citation-tracking, hand-searched relevant studies and reviews, and contacted experts for grey literature and unpublished studies. We drew on a comprehensive search strategy, with input from the information specialist from the Cochrane Pain, Palliative and Supportive Care group, to minimise the chances of missing relevant studies. We believe that this synthesis includes an unbiased sample that covers the populations targeted by this review. Nonetheless, we cannot rule out time-lag bias, which occurs when the results of negative trials take longer to publish than those of positive trials. 191

To be included in this review, the intervention had to be delivered by a MDT. We defined a MDT quite broadly, encompassing studies in which different professionals delivered the intervention, and those in which one single professional led the service and included other professionals as needed. Studies such as Maltoni et al. 192 and Schenker et al. 193 were excluded because they did not meet our definition of a MDT. Furthermore, studies such as Brims et al. 194 and Wong et al. 195 were also excluded because palliative care was an integral part of routine usual care. Our decision to include studies in which the training of the palliative care team was unclear might have implications for the effect estimates that we found, with the possibility of smaller effect sizes in the review. Moreover, in almost half of the studies (n = 20), there was palliative care involvement in the control group. This could have resulted in a smaller effect of the intervention in these studies. Owing to differences in the reporting of the cost-effectiveness results, and also the lack of cost-effectiveness studies in this review, we could not carry out a subgroup analysis to explore differences in cost-effectiveness across countries.

We included studies in which the authors stated that the intervention that they provided was early palliative care or if this was their intention. Given that the definition of early palliative care is still an area of ongoing debate,18 there is a need for consensus on its definition. Early palliative care being the intention of the study authors in this review will assist in having a common definition in future studies, and future reviews could pool these studies to assess its effect.

Agreements and disagreements with other studies or reviews

Four relevant systematic reviews have been published prior to this review. 9,17,18,196 Three included HSPC, whereas Haun et al. 18 assessed the effectiveness of early palliative care for cancer patients only. None of these previous reviews included all the RCTs in this review. This review is the first, to our knowledge, to assess the effectiveness and cost-effectiveness of HSPC on different outcomes in people with cancer, people who do not have cancer and people who have mixed diagnoses.

Dalgaard et al. 196 assessed the best methods for early identification of palliative trajectories in patients with cancer, patients with chronic heart failure and patients with COPD, while also identifying preconditions for early integration of general palliative care in hospitals, and outcomes for patients and relatives. This review included only one of the seminal papers on early palliative care by Temel et al. ,35 which found that early integration of palliative care with standard oncology care for patients with non-small cell lung cancer led to significantly better quality of life and mood, as well as longer survival. This review concluded that evidence about outcomes was sparse and mostly relates to cancer patients receiving specialised palliative care.

Gaertner et al. 17 assessed the effect of specialist palliative care on quality of life and other outcomes in adults with advanced illness in hospital, hospice or community settings. This review included eight RCTs that we also identified in our review and concluded that specialist palliative care had a small beneficial effect on quality of life. The benefits were better among those who received palliative care early for cancer. 17 The review found that the results for pain and other secondary outcomes [fatigue, nausea, dyspnoea, psychosocial variables (distress, depression, anxiety, spiritual well-being, social well-being and satisfaction), survival time, place of death, cost of care and attrition (or completion rate)] were inconclusive.

Haun et al. 18 assessed the effectiveness of early palliative care on different outcomes such as HRQoL, depression, symptom intensity and survival among patients with advanced cancer. This review included six RCTs that were also part of our review and concluded that ‘early palliative care interventions may have more beneficial effects on quality of life and symptom intensity among patients with advanced cancer than among those given usual/standard cancer care alone’. 18 The authors found only small effect sizes. The effects on mortality and depression were uncertain. The authors further stated that results should be interpreted with caution because of the very low to low certainty of the evidence and between study differences regarding participant populations, interventions and methods.

