Pharmacological and non-pharmacological treatments and outcomes for new-onset atrial fibrillation in ICU patients: the CAFE scoping review and database analyses

Scoping review

• To evaluate the evidence for the clinical effectiveness and safety of pharmacological and non-pharmacological NOAF treatments.

• To provide guidance for the database analysis on:

  • NOAF definitions used for patients in an ICU

  • patient subgroups who develop NOAF in an ICU

  • inclusion/exclusion of specific treatments and potential confounders

• determining barriers to future research.

Database analysis: RISK-II

• To determine how common NOAF is in critical care.

• To determine the typical characteristics of patients with NOAF in critical care and how they compare with other patients in critical care.

• To increase the understanding of the outcomes of patients with NOAF in critical care and how they compare with other patients in critical care.

• To investigate how much of the difference in outcomes is explained by differences in patient characteristics and comorbidities.

Database analysis: MIMIC-III and PICRAM

• To compare the use and clinical effectiveness of pharmacological and non-pharmacological NOAF treatments.

• To determine the incidence of short- and long-term NOAF complications.

Literature searches

The search strategy was developed by an information specialist in MEDLINE (via Ovid^®; Wolters Kluwer, Alphen aan den Rijn, the Netherlands) without any date or language restrictions. The search strategy included terms used to describe NOAF combined with a set of terms used for critical care.

An adapted MEDLINE search strategy was used to search the following databases in March 2019: MEDLINE, EMBASE™ (Elsevier, Amsterdam, the Netherlands), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science™ [Clarivate Analytics, Philadelphia, PA, USA; including Conference Proceedings Citation Index – Science (Clarivate Analytics)], OpenGrey, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials (CENTRAL) and the Database of Abstracts of Reviews of Effects (DARE; searched from 1994 to 2015). We were not able to search the National Guideline Clearinghouse, as suggested in our protocol,22 because this database was no longer available. The following clinical trial databases were searched for studies in progress or completed but not reported: International Standard Randomised Controlled Trial Number (ISRCTN), ClinicalTrials.gov, the EU Clinical Trials register, additional World Health Organization International Clinical Trials Registry Platform (ICTRP) trial databases and the National Institute for Health Research Clinical Trials Gateway.

The search results were imported into EPPI-Reviewer 4 software (Evidence for Policy and Practice Information and Co-ordinating Centre, University of London, London, UK) and duplicates were removed. The search strategies can be found in Appendix 1. The reference lists of included review articles and studies were also reviewed to identify any relevant studies.

Eligibility criteria

The eligibility criteria used to screen titles, abstracts and full-text articles were as follows.

• Population:

  • Studies of adults (age ≥ 16 years) with NOAF (or without any history of AF, for prevention/prophylactic studies) admitted to general medical, surgical or mixed ICUs were included.

  • Studies of cohorts defined by a single disease or narrow disease group not normally admitted to a general ICU (e.g. myocardial infarction) and studies based on service-specific ICUs (e.g. cardiothoracic or neurosurgical) were excluded.

  • Studies in which a majority (> 50%) of patients belonged to a single, specific disease or operative cohort (e.g. liver resections or lung surgery) and cohorts with a known history of chronic or paroxysmal AF were excluded.

  • Studies of disease groups commonly admitted to an ICU, such as sepsis and septic shock, were included.

  • Studies of patients with supraventricular arrhythmias if AF constituted at least 70% of arrhythmias were included. Where these data were unavailable, we included studies that grouped AF and atrial flutter together if no other arrhythmia types were included.

  • Studies reporting on populations that were a mixture of NOAF and known AF were included only if data for the NOAF subgroup were reported separately.

  • Studies that included both ICU and non-ICU patients, but which did not present results separately, were included only if > 50% of the total cohort were ICU patients and if a valid method for confounding adjustment was used with ICU status included as a covariate.

• Intervention:

  • Studies investigating pharmacological, electrical and other non-pharmacological (including electrolyte) treatment strategies for treatment or prevention of NOAF were included.

  • Studies of short- or long-term anticoagulation were included.

  • Studies of ablation or surgical interventions were excluded.

• Comparators:

  • Any eligible intervention could be a comparator, including no treatment or ‘standard care’.

  • Placebo was also eligible.

• Outcomes – any of the following outcomes were eligible:

  • rhythm and rate control

  • length of ICU and hospital stay

  • mortality (ICU, hospital, 30 days and long term)

  • arterial thromboembolism and adverse treatment effects

  • in the case of studies of preventative/prophylactic treatments, the incidence of NOAF had to be reported.

• Study design – we included quantitative studies with the following designs:

  • randomised and non-randomised trials

  • cohort studies and case series containing five or more patients

  • practitioner surveys and opinion pieces (for research recommendations and interventions not otherwise identified) were also included.

Two reviewers independently screened titles and abstracts and potentially relevant full-text articles. Any discrepancies between the reviewers were resolved either through discussion or by a third reviewer if necessary. Titles, abstracts and full-text articles were screened using EPPI-Reviewer 4 software. The screening of titles and abstracts was facilitated by use of the highlighting function in EPPI-Reviewer 4 (which highlights keywords associated with inclusion or exclusion criteria). This function allowed more prompt decisions to be made. After screening the titles and abstracts, all potentially relevant full-text articles were uploaded on Mendeley Reference Manager Software (1.19.5; Elsevier, Amsterdam, the Netherlands) for easy access and sharing purposes.

Full-text articles that were not published in English included papers in French, German, Czech, Chinese and Spanish. These were screened by native speakers. None of the foreign language articles was eligible for the review.

Data charting

Data-charting forms were developed for the following study designs:

• randomised controlled trials (RCTs)

• prospective comparative studies (non-RCTs)

• retrospective comparative studies

• single-group studies.

The extracted data included the following:

• details of the study (authors, country, setting, sample size and proportion of NOAF patients included in the study)

• population characteristics (primary diagnosis; mean age; proportion of males; severity of illness; proportion of patients on vasopressors; proportion of patients with cardiovascular disease, acute renal failure, acute respiratory failure and mechanical ventilation; mean serum potassium levels; and authors’ definition of NOAF)

• description of intervention and comparator(s)

• methods to address confounding (for non-randomised studies)

• results

• any relevant recommendations for the future research.

The data-charting forms were piloted on a small number of studies and were adapted accordingly where necessary. Decisions about which population characteristics to extract were informed by a recent systematic review on risk factors for NOAF on the ICU23 and a retrospective observational study on predictors for sustained NOAF in the critically ill. 24 All data were extracted by one reviewer and checked by another member of the team; any disagreements would be referred to a third member of the team.

Critical appraisal

Randomised trials were evaluated using version 2 of the Cochrane risk-of-bias tool (see Appendix 2). 25 Non-randomised comparative studies that fulfilled the following criteria were evaluated for risk of bias using the Risk Of Bias In Non-randomized Studies – of Interventions (ROBINS-I) tool:26

• reported as full papers

• included at least 100 patients per treatment arm

• reported on methods to adjust for confounding.

