Does progesterone prophylaxis to prevent preterm labour improve outcome? A randomised double-blind placebo-controlled trial (OPPTIMUM)

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Chapter 1 Introduction

The OPPTIMUM study was conceived in 2007, after two large randomised trials1,2 suggested that progestogens prevent preterm delivery and may improve neonatal outcomes. At the conception of the study, we firmly believed that understanding the long-term effects of progesterone on the baby (either good or bad) would be important for both women and caregivers in deciding when preterm birth prophylaxis with progesterone would be important.

By the time OPPTIMUM was completed in 2015, the question of the long-term effects of progesterone, when given for preterm birth prophylaxis, remained important. Preterm birth is the single biggest cause of neonatal mortality and morbidity, with rates of 7.6% in the UK in 2015. 3 Although there has been a modest decline in rates of preterm birth in the USA since 2006, to 11.4% in 2013,4 no such change has been observed in the UK. Worldwide, 15 million babies are born preterm each year, accounting for 2 million deaths within the first month after birth and 77 million disability-adjusted life-years, 3.1% of the global total. 5 The economic burden is huge.

Since starting OPPTIMUM in 2008, further randomised trials have been published examining the efficacy of progestogens to prevent preterm birth. One of two formulations of progestogen are commonly used: (1) a synthetic hormone, 17α-hydroxyprogesterone caproate (17α-OHP), injected intramuscularly; and (2) ‘natural’ progesterone, usually administered vaginally. Several systematic reviews, the most recent by the Cochrane collaboration,6 and one individual patient-level data meta-analysis7 have summarised the effect of progestogens on obstetric and neonatal outcomes. We performed a literature search on 11 July 2016 to identify any randomised trials in which asymptomatic women with a singleton pregnancy were given progesterone or progestogens with the aim of preventing preterm birth that were published since the search date of the Cochrane meta-analysis (January 2013). The only relevant published study was OPPTIMUM,8 the study described in this report.

The Cochrane review6 summarises the data by preterm birth risk (e.g. previous preterm birth or cervical shortening). In women with a previous preterm birth, progestogen prophylaxis reduces preterm birth before 34 weeks’ gestation, perinatal mortality, birthweight of < 2500 g and rates of neonatal death (Table 1).

In women with cervical shortening, progestogens reduce the risk of preterm birth before 34 weeks’ gestation, but have no significant effect on perinatal mortality, birthweight of < 2500 g or neonatal death (Table 2).

In contrast, in the individual patient-level data meta-analysis7 restricted to women with cervical shortening, progesterone prophylaxis reduced both rates of preterm birth and composite adverse neonatal outcomes with relative risks of 0.58 and 0.57, respectively (Table 3).

The reasons for the discrepancy in results for the outcomes for women with a short cervix are not clear. It is possible that the additional statistical power conferred by analysis of the individual patient-level data is responsible for the significant reduction reported in the Romero et al. 7 paper but not the Cochrane review. 6 Alternative explanations are that 17α-OHP is ineffective in women with a short cervix and that inclusion of these data in the Cochrane review,6 but not in the Romero et al. 7 paper, accounts for the difference in results. Regardless, there is a consensus from both these systematic reviews6,7 that progesterone prevents preterm birth, at least in women with a short cervix, but disagreement about whether or not this reduction in preterm birth is associated with improved outcomes for the baby.

17α-hydroxyprogesterone caproate (Makena^®; Amag Pharmaceuticals, Waltham, MA, USA) is the only progestogen licensed for preterm birth prevention in the USA, with the licensing application having been supported by data from the Meis et al. 9 trial. The indication for use is to reduce preterm birth in women with a history of spontaneous preterm birth in a previous singleton pregnancy, where the index pregnancy is a singleton pregnancy. 10

Although 17α-OHP and progesterone are both progestogens, they are somewhat different drugs and may have different effects. A licensing application was submitted to the US Food and Drug Administration (FDA) for progesterone based on data from a large randomised trial of progesterone to prevent preterm birth in women with a short cervix,11 but the FDA advisory panel voted 13 to 4 against it. 12

The primary rationale for the OPPTIMUM study was that the long-term effects of progesterone prophylaxis to prevent preterm birth on the child are unknown. It is plausible that preventing preterm birth could be harmful: preterm birth is known to be associated with high rates of intrauterine infection and/or inflammation,13 and intrauterine infection is known to have deleterious effects on the baby. 14 The absence of adverse effects in the short term does not mean that there will be no long-term harm. For example, in the ORACLE II trial,15,16 maternal administration of antibiotics to prevent preterm birth had no effect on the baby in the short term, but there was an increase in the rate of cerebral palsy at 7 years of age with each of co-amoxicillin and erythromycin, with some evidence of higher rates when both antibiotics were given together.