Higginson et al. 9 is the oldest review that was relevant. Its objective was to assess whether or not hospital-based palliative care teams improved the process or outcomes of care for patients and families at the end of life, through a qualitative meta-synthesis and quantitative meta-analysis. It did not include any of the studies in our review, and there was only one RCT. The authors found a small positive effect for hospital-based palliative care teams. Higginson et al. 9 further highlighted the need for better-designed studies comparing different models of HSPC, as well as the use of standardised outcome measures for assessing symptoms.

Our review agrees with these past reviews in some respects, especially with regards to HRQoL. We found evidence that HSPC may be effective in improving patient HRQoL and patient symptom burden at a small effect size. We also found that HSPC may lead to benefits on some of our secondary outcomes, such as patient satisfaction with care, achieving patient preferred place of death (measured by number of home deaths) and patient depression. The quality of the evidence ranged from very low to low. The findings of the review by Gaertner et al. 17 on HRQoL were comparable to our results on patient HRQoL. Gaertner et al. 17 found a small effect of specialist palliative care on HRQoL (seven studies, 1218 participants, SMD 0.16, 95% CI 0.01 to 0.31, moderate-quality evidence). The Cochrane review by Haun et al. 18 also showed a small effect of early palliative care on HRQoL (seven studies, 1028 participants, SMD 0.27, 95% CI 0.15 to 0.38, low-quality evidence).

Authors’ conclusions

Contributions of authors

Adejoke O Oluyase (https://orcid.org/0000-0002-1506-7262) (Postdoctoral Research Associate) was involved in the design, data collection, data analysis and interpretation, drafting the review and critical revision of the review.

Irene J Higginson (https://orcid.org/0000-0002-3687-1313) (Professor of Palliative Care) and Deokhee Yi (https://orcid.org/0000-0003-4894-1689) (Health Economist) were involved in the design, data analysis and interpretation, drafting the review and critical revision of the review.

Wei Gao (https://orcid.org/0000-0001-8298-3415) (Statistician and Reader in Palliative Care), Catherine J Evans (https://orcid.org/0000-0003-0034-7402) [Senior Clinical Lecturer and Honorary Nurse Consultant in Palliative Care (Older People) Sussex Community NHS Foundation Trust], Gunn Grande (https://orcid.org/0000-0003-2200-1680) (Professor of Palliative Care), Chris Todd (https://orcid.org/0000-0001-6645-4505) (Professor of Primary Care and Community Health), Massimo Costantini (https://orcid.org/0000-0002-5293-7079) (Scientific Director) and Fliss EM Murtagh (https://orcid.org/0000-0003-1289-3726) (Professor of Palliative Care) were involved in the design, data analysis and interpretation, and critical revision of the review.

Sabrina Bajwah (https://orcid.org/0000-0001-5338-8107) (Clinical Senior Lecturer and Honorary Consultant in Palliative Care) won funding for the project, and led both the review and the project team. She was involved in the design, data collection, data analysis and interpretation, drafting the review and critical revision of the review.

Publication

Bajwah S, Oluyase AO, Yi D, Gao W, Evans CJ, Grande G, et al. The effectiveness and costeffectiveness of hospital-based specialist palliative care for adults with advanced illness and their caregivers. Cochrane Database Syst Rev 2020;9:CD012780.

Data-sharing statement

All data requests should be submitted to the corresponding author for consideration. Access to anonymised data may be granted following review.

Disclaimers

This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health and Social Care. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health and Social Care.

MEDLINE

Date range searched: 1947 to 27 August 2019.

EMBASE

Date range searched: 1974 to 27 August 2019.

PsycINFO

Date range searched: 1806 to 28 August 2019.

Cumulative Index to Nursing and Allied Health Literature search strategy

Date range searched: 1982 to 28 August 2019.

The Cochrane Library

• CENTRAL: issue 8 of 12, 2019.

• Cochrane Database of Systematic Reviews: issue 8 of 12, 2019.

• DARE: issue 2 of 4, 2015.

• HTA Database: issue 4 of 4, 2016.

• NHS EED: issue 2 of 4, 2015.

CareSearch

Date range searched: inception to 12 September 2019.

Ongoing studies

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IRCT20160521027993N1: Lemeski AT. The effect of palliative care education on self efficacy of elderly with chronic heart failure. Ongoing study, June 2018.