The ROBINS-I tool was adapted for use in this scoping review by including a stopping rule: the risk-of-bias assessment stopped if a serious or critical risk-of-bias judgement was made for the ‘bias due to confounding’ domain. For the confounding domain, decisions regarding which covariates should be reported as being controlled for in analyses were made by the clinical experts in the CAFE (Critical care Atrial Fibrillation Evaluation) study team, with supporting references where possible, and are reported in Table 1, along with the risk-of-bias judgements.

Collating and summarising the results

The details of the primary studies were presented in structured tables categorised by study design. For each type of study design, the extent, range and nature of the identified research were described. Study parameters and results were then described and summarised narratively.

Expert panel review

We convened a face-to-face meeting of expert panel members to review our scoping review results and to inform our subsequent database analysis. We created a list of variables identified from our scoping review that may affect NOAF treatment choice. We then circulated this list among our expert panel where it was independently added to and refined. We collated a final list of these confounding variables, which was then ratified by our expert panel. We repeated this process with definitions of NOAF, interventions of interest and outcomes of interest.

Quantity and quality of the research available

Following the removal of duplicates from the articles retrieved by database searches, 3651 articles were screened on their title and abstract. From those screened, 198 articles were identified as of potential interest and were screened on their full text. Two articles were unobtainable: a conference abstract published in 2000 and an old study from 1974 looking at amiodarone as a treatment of supraventricular tachyarrhythmias in critically ill patients. Therefore, copies of the 196 full-text articles were assessed for inclusion in the scoping review and 42 articles were included in the review. One eligible article was identified from checking the reference lists of included review articles. Figure 1 illustrates the flow of the articles throughout the review process and the number of included articles classified by study design. Studies excluded after full-text review are listed in Appendix 4, Table 19.

FIGURE 1.

Flow chart showing the number of studies identified, excluded and eligible for inclusion in the scoping review.

Primary studies of clinical effectiveness and safety

Table 2 presents an overview of the primary study evidence identified in the review. Further details are reported in the following sections, according to study design.

Reviews and guidelines

Twelve review articles53–64 were included in the current review. Of these, two were systematic reviews,53,56 six were narrative review articles54,57–59,62,64 and four were review articles55,60,61,63 that proposed a treatment algorithm based on available evidence. Most of the included reviews53–55,57–61,63,64 (n = 10) were published after 2012. No guidelines were identified in this scoping review.

Surveys and opinion pieces

Definitions used for new-onset atrial fibrillation

Studies varied in how they reported and defined NOAF. Four studies38,43,47,49 required NOAF to have a heart rate of > 100 b.p.m. and two studies31,36 required NOAF to have a heart rate of > 120 b.p.m. Nineteen studies8,27–30,32–35,37,39–42,44–46,48,50 did not provide a heart rate threshold for NOAF. Studies also reported different time periods for which NOAF must be sustained, ranging from 30 seconds to 24 hours. 27,30,31,41,43,47,49 Eighteen studies8,28,29,32–40,42,44–46,48,50 did not define the time period for which NOAF must be sustained. Six studies8,28,29,38,39,50 clarified in which instances AF would be considered as new onset; for example, when a patient had no prior history of AF,38 when a patient had no previous history of atrial tachyarrhythmias and anti-arrhythmic drug use,39 when AF occurred during an ICU stay,39,50 and when AF was absent on admission. 28,29 Ten studies32–35,37,40,44–46,48 did not provide any definition for NOAF.

Recommendations for and barriers to future research

Most researchers concluded that further prospective research accounting for confounding factors is required to determine the success and clinical implications of prophylactic and rhythm and rate control strategies in critically ill patients with NOAF. 8,27,28,30,32,36,37,39,41,44,45,48,53–58,61,62,70 Moreover, it has been emphasised that the optimal regimens and the best dosing strategies for treatments are yet to be established. 38,42,49 Eight primary studies31,33–35,40,43,46,47 and four review articles59,60,63,64 did not provide any recommendations for future research.

Kanji et al. 56 recognised that there are very few prospective studies conducted to evaluate the treatment strategies for NOAF in the critically ill population, given the prevalence of NOAF in this population and the associated morbidity and mortality. Kanji et al. 56 argue that the lack of prospective trials is because of the nature of this population. NOAF is considered an emergency, and enrolling these patients into prospective trials is logistically difficult because rapid treatment is often needed. Moreover, the lack of standardised outcome measures, such as the definition of successful cardioversion, was identified as a major limitation. Kanji et al. 56 also suggested that grouping AF together with other types of supraventricular tachycardias might be inappropriate because the physiology and their treatment response might be different.

Two primary studies29,40 and three review articles61,62,64 that discussed anticoagulation strategies in critically ill patients with NOAF did not provide any recommendations for the future research in this population.

Expert panel review

We convened an expert panel (see Appendix 8) to review the findings of the scoping review to inform definitions, treatments and confounders to be used in the ICU database analysis (see Chapter 4). The scoping review highlighted that definitions of NOAF in patients in an ICU and definitions of treatment success varied. In the absence of any consensus definition of NOAF, we adopted the agreed definition of AF in patients outside an ICU: any AF lasting ≥ 30 seconds. We defined time to cardioversion as the time to first reversion of sinus rhythm, and the time to rate control was defined as the time to a heart rate of < 110 b.p.m. Two studies27,41 defined AF as lasting for longer than 30 seconds. No studies provided a definition for time to cardioversion. Where studies defined a heart rate threshold for AF, it was either > 100 b.p.m. 38,43,47,49 or > 120 b.p.m. 31,36 A list of the interventions used in the studies identified in the scoping review was created and reviewed, but was not altered by the expert panel. We then screened our databases for presence of data pertaining to identified interventions. A list of identified and available interventions is shown in Appendix 8, Treatments to be included in the analysis and identified but unavailable interventions in Appendix 8, Treatments of interest, but not possible with our data. A list of confounding variables was created from those identified in studies in our scoping review. This list was then supplemented though two rounds of individual review by expert panel members, resulting in a final list of confounders that was ratified by the panel. We then screened our databases for the presence of data pertaining to identified confounders. The final list is shown in Appendix 8, Confounding/matching variables.

Data sources

We analysed patient records from the RISK-II database, which includes anonymised, linked, routinely collected data from (1) the Case Mix Programme (CMP) national clinical audit of adult intensive care,74 (2) Hospital Episode Statistics (HES) for England and (3) the Office for National Statistics (ONS) mortality database.

Case Mix Programme data are collected for the purpose of service evaluation and quality improvement in critical care. 75 The CMP includes records for each admission to a participating adult high-dependency unit or ICU in England, Wales and Northern Ireland. Not all ICUs participated during the period for which data were extracted; coverage of adult general ICUs increased over the study period, reaching 100% in the final year of extracted data. Some, but not all, specialist ICUs participated (cardiothoracic ICUs were excluded from the analysis; see Inclusion and exclusion criteria). The CMP was used to identify the study sample, provide dates for the start and end of hospital admission and critical care, and to identify patient demographics.