Hence, the purpose of the OPPTIMUM study was to determine whether or not, in women at high risk of preterm labour, 200 mg of prophylactic vaginal natural progesterone, inserted once daily from 22 to 34 weeks’ gestation, compared with placebo:

1. improves obstetric outcome by lengthening pregnancy and, thus, reduces the incidence of preterm delivery (before 34 weeks’ gestation)

2. improves neonatal outcome by reducing a composite of death and major morbidity

3. leads to improved childhood cognitive and neurosensory outcomes at age 2 years.

A successful grant application was submitted to the Medical Research Council (MRC) in 2007 to test these hypotheses.

Chapter 2 Methods

The OPPTIMUM study methodology is described in detail in the published protocol17 and in the ‘working’ protocol of this paper. An abbreviated version is also described in the main publication8 summarising the results of the study.

Chapter 3 Results

Chapter 4 Safety evaluation

Treatment compliance (assessed according to the criteria described above) is shown in Table 11. We assessed compliance by looking at medication pack returns, patient diaries and asking patients what they had been taking. Prior to unblinding, we defined adequate compliance as women in whom the proportion of actual doses of study medication were 80% of those of expected doses.

Compliance was calculated from the expected number of doses taken and the assumed number of doses taken, based on the number of doses issued (usually 84) and the number returned or reportedly lost. If the number returned or lost was not recorded, this were taken as zero. In some cases, this yields implausibly large values for compliance.

Six women had a derived compliance value of > 120%:

1. compliance = 2100% – expected four doses; number of doses returned or lost not recorded; doses taken calculated as 84

2. compliance = 158% – expected 53 doses; number of doses returned or lost both zero; doses taken calculated as 84

3. compliance = 156% – expected 53 doses; number of doses returned = 1, lost = 0; doses taken calculated as 83

4. compliance = 138% – expected 26 doses; number of doses returned = 48, lost = 0; doses taken calculated as 36

5. compliance = 135% – expected 17 doses; number of doses returned = 61, lost = 0; doses taken calculated as 23

6. compliance = 133% – expected nine doses; number of doses returned = 0, lost = 72; doses taken calculated as 12.

The compliance value for subject 1, listed above, is clearly erroneous, as the participant could not have taken all 84 doses within 4 days. This subject withdrew from study treatment very soon after randomisation, delivered shortly afterwards – at approximately 25 weeks’ gestation – and withdrew from the study. The child died within 2 weeks of birth. However, there is no information that indicates that the participant was not compliant with treatment during the time that she was supposedly taking the medication.

Compliance (excluding data from subjects who had missing compliance data) is shown in Table 12.

Of the individuals indicated below, the following remain (only the 2100% is removed):

1. compliance = 158% – expected 53 doses; number of doses returned or lost both zero; doses taken calculated as 84

2. compliance = 156% – expected 53 doses; number of doses returned = 1, lost = 0; doses taken calculated as 83

3. compliance = 138% – expected 26 doses; number of doses returned = 48, lost = 0; doses taken calculated as 36

4. compliance = 135% – expected 17 doses; number of doses returned = 61, lost = 0; doses taken calculated as 23

5. compliance = 133% – expected nine doses; number of doses returned = 0, lost = 72; doses taken calculated as 12.

Premature treatment withdrawal is shown in Table 13.

Serious adverse events (SAEs) known to occur in the safety population in the reporting window (maximum of end of treatment date + 28 days and date of delivery + 30 days) or where it is unclear whether or not they are in the reporting window are listed in Table 14.

Serious adverse events known to occur outside the reporting window and those in which the timing was uncertain are also reported separately in Appendix 3.

Other prespecified safety outcomes are shown in Tables 15–17.