IRCT20160914029817N6: Nouhi E. Effect of non-drug palliative care on quality of life in patients with chronic obstructive pulmonary disease. Ongoing study, June 2018.

IRCT20180531039925N1: Vashani HB. The effect of palliative care program on the quality of life of children with leukemia. Ongoing study, June 2018.

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Matsumoto Y. Early specialized palliative care in Japan: a feasibility study. Ann Oncol 2016;27(Suppl. 7):mdw466.

NCT01828775. Responsible party: City of Hope Medical Centre. Integration of palliative care for cancer patients on phase I trials. Ongoing study, September 2014.

NCT01846520. Responsible party: City of Hope Medical Centre. A randomized trial of a family caregiver palliative care intervention. Ongoing study, October 2013.

NCT01983956. Responsible party: University Hospital Inselspital. A structured early palliative care intervention for patients with advanced cancer – a randomized controlled trial with a nested qualitative study (SENS Trial) (SENS). Ongoing study, December 2013.

NCT02139917. Responsible party: Tam BML. Effects of a transitional palliative care model on patients with end-stage renal failure (ESRF). Ongoing study, August 2014.

NCT02308865. Responsible party: University Hospital, Lille. Impact of early palliative care on quality of life and survival of patients with non-small-cell metastatic lung cancer in Northern France. Ongoing study, October 2014.

NCT02375997. Responsible party: Shen Lin. Early palliative care with standard oncology care versus standard oncology care alone in Metastatic Esophageal Squamous Carcinoma (ESCC) and gastric cancer. Ongoing study, October 2014.

NCT02533921. Responsible party: University of Colorado, Denver. Does outpatient palliative care improve patient-centered outcomes in Parkinson’s Disease? Ongoing study, October 2015.

NCT02543541. Responsible party: Case Comprehensive Cancer Centre. A pilot study of structured palliative care for patients enrolled on phase I clinical trials. Ongoing study, October 2015.

NCT02631811. Responsible party: Hospices Civils de Lyon. Impact on quality of life of an early management supportive care of patients with acute leukemia in first relapse. Ongoing study, November 2015.

NCT02712229. Responsible party: Schenker Y. A cluster randomized trial of a primary palliative care intervention (CONNECT) for patients with advanced cancer. Ongoing study, July 2016.

NCT02719938. Responsible party: University of North Carolina, Chapel Hill. Triggered palliative care for advanced dementia. Ongoing study, March 2016.

NCT02786524. Responsible party: Harris Katherine. A randomized study to evaluate the effect of outpatient symptom management on symptom burden in advanced stage or recurrent gynecologic oncology patients receiving chemotherapy. Ongoing study, February 2016.

NCT02868112. Responsible party: Nipp R. Pilot study of a transdisciplinary intervention integrating geriatric and palliative care with oncology care for older adults with cancer. Ongoing study, October 2016.

NCT02929966. Responsible party: Nava S. Effect of palliative care in patients with end stage pulmonary fibrosis: a randomized control study. Ongoing study, July 2016.

NCT02975869. Responsible party: El-Jawahri A. Randomized trial of a collaborative palliative and oncology care model for patients with acute myeloid leukemia. Ongoing study, November 2016.

NCT03022630. Responsible party: Bernard G. The Creation of Models for Palliative Assessments to Support Severe Illness (COMPASS) investigation: testing early and ongoing implementation of palliative care for incurable non-malignant diseases. Ongoing study, February 2017.

NCT03088202. Loge JH. PALLiON – PALLiative Care In ONcology – a cluster-randomized trial to improve the care for cancer patients with a short life expectancy. Ongoing study, March 2017.

NCT03170466. Responsible party: Kavalieratos D. Primary palliative care in heart failure: a pilot trial. Ongoing study, October 2017.

NCT03181854. Responsible party: Yun YH. Randomized controlled trial of integrated early palliative care for advanced cancer patients. Ongoing study, September 2017.