The HES database is collected for the purpose of reimbursing NHS trusts for the provision of hospital services. The RISK-II database includes records from the admitted patient care section of HES, which contains one record for each ‘episode of care’ under one consultant during a hospital admission. One hospital admission may contain multiple episodes of care, one of which would generally correspond to the period in critical care, but there are differences between trusts in the way that these data are recorded; therefore, HES and CMP records do not align consistently. Each HES record includes up to 20 International Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), diagnosis codes and up to 24 Office of Population Censuses and Surveys Classification of Surgical Operations and Procedures (OPCS-4) codes that were used to identify NOAF, comorbidities and diagnosis-specific rates of subsequent hospitalisation (see Identification of new-onset atrial fibrillation).

The ONS mortality database contains information about all of the deaths registered in the UK, and was used to derive indicators of mortality.

The anonymised RISK-II database is maintained by the Intensive Care National Audit and Research Centre (ICNARC) (London, UK) and was linked by NHS Digital (Leeds, UK) using a standard deterministic algorithm involving the NHS number (a unique patient identifier), date of birth, postcode and sex. The RISK-II database includes CMP records from 1 April 2009 to 31 March 2016, HES records from 1 April 2004 to 31 March 2016 and ONS records from 1 April 2009 to 31 October 2018.

Inclusion and exclusion criteria

We included the records of patients admitted to an ICU between 1 April 2009 and 31 March 2016 (5 financial years). Contiguous ICU admissions, representing transfers between ICUs and readmissions to an ICU within 1 calendar day, were combined into single records. We excluded admissions to cardiothoracic units, admissions lasting < 4 hours and patients aged < 16 years at the time of ICU admission.

Identification of new-onset atrial fibrillation

The codes used for identifying health conditions are summarised in Table 9. We defined patients as having NOAF during an ICU admission by identifying a CMP record that overlapped with a linked HES record that was the first HES record containing an ICD-10 code for AF for that patient anywhere in the database. Adopting a cautious approach to defining NOAF, we excluded patients with a linked HES record relating to the same hospital admission and contained an OPCS-4 procedure code for atrial ablation, pacemaker insertion or DCC during the same hospital admission from this group, because AF may have developed prior to ICU admission.

There are many possible ways that CMP records and HES records can overlap: the HES record may commence on the same day or on an earlier/later date than the CMP record, and similarly may finish on the same day or on an earlier/later date. Many of these scenarios result in uncertainty about whether the AF developed prior to ICU admission, in an ICU or after discharge from an ICU. We, therefore, implemented the following rules for classifying NOAF (Table 10):

• If the HES record commenced on the same day as the CMP record, then it was considered NOAF only if there was also a prior HES record from the same hospital admission (i.e. if the patient was admitted straight to an ICU from an emergency room and, therefore, had no prior HES record from that hospital admission then we assumed that the AF developed prior to ICU admission).

• If the HES record commenced on an earlier date than the CMP record, then the AF was assumed to have developed prior to ICU admission and not to represent NOAF.

• If the HES record commenced after the CMP record commenced and finished before the CMP record finished, then the admission was considered to represent NOAF.

If the HES record continued beyond the CMP record, then it was considered NOAF only if the discrepancy was only 1 day (to allow for minor discrepancies in data entry between the systems), otherwise it was assumed that the AF developed after discharge from the ICU. As a sensitivity analysis, we allowed for any amount of discrepancy in end date provided that other rules were met. This resulted in two possible definitions of NOAF: one for use in the primary analysis and an alternative approach described above for use in the sensitivity analysis. Both, however, reflect a cautious approach to identification of NOAF and exclude many scenarios for which we cannot distinguish NOAF from prior or subsequent AF. This has implications for the interpretation of results that will be highlighted below.

Identification of comorbidities and outcomes

Comorbidities were identified from any previously linked HES record that contained an ICD-10 diagnosis code for diabetes mellitus, hypertension, thromboembolism, valvular heart disease, dilating cardiomyopathy, pulmonary hypertension or heart failure (see Table 9). The date and cause of death were obtained from the linked ONS records. Subsequent hospital admissions were identified by linked HES records and classified as involving AF, stroke or heart failure.

Selection of matched comparators

Observational research using routine data has a fixed ‘observation window’ within which the data are collected (and within the RISK-II database, this window varies across the contributing data sets, as described in Data sources). Patients admitted to an ICU at different points in time are, therefore, followed up for different amounts of time. It is also common for the quality of routine data to vary over time and between contributors to a data set (e.g. between hospitals). To ensure that follow-up and data quality are comparable between patients with NOAF and any comparison group, we selected a cohort of comparators matched on hospital and month/year of admission to ICU.

Comparator patients were selected from all available admissions that were classified as neither NOAF nor prior AF (but including admissions for patients who subsequently developed AF). To ensure that patients with multiple admissions were not over-represented among comparators, one admission was selected at random from each patient’s set of candidate comparator admissions for consideration in the matching process. Matching was then performed with the largest ratio that could be supported while ensuring that at least 99% of patients with NOAF were included in comparisons.

Statistical analysis

Patient characteristics and comorbidities are reported as median with interquartile range (IQR) or as counts and percentages. To account for varying duration of follow-up of patients admitted at different points in time, outcomes were estimated using time-to-event methods with censoring of patients at the end of the relevant data set’s observation window (31 October 2020 for mortality or 30 March 2020 for hospitalisation). Mortality was estimated using the Kaplan–Meier cumulative incidence function from the date of ICU admission and, separately, from the date of hospital discharge among hospital survivors. The cumulative incidences of subsequent hospitalisation with AF, stroke and heart failure were estimated using non-parametric methods to account for the competing risk of death. 76,77 Mortality was assessed at hospital discharge, and at 90 days, 1 year, 3 years and 5 years after hospital discharge, among hospital survivors. The cumulative incidences of hospitalisation with AF, stroke and heart failure were assessed at 1, 3 and 5 years after hospital discharge, among hospital survivors.

We estimated the associations between NOAF and outcomes before and after adjustment for patient characteristics and comorbidities using multivariable regression models adjusting for age, sex and comorbidities. Odds ratios (ORs) for hospital mortality were estimated using logistic regression. Hazard ratios (HRs) for mortality after hospital discharge were estimated using Cox proportional hazard regression. For subsequent hospitalisation with AF, stroke and heart failure, we estimated unadjusted and adjusted cause-specific hazard ratios (CHRs),78,79 censoring patients at death or the limit of follow-up. The proportional hazard assumption was tested by visual inspection of Schoenfeld residual plots. All covariates were modelled using dummy variables except for age, which was modelled continuously using a restricted cubic spline. Knot positions for the restricted cubic spline were selected in in accordance with the recommendations of Harrell. 80

Finally, results for the primary analysis were compared with a sensitivity analysis that employed an alternative operational definition for NOAF, as detailed in Identification of new-onset atrial fibrillation.

Data linkage and matching

The selection of records is summarised in Figure 2. Between 1 April 2009 and 31 March 2016, there were 965,576 admissions to 248 ICUs participating in the CMP, with links available to HES and ONS. After combining multiple records representing transfers and readmissions, a total of 919,801 distinct ICU admissions were extracted. Of these, 841,005 ICU admissions met the inclusion criteria. Of 8203 records identified as NOAF or possible NOAF, 8145 were matched to 48,870 comparators. A total of 4615 (56.7%) patients with NOAF and 27,690 matched comparators were included in the primary analysis.

FIGURE 2.

Record selection and matching.

How common is new-onset atrial fibrillation in critical care?

Of the 841,005 critical care admissions examined, 4615 (0.6%) admissions had a linked HES record indicating likely NOAF. A further 3548 (0.4%) admissions had a linked HES record indicating possible NOAF but where the HES record continued for > 1 day beyond the CMP record (the latter were included in the sensitivity analysis). Although the prevalence of NOAF using either definition appeared stable over time, the prevalence of prior AF (n = 165,150, 19.6%) increased over the first 5 years of the observation window (Figure 3). A reduction in prior AF in the final year of the data partly reflects the unavailability of procedure codes for identifying atrial ablation and pacemaker insertion in that year (a structural limitation of the database, which did not appear to affect the identification of NOAF).

FIGURE 3.

Prevalence of AF in the RISK-II database.

What are the typical characteristics of patients with new-onset atrial fibrillation in critical care and how to do these compare with other patients in critical care?

Patient characteristics and comorbidities are summarised in Table 11. Patients with NOAF tended to be older and have higher levels of comorbidity, especially hypertension, heart failure and valvular heart disease, than comparator patients without NOAF.

What are the outcomes for patients with new-onset atrial fibrillation in critical care and how do these compare with those for other patients in critical care?

The outcomes are summarised in Table 12 and are illustrated in Figures 4 and 5. Patients with NOAF were more likely to die, both during their hospital admission and after discharge, than comparator patients without NOAF. They were also more likely to be subsequently admitted to hospital with AF, stroke or heart failure.

FIGURE 4.

Cumulative incidence of mortality from ICU admission, estimated using the Kaplan–Meier method.

FIGURE 5.

Cumulative incidence of mortality and hospitalisation after hospital discharge. (a) Mortality after discharge; (b) hospitalisation with AF; (c) hospitalisation with stroke; (d) hospitalisation with heart failure. Cumulative incidence of mortality estimated using the Kaplan–Meier approach. The cumulative incidences of hospital admission with AF, stroke and heart failure estimated using non-parametric methods to account for competing risk of death.

How much of the difference in outcomes is explained by differences in patient characteristics and comorbidities?

Adjusted outcomes are summarised in Table 13, with model coefficients provided in Appendix 5, Tables 20 and 21. The excess risk of hospital mortality reduced by about half when controlling for differences in patient characteristics and comorbidities (OR 3.22, 95% CI 3.02 to 3.44, before adjustment, reducing to OR 2.32, 95% CI 2.16 to 2.48, after adjustment).

For outcomes post discharge from hospital, an examination of the Schoenfeld residuals indicated that the proportion hazards assumption was unlikely to be met for mortality or subsequent hospitalisation with AF, but was met for subsequent hospitalisation with stroke and with heart failure. In response to this, for mortality we partitioned follow-up into three time periods: 1–90 days, 90 days to 1 year and > 1 year. We then fitted separate Cox regression models for each time period (see Table 12). Results suggested that a similar proportion of the excess risk of death in the first 90 days after hospital discharge was explained by differences in patient characteristics and comorbidities as for death in hospital. After 90 days, adjustment for these factors explained all of the excess risk of death associated with NOAF (CHR ≈ 1.00, after adjustment).

The analysis of subsequent hospitalisations was complicated by the need to account for both the proportion hazards assumption and the competing risk of death. Because subsequent hospitalisation with AF exhibited the largest between-group difference, we elected to ignore the possible violation of the proportional hazards assumption and present analysis of the entire follow-up period, in keeping with the hospitalisation with stroke and heart failure. The results for hospitalisation with AF should, therefore, be interpreted as an average over the follow-up period that should not be assumed to be constant. Adjustment for patient characteristics and comorbidities indicated that about half of the excess risk of subsequent hospitalisation with each of AF, stroke and heart failure was explained by these factors.

Sensitivity analysis

For the sensitivity analysis, patients who had less-certain evidence indicating possible NOAF and their corresponding comparators were included in the analysis (n = 8145 patients with NOAF or possible NOAF and n = 48,870 comparators). Patient characteristics and comorbidities were similar between the primary and the sensitivity analysis (see Appendix 5, Table 22). However, hospital mortality fell from 43.9% among patients with NOAF in the primary analysis to 34.5% among patients using the expanded definition of NOAF in the sensitivity analysis (mortality among comparators was equivalent between the analyses) (see Appendix 5, Table 23). The results from regression models (see Appendix 5, Table 24) were consistent with the primary analysis in terms of the proportion explained by patient characteristics and comorbidities. There remained a small but statistically significant impact of NOAF on mortality > 1 year after hospital discharge; however, the CI overlapped with the equivalent interval from the primary analysis. Sensitivity analysis outcomes are illustrated in Appendix 5, Figures 11 and 12.

Study design

We carried out a retrospective observational study of two large ICU databases from the UK (PICRAM82) and the USA [Medical Information Mart for Intensive Care III (MIMIC-III) v1.483].

The PICRAM database comprises data relating to > 12,000 patients who were treated in three general ICUs in the UK from 2008 to 2015. MIMIC-III comprises data relating to > 40,000 patients who were admitted to critical care units at a tertiary care hospital in the USA between 2001 and 2012.

All analyses were performed on each database individually given the potential for differences in case mix and interventions. Combined analyses were performed for each outcome to confirm findings. We adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 84

Study population

We included all adult (aged ≥ 16 years) patients. For patients who were admitted more than once to an ICU, we used their first admission. We excluded patients:

• cared for by a coronary care or cardiac surgery team

• with missing hospital outcome data

• with an ICU length of stay of < 24 hours85

• with significant arrhythmia in the first 3 hours of arrival to an ICU

• with pre-existing arrhythmias.

We defined patients as having a pre-existing arrhythmia if an arrhythmia or a medication prescribed with an indication of heart rhythm management were identified in the patient’s medical history.

Exposure and outcomes

For all ICUs that were included in the databases, routine practice was to display three lead electrocardiograms continuously, with heart rhythm recorded by the bedside nurse at regular intervals. We defined NOAF as the documentation of AF or atrial flutter lasting for ≥ 30 seconds. 30 A documented heart rhythm was assumed to persist until the next identifiable rhythm was recorded.

The availability of data relating to the interventions of interest was assessed in the MIMIC-III and PICRAM databases. The MIMIC-III database allowed four interventions to be compared: i.v. amiodarone, i.v. beta-blockers, i.v. calcium channel blockers and electrical cardioversion. The PICRAM database allowed three interventions to be compared: i.v. amiodarone, i.v. beta-blockers and i.v. digoxin. We analysed each intervention in an intention-to-treat fashion, in which treatment groups were determined by first treatment after NOAF onset. 86

Adjustment for confounding

We carried out a propensity score-weighted time-to-event analysis to adjust for measured confounding in the selection of patients between treatment groups. All statistical analyses were performed using R Core v4.0.2. We generated propensity score weights that were optimised to balance the covariate distributions of the treatment groups92 using the WeightIt package. 93 The confounding variables included admission variables, laboratory variables and physiological variables adjacent to NOAF onset. The list of confounding variables was generated based on the studies identified in our scoping review. This list was then reviewed and supplemented by members of the study oversight panel. The admission variables included age; sex; the OASIS88 within the first 3 hours of ICU admission; use of preadmission beta-blocker, antipsychotic or thyroid medication; severe congestive cardiac failure; chronic obstructive pulmonary disease (COPD); liver disease; and thyroid disease. The laboratory variables at NOAF onset included the most recent (to NOAF onset) plasma sodium, potassium, magnesium, creatinine and urea concentrations; white cell count; platelet count; haemoglobin concentration; and prothrombin time. The physiological/intervention variables at NOAF onset included systolic and mean blood pressure, heart rate, body temperature, presence and dose of vasoactive agent, presence of bronchodilator therapy, mechanical ventilation, renal replacement therapy and presence of central venous access.