NCT03229343. Responsible party: Assistance Publique – Hôpitaux de Paris. Impact of a systematic palliative care on quality of life, in advanced idiopathic pulmonary fibrosis (IPF). A randomized multi-center trial. Ongoing study, December 2017.

NCT03310918. Responsible party: El-Jawahri A. Randomized trial of a collaborative palliative and leukemia care model for patients with acute myeloid leukemia receiving non-intensive therapy. Ongoing study, October 2017.

NCT03456323. Responsible party: Baldwin M. Post-ICU palliative care consultation intervention pilot trial in older survivors of acute respiratory failure. Ongoing study, March 2018.

Weber C, Stirnemann J, Herrmann FR, Pautex S, Janssens JP. Can early introduction of specialized palliative care limit intensive care, emergency and hospital admissions in patients with severe and very severe COPD? A randomized study. BMC Palliat Care 2014;13:47.

Study awaiting classification

Aljohani A. Early Interdisciplinary Palliative Care for Patients with Non-small-cell Lung Cancer. 20th Congress of the Asian Pacific Society of Respirology, 3–6 December 2016, Kuala Lumpur, Malaysia, abstract no. 75. 69

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Community

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Rodin G, Lo C, Rydall A, Shnall J, Malfitano C, Chiu A, et al. Managing Cancer and Living Meaningfully (CALM): A randomized controlled trial of a psychological intervention for patients with advanced cancer. J Clin Oncol 2017;35(Suppl. 1).

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Yang GM, Teo I, Neo SH, Tan D, Cheung YB. Pilot randomized Phase II trial of the Enhancing Quality of Life in Patients (EQUIP) intervention for patients with advanced lung cancer. Am J Hosp Palliat Care 2018;35:1050–6.

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Not population of interest

Carter H, Mckinlay E, Scott I, Wise D, MacLeod R. Impact of a hospital palliative care service: perspective of the hospital staff. J Palliat Care 2002;18:160–7.

Cheng SY, Dy S, Fang PH, Chen CY, Chiu TY. Evaluation of inpatient multidisciplinary palliative care unit on terminally ill cancer patients from providers’ perspectives: a propensity score analysis. Jpn J Clin Oncol 2013;43:161–9.

Downing J, Batuli M, Kivumbi G, Kabahweza J, Grant L, Murray SA, et al. A palliative care link nurse programme in Mulago Hospital, Uganda: an evaluation using mixed methods. BMC Palliat Care 2016;15:40.

Galfin JM, Watkins ER, Harlow T. Evaluation of a training programme to teach a guided self-help psychological intervention to hospice staff. Int J Palliat Nurs 2011;17:119–24.

Morita T, Tamura K, Kusajima E, Sakai S, Kawa M, Imura C, et al. Nurse education program on meaninglessness in terminally ill cancer patients: a randomized controlled study of a novel two-day workshop. J Palliat Med 2014;17:1298–305.

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Excluded because of study design

Evaluation of a Hospital-based Palliative Care Programme by Means of a Family Satisfaction Survey. First Australian Conference on Hospice and Palliative Care, Australian Association for Hospice and Palliative Care Inc, 25–28 September 1990, Adelaide, Australia.

Evolution of a Hospital Based Supportive and Palliative Care Service for Patients with End-stage Heart Failure. Eighth Palliative Care Australia Conference – New Horizons, 2005.

Bell CL, Kuriya M, Fischberg D. Pain outcomes of inpatient pain and palliative care consultations: differences by race and diagnosis. J Palliat Med 2011;14:1142–8.

Adelson K, Paris J, Horton JR, Hernandez-Tellez L, Ricks D, Morrison RS, Smith CB. Standardized criteria for palliative care consultation on a solid tumor oncology service reduces downstream health care use. J Oncol Pract 2017;13:e431–e440.

Albanese TH, Radwany SM, Mason H, Gayomali C, Dieter K. Assessing the financial impact of an inpatient acute palliative care unit in a tertiary care teaching hospital. J Palliat Med 2013;16:289–94.

Aldasoro E, Alonso AP, Ribacoba L, Esnaola S, Olaizola M, Carrera JA, et al. Assessing quality of end-of-life hospital care in a southern European regional health service. Int J Technol Assess Health Care 2005;21:464–70.