We assessed the balance of covariates across weighted groups by tabulating group means pre and post weighting. We calculated standardised mean differences (SMDs)94 and the maximum SMD of all pairwise treatment group comparisons for each covariate.

We carried out a weighted Cox survival analysis to determine the average treatment effect of NOAF treatments on our outcomes of interest. Missing laboratory values were handled by using multiple imputation. We generated 20 imputed data sets. To account for the uncertainty in the generated propensity scores and to allow for the estimation of 95% CIs around effect estimates, we performed resampling with replacement (bootstrapping) with recalculation of propensity score weights and effect estimates with each bootstrap sample. We obtained 1000 bootstrap samples from each imputed data set. 95 The effect estimates and CIs from each imputed data set were combined using Rubin’s rules. 96

Critical Care Health Informatics Collaborative database analysis

We also analysed the Critical Care Health Informatics Collaborative (CCHIC) database. 97 This database was created with retrospectively collected detailed data from the ICU clinical information systems from four general ICUs in London and Cambridge, in the UK, from 2014 to 2018.

Of our drugs of interest, the CCHIC database contains beta-blocker data only. We, therefore, decided to use this database to analyse only the epidemiology and characteristics of NOAF to compare with our main analyses.

We used the eligibility criteria stated in Study population. However, we were unable to exclude patients with documented pre-existing arrhythmias because these data were not available in the CCHIC database. Pre-existing arrhythmia was, therefore, determined only by the presence of arrhythmia during the first 3 hours of ICU admission. Full methods are outlined in Appendix 7.

Study population

The MIMIC-III database contains data from 22,684 adult index ICU admissions. Of these patients, 220 had an ICU length of stay of < 24 hours. We identified 3905 of the remaining 22,464 patients as having pre-existing AF or AF documented within the first 3 hours of their ICU admission. Of the 18,559 patients who fulfilled our inclusion criteria, 1065 (5.7%) developed NOAF during their ICU stay. Of these patients, 742 went on to receive one of the interventions of interest. Only two patients received digoxin as their initial treatment and were, therefore, excluded, leaving 740 patients for the comparative analysis. This process is displayed in Figure 6.

FIGURE 6.

Study CONSORT flow diagrams. (a) MIMIC-III database; (b) PICRAM database. Reproduced with permission from Bedford et al. 81 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

Reproduced with permission from Bedford et al. 81 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

The PICRAM database contains data from 12,270 adult index ICU admissions. Of these patients, 3004 had an ICU length of stay of < 24 hours. We identified 899 of the remaining 9266 patients as having pre-existing AF or AF documented within the first 3 hours of ICU admission. Of the 8367 patients who fulfilled our inclusion criteria, 952 (11.4%) developed NOAF during their ICU stay. Of these patients, 471 went on to receive one of the interventions of interest. Five patients had missing outcome data and were, therefore, excluded from the analysis. Of those 466 patients with outcome data, only six patients received DCC or calcium channel blockers and were, therefore, excluded, leaving 460 patients for the comparative analysis. This process is displayed in Figure 6. In both databases, patients who developed NOAF were older, with a similar age difference as was identified in the RISK-II database analysis. Patients who developed NOAF also had longer ICU and hospital length of stay, and higher ICU and hospital mortality (see Appendix 6, Tables 25 and 26), than those who did not develop NOAF. The characteristics of included patients are displayed in Table 14.

The CCHIC database included data that were related to 33,451 adult first admissions to an ICU. Of these patients, 7889 had an ICU length of stay of < 24 hours. We identified 2713 patients being paced or with another significant arrhythmia during the first 3 hours of ICU admission. Of the remaining 22,849 patients, 1003 had missing hospital mortality data. Of the remaining 21,846 eligible patients, 2618 (12%) developed NOAF (see Appendix 7, Figure 23). The characteristics of patients with and without NOAF are shown in Appendix 7, Table 34.

Characteristics of new-onset atrial fibrillation in treated patients

The time from ICU admission to the onset of NOAF in treated patients was similar between the MIMIC-III and the PICRAM databases [median 40.5 hours (IQR 21–79 hours) vs. 40.3 hours (IQR 41–75 hours), respectively]. Patients with data reported in the MIMIC-III database had, on average, shorter total durations of AF [median 11.6 hours (IQR 4–37 hours) vs. 18.1 hours (IQR 6–44 hours), respectively]. The timing of onset and AF duration data are displayed in Figures 7 and 8, respectively.

FIGURE 7.

Time from ICU admission to AF onset in treated patients. (a) MIMIC-III database; (b) PICRAM database. Data from 93 patients with time to AF onset > 168 hours (7 days) not shown.

FIGURE 8.

Total duration of AF per treated patient. (a) MIMIC-III database; (b) PICRAM database. Data from 89 patients with AF duration > 120 hours (5 days) not shown.

Association of new-onset atrial fibrillation with hospital mortality

In the unadjusted analysis, NOAF was associated with an almost identical increased risk of hospital mortality in the MIMIC-III and PICRAM databases [CHR 1.89 (95% CI 1.69 to 2.13) and CHR 1.89 (95% CI 1.68 to 2.13) respectively]. After adjustment for illness severity at ICU admission, this association remained evident [CHR 1.47 (95% CI 1.31 to 1.65) and CHR 1.73 (95% CI 1.54 to 1.96) respectively].

New-onset atrial fibrillation treatments

Of the patients who were identified in the MIMIC-III database, 94 received amiodarone, 473 received beta-blockers, 144 received calcium channel blockers and 29 received electrical cardioversion as their initial NOAF treatment. In the PICRAM database, 344 patients received amiodarone, 47 received beta-blockers and 69 received digoxin as their initial NOAF treatment. The characteristics of patients by treatment group are displayed in Appendix 6, Tables 27 and 28, for the MIMIC-III and PICRAM databases, respectively.

Adjustment for confounding

After propensity score weighting, covariates were well matched across all treatment groups in each database. Across 30 variables, only the mean urea concentration in the MIMIC-III database had the maximum pairwise SMD of > 0.2. Unweighted and weighted means for each treatment group can be found in Appendix 6, Tables 29 and 30.

Rate control

The MIMIC-III database

The time at which 50% of patients had achieved rate control was 60 minutes, 60 minutes, 80 minutes and 10 minutes in the amiodarone, beta-blocker, calcium channel blocker and electrical cardioversion groups, respectively. The cumulative incidence curves of rate control for each treatment group are shown in Appendix 6, Figure 13.