Alsirafy SA, Abou-Alia AM, Ghanem HM. Palliative care consultation versus palliative care unit: which is associated with shorter terminal hospitalization length of stay among patients with cancer? Am J Hosp Palliat Med 2015;32:275–9.

Alsirafy SA, Mohammed AA, Al-Zahrani AS, Raheem AA, El-Kashif AT. The relation between the timing of palliative care and the frequency and timing of do-not-resuscitate orders among cancer deaths in a tertiary care hospital. Am J Hosp Palliat Care 2015;32:544–8.

Alturki A, Gagnon B, Petrecca K, Scott SC, Nadeau L, Mayo N. Patterns of care at end of life for people with primary intracranial tumors: lessons learned. J Neurooncol 2014;117:103–15.

Amano K, Morita T, Tatara R, Katayama H, Uno T, Takagi I. Association between early palliative care referrals, inpatient hospice utilization, and aggressiveness of care at the end of life. J Palliat Med 2015;18:270–3.

Andershed B, Ternestedt BM. Patterns of care for patients with cancer before and after the establishment of a hospice ward. Scand J Caring Sci 1997;11:42–50.

Araw M, Kozikowski A, Sison C, Mir T, Saad M, Corrado L, et al. Does a palliative care consult decrease the cost of caring for hospitalized patients with dementia? Palliat Support Care 2015;13:1535–40.

Armstrong B, Jenigiri B, Hutson SP, Wachs PM, Lambe CE. The impact of a palliative care program in a rural Appalachian community hospital: a quality improvement process. Am J Hosp Palliat Care 2013;30:380–7.

Au DH, Udris EM, Fihn SD, McDonell MB, Curtis JR. Differences in health care utilization at the end of life among patients with chronic obstructive pulmonary disease and patients with lung cancer. Arch Intern Med 2006;166:326–31.

Austin P, Wiley S, McEvoy PM, Archer L. Depression and anxiety in palliative care inpatients compared with those receiving palliative care at home. Palliat Support Care 2011;9:393–400.

Axelsson B, Christensen SB. Evaluation of a hospital-based palliative support service with particular regard to financial outcome measures (structured abstract). Palliat Med 1998;12:41–9.

Bailey FA, Ferguson L, Williams BR, Woodby LL, Redden DT, Durham RM, et al. Palliative care intervention for choice and use of opioids in the last hours of life. J Gerontol A Biol Sci Med Sci 2008;63:974–8.

Bailey FA, Williams BR, Woodby LL, Goode PS, Redden DT, Houston TK, et al. Intervention to improve care at life’s end in inpatient settings: the BEACON trial. J Gen Intern Med 2014;29:836–43.

Bakitas M, Dionne-Odom JN, Pamboukian SV, Tallaj J, Kvale E, Swetz KM, et al. Engaging patients and families to create a feasible clinical trial integrating palliative and heart failure care: results of the ENABLE CHF-PC pilot clinical trial. BMC Palliat Care 2017;16:45.

Beck-Friis B, Strang P, Sjödén PO. Caring for severely ill cancer patients. A comparison of working conditions in hospital-based home care and in hospital. Support Care Cancer 1993;1:145–51.

Bates MJ, Mijoya A. A review of patients with advanced cervical cancer presenting to palliative care services at Queen Elizabeth Central Hospital in Blantyre, Malawi. Malawi Med J 2015;27:93–5.

Baumann AJ, et al. Benefit of early palliative care intervention in end-stage liver disease patients awaiting liver transplantation. Hepatology 2014;60:944A.

Bell CL, Kuriya M, Fischberg D. Hospice referrals and code status: outcomes of inpatient palliative care consultations among Asian Americans and Pacific Islanders with cancer. J Pain Symptom Manage 2011;42:557–64.

Bendaly EA, Groves J, Juliar B, Gramelspacher GP. Financial impact of palliative care consultation in a public hospital. J Palliat Med 2008;11:1304–8.