In the unadjusted analysis, no differences were observed between any intervention and amiodarone in the time to achieving rate control (see Appendix 6, Table 31).

After propensity score weighting, beta-blockers, calcium channel blockers and cardioversion were not associated with any significant difference in rate of achieving rate control when compared with amiodarone (HR 1.09, 95% CI 0.78 to 1.51; HR 0.81, 95% CI 0.55 to 1.19; and HR 1.59, 95% CI 0.44 to 5.75; respectively) (Figure 9; see Appendix 6, Table 31).

FIGURE 9.

Unadjusted and adjusted HRs for rate and rhythm control. (a) MIMIC-III unadjusted; (b) PICRAM unadjusted; (c) MIMIC-III adjusted; (d) PICRAM adjusted. Adapted with permission from Bedford et al. 81 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

Adapted with permission from Bedford et al. 81 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

After initial rate control, reversion to a heart rate of ≥ 110 b.p.m. was common. Of those patients achieving rate control, 65%, 62%, 80% and 68% of patients in the amiodarone, beta-blocker, calcium channel blocker and electrical cardioversion groups, respectively, had at least one episode of a heart rate of ≥ 110 b.p.m. within the 24 hours after initial rate control (see Appendix 6, Figure 14).

Rhythm control

Hospital survival

Haemodynamic changes associated with atrial fibrillation onset

Multilevel linear modelling revealed that NOAF was associated with a significant heart rate increase of 22 b.p.m. (p < 0.001) and 19 b.p.m. (p < 0.001) in the MIMIC-III and PICRAM databases, respectively. The average heart rate after AF onset was 122 b.p.m. and 127 b.p.m., respectively.

New-onset atrial fibrillation was associated with a significant reduction in systolic blood pressure in the MIMIC-III and the PICRAM databases of 7 mmHg and 4 mmHg, respectively (p < 0.001). This was despite significant increases in the doses of vasoactive medication after NOAF onset in those receiving vasoactive medications prior to NOAF onset [vasoactive-inotropic score increase of 2.5 (p < 0.001) and 1.8 (p = 0.001), respectively]. New hypotension (systolic blood pressure of < 90 mmHg or mean blood pressure of < 65 mmHg) occurred after NOAF in 28% and 21% of patients with a systolic blood pressure of ≥ 90 mmHg or a mean blood pressure of ≥ 65 mmHg prior to AF onset, respectively. There was a non-significant increase in the proportion of patients receiving vasoactive medications after NOAF onset in the MIMIC-III database (17.6% to 20.2%; p = 0.29). This proportion was unchanged after NOAF onset in the PICRAM database (29%).

The change in heart rate, blood pressure and vasoactive medication use over time, before and after AF onset, is displayed in Figure 10.

FIGURE 10.

Haemodynamic changes associated with AF onset. (a) Heart rate, MIMIC-III database; (b) heart rate PICRAM database; (c) systolic blood pressure, MIMIC-III database; (d) systolic blood pressure, PICRAM database; (e) vasoactive-inotropic score, MIMIC-III database; (f) vasoactive-inotropic score, PICRAM database; (g) proportion of patients on vasoactive medications, MIMIC-III database; (h) proportion of patients on vasoactive medications, PICRAM database. Vasoactive-inotropic score shown for those patients receiving vasoactive medications prior to AF onset. VIS = dopamine dose (µg/kg/minute) + dobutamine dose (µg/kg/minute) + 100 × adrenaline dose (µg/kg/minute) + 10 × milrinone dose (µg/kg/minute) + 10,000 ± vasopressin dose (units/kg/minute) + 100 × noradrenaline dose (µg/kg/minute). Reproduced with permission from Bedford et al. 81 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

Reproduced with permission from Bedford et al. 81 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original figure.

Scoping review

The evidence base available for NOAF management in critically ill patients was very limited. A key problem with the studies identified in this scoping review was that many (n = 12) were single-group studies (i.e. lacking a comparator group). Of the 25 primary studies included in the review, only two were RCTs30,31 and only three of the non-randomised comparative studies27–29 attempted to control for confounding factors, which may have affected outcomes. For all of these studies, which used more robust approaches, there were still serious concerns about how bias (arising from their designs and/or analyses) might affect their results. Although the two RCTs30,31 did not find statistically significant differences in conversion rates between the treatments studied, each contained < 60 patients. Many studies27–30 concluded that more research is needed.

Heterogeneity in the treatment dose (e.g. total doses ranging from < 1 g to 8 g for amiodarone8,31,32,34,35,42,43) and administration (e.g. bolus or continuous infusion), and varying time points to assess conversion to sinus rhythm (e.g. within 2 hours,30 4 hours,31 12 hours30 and 24 hours8,32,35,36) were observed across studies. Comparing studies was, therefore, challenging. There is a need to establish optimal treatment dosing and administration regimens, as well as validated definitions of treatment success. Six studies33,34,36,37,40,48 (14%) were available only as conference abstracts. Only limited data could be extracted for these studies, making their results more difficult to interpret.

Studies varied in how they reported and defined NOAF. Different heart rate thresholds for NOAF were used. 31,36,38,43,47,49 Studies also reported different time periods for which NOAF must be sustained27,30,41,43,47,49 and for which instances AF would be considered NOAF. 8,28,29,38,39,50 Ten studies32–35,37,40,44–46,48 did not provide any definition for NOAF.

The evidence from this review34–36 suggests that beta-blockers might be more effective than amiodarone for the conversion back to normal sinus rhythm, with better outcomes for mortality reported in those who received beta-blockers than in those who received amiodarone. 28,35 A recent UK-wide survey16 found that amiodarone is the most commonly used pharmacological treatment for NOAF in UK ICUs, which suggests that these studies are not changing current practice. Calcium channel blockers appeared to be less effective than beta-blockers and amiodarone for conversion to sinus rhythm. 30,31,36 Two studies27,48 reported that hydrocortisone might be effective as a prophylactic treatment. However, a larger comparative study27 reported slightly higher (but not statistically significant) mortality associated with the use of hydrocortisone. All of the studies reporting effects of hydrocortisone had significant methodological limitations.

The evidence base for NOAF treatment strategies was reported as very limited in two systematic reviews, which agrees with our findings. 53,56 Both systematic reviews were not able to report any conclusive findings, citing the low quality of the reviewed evidence53 and methodological differences between the included studies. 56 In agreement with our findings, the review by Kanji et al. 56 concluded that a standardised outcome measure of success is needed as varying time points used to assess conversion to sinus rhythm limits recommendations on treatment efficacy. 56 Both systematic reviews emphasised the urgent need for further research studies. 53,56

The current literature29,62 suggests that it is unclear if the benefits of administering anticoagulants in critically ill patients with NOAF for stroke prevention outweigh the increased risk of bleeding. Two review articles61,64 proposed a patient-centred approach to administer anticoagulants only in patients with high risk of arterial thromboembolic events. Outside the ICU, withholding anticoagulation for a short time in the perioperative period in patients with AF undergoing elective surgery was not associated with an increased risk of arterial thromboembolism and decreased the risk of major bleeding. 99 It is, however, not known how these findings translate to critically ill patients in ICUs.

The risk assessment scores for subsequent thromboembolism following the development of NOAF have generally not been developed or validated in the ICU population. The CHA₂DS₂-VASc score100 has, however, once been shown to be predictive of thromboembolic event risk in the ICU setting, although with poor sensitivity and predictive value. 101 Bleeding risk scores to guide anticoagulation decisions in NOAF have been developed using population-based cohorts of patients with AF in the community. Common risk factors used in these scores include older age, renal dysfunction, hypertension and a history of bleeding. Comparative studies of these tools report varying results depending on the patient population. 102–105 Patients who develop NOAF during sepsis are at a higher risk of in-hospital and post-discharge stroke and death than those who do not, despite adjusting for confounders. 14,106 Those with NOAF are at a higher risk of thromboembolic complications than patients with pre-existing AF. 106 However, the individual risk of stroke and thromboembolic events in patients who develop NOAF during an ICU admission remains poorly understood.

Although community-based bleeding risk scores are chiefly composed of chronic comorbidities, the bleeding risk in patients in an ICU is likely to be more related to acute factors, such as illness severity, systemic inflammation, type and location of surgery, nutritional status, invasive devices, and acute coagulopathy and thrombocytopenia. 70,107–109 Bleeding risk in critically ill patients with NOAF is higher than patients in the community, with one study demonstrating that 9% of patients who received systemic anticoagulation had significant bleeding warranting cessation of anticoagulation and at least one blood transfusion. 8

A recent UK-wide survey suggested that around one-third of clinicians initiate anticoagulation in patients with NOAF during an ICU admission. 16 Once in stable AF in the community, anticoagulation can be recommended for almost all patients with AF. However, the balance of risks in patients either in an ICU or in whom AF was demonstrated only during the ICU admission is likely to be more complex and dynamic. Modified risk scores that incorporate such complexities are, therefore, required for critically ill patients with NOAF.

The RISK-II database analysis

This analysis of the RISK-II database provides clear evidence of a patient group developing NOAF in critical care with substantially worse short- and long-term outcomes than patients without any record of AF during or prior to ICU admission. We adopted a restrictive approach to ensure that patients’ CMP and HES records provided sufficient confidence in discriminating NOAF in the ICU from prior and subsequent AF. In our sensitivity analysis, we adopted a slightly broader, yet still conservative, definition of NOAF. The outcomes, although somewhat less severe, remained consistent with the primary analysis. Outcomes were substantially worse for patients with AF than for patients with no AF, even after controlling for patient characteristics and past medical history. Patients developing NOAF during an ICU admission were less likely to survive and more likely to be readmitted with AF, stroke or heart failure than those who did not develop NOAF. Comparison with other data sources suggests that our methodology identified a subset of all patients who develop NOAF in critical care. Given the inconsistent nature with which diagnoses are recorded in HES,110 the group identified as developing NOAF in this analysis might best be interpreted as representing patients for whom their AF was of sufficient clinical importance to be documented in their clinical notes (which are then used to code diagnoses in HES records).

The evidence provided by multivariable regression indicates that the impact of NOAF on mortality is not constant over time, but rather focused on the period in and immediately after discharge from hospital. Patients who developed NOAF in an ICU had increased mortality up to the limit of follow-up. However, from 90 days after hospital discharge, the increased mortality appeared to be entirely explained by their older age, sex and comorbidities. During hospital admission and the first 90 days after discharge, roughly half of the increased mortality appeared attributable to either NOAF or unobserved clinical factors that are associated with it. These findings emphasise the importance of developing strategies for both the treatment and the anticoagulation management of NOAF in the period of critical illness and its immediate aftermath, for which current evidence is lacking.

We did not break down multivariable models for subsequent hospitalisation into similar time periods, given the complexity of interpreting proportional hazards regressions with both non-proportional hazards (effects that vary over time) and competing risks. However, given that hospitalisation is closely related to mortality, it may be reasonable to assume that a similar pattern of varying effects over time would be observed.

The MIMIC-III and PICRAM database analysis

To the best of our knowledge, this is the first study to quantify the haemodynamic changes associated with NOAF onset in ICU patients accounting for patients’ pre-AF parameters at scale. We found that NOAF during an ICU admission is associated with a significant increase in heart rate and a significant decrease in blood pressure, despite an increase in vasoactive medication doses. Organised atrial activity contributes to ventricular filling, cardiac output and the closure of the atrioventricular valves. 6 New-onset atrial fibrillation precludes these mechanisms and the effects of the loss of atrial contraction on ventricular filling may be compounded by the diastolic dysfunction commonly seen during critical illness. 111 These mechanisms may explain why NOAF is temporally associated with a reduction in cardiac index in non-ICU patients with chronic heart failure. 7 Our findings are consistent with one previous study that found that haemodynamic instability developed in 37% of post-surgical ICU patients after NOAF. 8

Atrial contractile dysfunction occurs after brief episodes of AF112 and can last for several weeks after achieving rhythm control. 113 Any AF may, therefore, have considerable impact in any critically ill patient with minimal physiological reserve. Indeed, episodes of NOAF lasting for ≥ 30 minutes in the ICU are independently associated with increased hospital mortality. 11 We found that NOAF is associated with hospital mortality after adjustment for confounding variables, regardless of whether NOAF is diagnosed based on continuous monitoring (ICU databases analysis) or from hospital diagnosis codes (RISK-II database analysis). In both cases, some, but not all, of this association is explained by confounding variables. The associated mortality risk was higher in our RISK-II analysis than in our in-ICU analysis. This may be explained by the differing definitions, with NOAF in the RISK-II data probably representing AF significant enough to result in a HES code versus any AF in the in-ICU data.

Together, our findings highlight the importance of optimal treatment and follow-up of patients who develop NOAF during an ICU stay. We found that the use of digoxin is associated with lower rates of achieving heart rate control than the use of amiodarone. Digoxin may be selected to reduce heart rates without inducing hypotension; however, digoxin may be less effective during states of increased sympathetic drive,114 including critical illness. One small study of patients in the ICU with AF with rapid ventricular response (not exclusive to NOAF) demonstrated that digoxin was less effective at rate control in patients receiving catecholaminergic medication. 115

We found that the use of calcium channel blockers was associated with lower rates of achieving rhythm control than amiodarone. These findings are supported by a small, randomised study of paroxysmal AF outside the ICU,116 which reported a cardioversion proportion of 0% in patients who received verapamil compared with 77% in patients who received amiodarone. Our findings contrast with one RCT identified in our scoping review, which compared amiodarone with calcium channel blockers31 and found no difference in achieving rhythm control. However, this study included only 20 patients per treatment group, making it difficult to draw any conclusions. We did not identify any significant difference in hospital survival between any of the treatments when compared with amiodarone.

In the MIMIC-III database, the unadjusted analysis demonstrated an apparent mortality benefit in the beta-blocker treatment group. However, patients in the beta-blocker group were younger and less unwell at presentation than those in the amiodarone group. After developing NOAF, patients in the beta-blocker group had higher blood pressures, were less likely to be on vasoactive medications and had lower inflammatory markers. After our comprehensive adjustment, the difference in mortality was no longer evident. Our adjusted results conflict with one large study,28 which suggested a survival benefit with beta-blockers over amiodarone therapy in patients with sepsis. However, this study was unable to adjust for features around NOAF onset. Our study demonstrates that important differences exist between treatment groups after the onset of AF, which may influence treatment choice. Failure to adjust for these factors is likely to result in residual confounding.