Bennett M, Corcoran G. The impact on community palliative care services of a hospital palliative care team. Palliat Med 1994;8:237–44.

Bennett MI, Ziegler L, Allsop M, Daniel S, Hurlow A. What determines duration of palliative care before death for patients with advanced disease? A retrospective cohort study of community and hospital palliative care provision in a large UK city. BMJ Open 2016;6:e012576.

Bergenholtz H, Jarlbaek L, Hølge-Hazelton B. Generalist palliative care in hospital – Cultural and organisational interactions. Results of a mixed-methods study. Palliat Med 2016;30:558–66.

Bernabeu-Wittel M, García-Morillo S, González-Becerra C, Ollero M, Fernández A, Cuello-Contreras JA. [Impact of palliative care and clinical features of patients with terminal diseases in areas of Internal Medicine.] Rev Clin Esp 2006;206:178–81.

Bhatraju P, Friedenberg AS, Uppal A, Evans L. Factors associated with utilization of an inpatient palliative care consultation service in an urban public hospital. Am J Hosp Palliat Care 2014;31:641–4.

Billings JA, Kolton E. Family satisfaction and bereavement care following death in the hospital. J Palliat Med 1999;2:33–49.

Binney ZO, Quest TE, Feingold PL, Buchman T, Majesko AA. Feasibility and economic impact of dedicated hospice inpatient units for terminally ill ICU patients. Crit Care Med 2014;42:1074–80.

Birks T, Krikos D, McGowan C, Stone P. Is there a need for weekend face-to-face inpatient assessments by hospital specialist palliative care services? Evaluation of an out-of-hours service. Palliat Med 2011;25:278–83.

Bischoff K, Weinberg V, Rabow MW. Palliative and oncologic co-management: symptom management for outpatients with cancer. Support Care Cancer 2013;21:3031–7.

Blackhall LJ, Read P, Stukenborg G, Dillon P, Barclay J, Romano A, Harrison J. CARE Track for advanced cancer: impact and timing of an outpatient palliative care clinic. J Palliat Med 2016;19:57–63.

Bollini P, Venkateswaran C, Sureshkumar K. Palliative care in Kerala, India: a model for resource-poor settings. Onkologie 2004;27:138–42.

Bonsignore L, Bloom N, Steinhauser K, Nichols R, Allen T, Twaddle M, Bull J. Evaluating the feasibility and acceptability of a telehealth program in a rural palliative care population: TapCloud for palliative care. J Pain Symptom Manage 2018;56:7–14.

Bramley T, Antao V, Lunacsek O, Hennenfent K, Masaquel A. The economic burden of end-of-life care in metastatic breast cancer. J Med Econ 2016;19:1075–80.

Braus N, Campbell TC, Kwekkeboom KL, Ferguson S, Harvey C, Krupp AE, et al. Prospective study of a proactive palliative care rounding intervention in a medical ICU. Intensive Care Med 2016;42:54–62.

Breitkopf CR, Stephens EK, Jatoi A. Hospice in end-of-life patients with cancer: does it lead to changes in nonhospice health care utilization after stopping cancer treatment? Am J Hosp Palliat Care 2014;31:392–5.

Bretscher M, Rummans T, Sloan J, Kaur J, Bartlett A, Borkenhagen L, Loprinzi C. Quality of life in hospice patients. a pilot study. Psychosomatics 1999;40:309–13.

Gunatilake S, Brims FJ, Fogg C, Lawrie I, Maskell N, Forbes K, et al. A multicentre non-blinded randomised controlled trial to assess the impact of regular early specialist symptom control treatment on quality of life in malignant mesothelioma (RESPECT-MESO): study protocol for a randomised controlled trial. Trials 2014;15:367.

Brims F, Gunatilake S, Lawrie I, Marshall L, Fogg C, Qi C, et al. Early specialist palliative care on quality of life for malignant pleural mesothelioma: a randomised controlled trial. Thorax 2019;74:354–61.

Brinkman-Stoppelenburg A, Polinder S, Vergouwe Y, van der Heide A. Palliative care consultation services in hospitals in the Netherlands: the design of the COMPASS study. BMC Palliat Care 2015;14:68.

Brody AA. The Effects of an Inpatient Palliative Care Team on Mortality, Utilization, and Cost in a Large Non-profit Teaching Hospital. PhD thesis. San Francisco, CA: University of California, San Francisco; 2008.

Brody AA, Ciemins E, Newman J, Harrington C. The effects of an inpatient palliative care team on discharge disposition. J Palliat Med 2010;13:541–8.

Bronwyn Long M, Bekelman DB, Make B. Improving quality of life in chronic obstructive pulmonary disease by integrating palliative approaches to dyspnea, anxiety, and depression. J Hosp Palliat Nurs 2014;16:514–20.

Brown W. Opioid use in dying patients in hospice and hospital, with and without specialist palliative care team involvement. Eur J Cancer Care 2008;17:65–71.

Bruera E, Neumann CM, Gagnon B, Brenneis C, Quan H, Hanson J. The impact of a regional palliative care program on the cost of palliative care delivery. J Palliat Med 2000;3:181–6.

Campbell ML, Guzman JA. Impact of a proactive approach to improve end-of-life care in a medical ICU. Chest 2003;123:266–71.

Campbell ML, Guzman JA. A proactive approach to improve end-of-life care in a medical intensive care unit for patients with terminal dementia. Crit Care Med 2004;32:1839–43.

Chan AT, Jacobs P, Yeo W, Lai M, Hazlett CB, Mok TS, et al. The cost of palliative care for hepatocellular carcinoma in Hong Kong. Pharmaco Economics 2001;19:947–53.

Chan CW, Chui YY, Chair SY, Sham MM, Lo RS, Ng CS, et al. The evaluation of a palliative care programme for people suffering from life-limiting diseases. J Clin Nurs 2014;23:113–23.

Chan KY, Cheng HW, Yap DY, Yip T, Li CW, Sham MK, et al. Reduction of acute hospital admissions and improvement in outpatient attendance by intensified renal palliative care clinic follow-up: the Hong Kong experience. J Pain Symptom Manage 2015;49:144–9.

Chen YR, Yang Y, Wang SC, Chou WY, Chiu PF, Lin CY, et al. Multidisciplinary care improves clinical outcome and reduces medical costs for pre-end-stage renal disease in Taiwan. Nephrology 2014;19:699–707.

Ciais JF, Pradier C, Ciais C, Berthier F, Vallageas M, Raucoules-Aime M. [Impact of a hospice home visit team on unwanted hospitalization of terminally-ill patients at home in acute medical emergencies.] Presse Medicale 2007;36:404–9.

Clark K, Byfieldt N. Improving the quality of care delivered to people imminently dying in hospital by implementing a care bundle: an observational before and after feasibility study. Int J Care Coord 2015;18:18–26.

Constantine LA. Evaluation of a Nurse-led Intervention to Improve Palliative Care for Select Medical Intensive Care Patients. DNP thesis. Morgantown, WV: West Virginia University; 2013.

Costantini M, Toscani F, Gallucci M, Brunelli C, Miccinesi G, Tamburini M, et al. Terminal cancer patients and timing of referral to palliative care: a multicenter prospective cohort study. Italian Cooperative Research Group on Palliative Medicine. J Pain Symptom Manage 1999;18:243–52.

Coyle D, Small N, Ashworth A, Hennessy S, Jenkins-Clarke S, Mannion R, et al. Costs of palliative care in the community, in hospitals and in hospices in the UK. Crit Rev Oncol Hematol 1999;32:71–85.

Currow DC, Allingham S, Yates P, Johnson C, Clark K, Eagar K. Improving national hospice/palliative care service symptom outcomes systematically through point-of-care data collection, structured feedback and benchmarking. Support Care Cancer 2015;23:307–15.

Curtis JR, Treece PD, Nielsen EL, Downey L, Shannon SE, Braungardt T, et al. Integrating palliative and critical care: evaluation of a quality-improvement intervention. Am J Respir Crit Care Med 2008;178:269–75.

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