Search strategy

1. ATRIAL FIBRILLATION/

2. ATRIAL FLUTTER/

3. SUPRAVENTRICULAR TACHYCARDIA/

4. (“atrial fibrillation*” or AF).ab,ti.

5. “atrial flutter* “.ab,ti.

6. “atrial arrhythmia* “.ab,ti.

7. (“supraventricular tachycardia*” or SVT).ab,ti.

8. “NOAF*”.ab,ti.

9. 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8

10. INTENSIVE CARE UNITS/

11. CRITICAL CARE/

12. SEPSIS/

13. SEPTIC SHOCK/

14. “intensive care”.ab,ti.

15. “critical care unit* “.ab,ti.

16. “intensive therapy unit* “.ab,ti.

17. “high dependenc* “.ab,ti.

18. (ICU* or ITU* or HDU* or CCU*).ab,ti.

19. “critically unwell”.ab,ti.

20. “critically ill”.ab,ti.

21. (sepsis or “septic shock”).ab,ti.

22. 10 or 11 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20

23. 9 and 22

24. limit 23 to animals

25. limit 23 to (“newborn infant (birth to 1 month)” or “infant (1 to 23 months)” or “preschool child (2 to 5 years)” or “child (6 to 12 years)”)

26. “case report* “.ti.

27. 24 or 25 or 26

28. 23 not 27

29. “atrial fibrillation* “.ab,ti.

30. (cardiac or cardiothoracic or “cardio thoracic” or cardiopulmonary or “cardio pulmonary”).ab,ti.

31. “surg*”.ab,ti.

32. “coronary care unit* “.ab,ti.

33. 29 and 30 and 31

34. 29 and 32

35. 33 or 34

36. 28 not 35.

Search strategy

1. ATRIAL FIBRILLATION/

2. ATRIAL FLUTTER/

3. SUPRAVENTRICULAR TACHYCARDIA/

4. (“atrial fibrillation*” or AF).ab,ti.

5. “atrial flutter* “.ab,ti.

6. “atrial arrhythmia* “.ab,ti.

7. (“supraventricular tachycardia*” or SVT).ab,ti.

8. “NOAF*”.ab,ti.

9. 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8

10. INTENSIVE CARE UNITS/

11. CRITICAL CARE/

12. SEPSIS/

13. SEPTIC SHOCK/

14. “intensive care”.ab,ti.

15. “critical care unit* “.ab,ti.

16. “intensive therapy unit* “.ab,ti.

17. “high dependenc* “.ab,ti.

18. (ICU* or ITU* or HDU* or CCU*).ab,ti.

19. “critically unwell”.ab,ti.

20. “critically ill”.ab,ti.

21. (sepsis or “septic shock”).ab,ti.

22. 10 or 11 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20

23. 9 and 22

24. limit 23 to animal studies

25. limit 23 to (infant <to one year> or preschool child <1 to 6 years> or school child <7 to 12 years>)

26. “case report* “.ti.

27. 24 or 25 or 26

28. 23 not 27

29. “atrial fibrillation* “.ab,ti.

30. (cardiac or cardiothoracic or “cardio thoracic” or cardiopulmonary or “cardio pulmonary”).ab,ti.

31. “surg*”.ab,ti.

32. “coronary care unit* “.ab,ti.

33. 29 and 30 and 31

34. 29 and 32

35. 33 or 34

36. 28 not 35

37. limit 36 to conference abstracts

38. 36 not 37.

Search strategy

1. ATRIAL FIBRILLATION/ (20,100)

2. ATRIAL FLUTTER/ (1521)

3. TACHYCARDIA, SUPRAVENTRICULAR/ (2593)

4. (“atrial fibrillation*” OR AF).ti,ab (24,117)

5. (“atrial flutter*”).ti,ab (1541)

6. (“atrial arrhythmia*”).ti,ab (867)

7. (“supraventricular tachycardia*” OR SVT).ti,ab (1683)

8. (NOAF*).ti,ab (9)

9. (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8) (32,813)

10. INTENSIVE CARE UNITS/ (32,253)

11. CRITICAL CARE/ (19,423)

12. SHOCK, SEPTIC/ (4198)

13. (“intensive care”).ti,ab (50,295)

14. (“critical care unit*”).ti,ab (1967)

15. (“intensive therapy unit*”).ti,ab (254)

16. (“high dependenc*”).ti,ab (627)

17. (ICU* OR ITU* OR HDU* OR CCU*).ti,ab (24,659)

18. (“critically unwell”).ti,ab (17)

19. (“critically ill”).ti,ab (17,286)

20. (10 OR 11 OR 12 OR 13 OR 14 OR 15 OR 16 OR 17 OR 18 OR 19) (92,933)

21. (9 AND 20) (480)

22. (“atrial fibrillation*”).ti,ab (21,936)

23. (cardiac OR cardiothoracic OR “cardio thoracic” OR cardiopulmonary OR “cardio pulmonary”).ti,ab (114,324)

24. (surg*).ti,ab (319,271)

25. (“coronary care unit*”).ti,ab (859)

26. (“case report*”).ti (44,822)

27. (22 AND 23 AND 24) (894)

28. (22 AND 25) (20)

29. (26 OR 27 OR 28) (45,731)

30. 21 NOT 29 (361)

31. 30 [Human age groups Infant∼Newborn: birth-1 month OR Infant: 1-23 months OR Child∼Preschool: 2-5 years OR Child: 6-12 years] (37)

32. ANIMAL STUDIES/ (98,956)

33. (30 AND 32) (1)

34. (31 OR 33) (38)

35. 30 NOT 34 (323).

Search strategy

“TOPIC: (atrial AND fibrillat*).

Refined by: TOPIC: ((intensive OR critical) AND (care OR therapy)).

Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC”.

Search strategy

“atrial fibrillation” AND (“intensive care” OR “critical care” OR “intensive therapy”).

Search strategy

(“atrial fibrillation” AND (“critical care” OR “intensive care”).

Search strategy

“Atrial fibrillation” AND (“Intensive Care” OR “Critical Care” OR “Intensive Therapy”).

ClinicalTrials.gov

URL: https://clinicaltrials.gov/.

Date range searched: inception to 11 March 2019.

Date searched: 11 March 2019.

Records retrieved: 264.

International Standard Randomised Controlled Trial Number

Date range searched: inception to 11 March 2019.

Date searched: 11 March 2019.

Records retrieved: 292.

EU clinical trials register

Date range searched: inception to 11 March 2019.

Date searched: 11 March 2019.

Records retrieved: 12.

World Health Organization International Clinical Trials Registry Platform

Date range searched: inception to 11 March 2019.

Date searched: 11 March 2019.

Records retrieved: 12.

National Institute for Health Research UK Clinical Trials Gateway

Date range searched: inception to 11 March 2019.

Date searched: 11 March 2019.

Records retrieved: 149.