Chapter 5 Subgroup analyses

Subgroup analyses for the subgroups fibronectin positive (yes/no), short cervix (yes/no; ≤ 25 mm and < 15 mm), previous preterm birth and chorioamnionitis are shown in Tables 18–22.

Chapter 6 Further analysis of factors influencing the childhood outcome

As a further post hoc analysis, we investigated the influence of gestational age at birth and other factors at birth on the childhood outcome.

Figure 1 shows a scatterplot of gestational age at delivery and Bayley-III cognitive composite scores (with deaths imputed).

FIGURE 1.

Scatterplot of raw values of gestational age at delivery and Bayley-III cognitive composite scores, with a Lowess line.

These data show that, at gestational ages of < 34 weeks, there is a linear relationship between gestation at delivery and the Bayley-III cognitive composite score. The shape of the Lowess line suggests that a quadratic model might fit best, which was confirmed by comparing the quadratic fit to thinplate regression splines and finding a very similar shape.

Table 23 shows the results for unadjusted and adjusted models predicting Bayley-III cognitive composite scores from gestational age as a linear and a quadratic term.

The predicted scores in Figure 2 are for a woman of average age, education and body mass index (BMI), who has had no previous pregnancy of ≤ 14 weeks, does not smoke and is at a low risk of preterm birth.

FIGURE 2.

Unadjusted and adjusted models predicting Bayley-III cognitive composite scores from gestational age as a linear and a quadratic term.

Gestational age at delivery has a significant effect on the cognitive outcome. Adjustment alters the effect estimates only slightly. Other significant predictors are maternal age, BMI, the number of previous pregnancies and whether the woman was in the high- or the low-risk group; with higher maternal age, lower BMI, lower number of previous pregnancies and being of a low risk predicting higher Bayley-III cognitive composite scores.

In addition, the relation between gestational age and Bayley-III cognitive composite scores has been analysed including gestational age as a categorical variable (gestational ages rounded to weeks), with 40 weeks as the reference group. Figure 3 shows the estimated regression coefficients for each week.

FIGURE 3.

Regression coefficients for gestational age at delivery from a linear model predicting Bayley-III cognitive composite scores from gestational age at delivery as a categorical variable, with 95% CIs. Gestational ages of 22 and 23 weeks have been grouped together, as well as gestational ages of 42 and 43 weeks. The reference category is 40 weeks. The adjusted model adjusts for mother’s age, years in full-time education, BMI, smoking, previous pregnancies of ≥ 14 weeks and high/low risk.

Those results suggest that the lower gestational age, the higher the gain from each additional week of gestation. From week 34 or 36 (weeks 34 and 35 results are unclear) onwards, there seems to be little additional gain from longer gestation.

Chapter 7 Discussion and overall conclusions

The OPPTIMUM study aimed to test the hypotheses that progesterone:

• improves obstetric outcome by lengthening pregnancy and reducing the incidence of preterm delivery (before 34 weeks’ gestation)

• improves neonatal outcome by reducing a composite of death and major morbidity

• leads to improved childhood cognitive and neurosensory outcomes at age 2 years.

In the OPPTIMUM study, the CI of the OR of treatment effect crossed unity for each of the obstetric, neonatal or childhood outcomes, suggesting that progesterone had no effect on any of these outcomes. These data contrast with the meta-analyses6,7 on preterm birth prevention (the obstetric outcome) detailed in Chapter 1, which found that progesterone prevents preterm birth. The literature is less consistent on whether or not progesterone improves neonatal outcomes. For women with a short cervix, two major meta-analyses6,7 come to different conclusions for the neonatal outcome, with one7 showing that progesterone reduces adverse outcomes and the other6 finding no benefit. For women with a previous preterm birth, the Cochrane meta-analysis6 suggests that progesterone reduces perinatal death and other adverse neonatal outcomes. OPPTIMUM, the largest single randomised trial, found no effect of progesterone on the composite neonatal outcome. In subgroup analyses, none of the p-values of any of the interaction terms approached statistical significance; in other words, we found no evidence that progesterone is any more effective in any subgroup.

The study benefited from participation of PPI in the conduct of the study. Having PPI representatives on the trial steering committee was useful in focusing on what patients would find helpful. Our PPI representatives faced the challenge that many ‘pregnancy’ PPI representatives face, that of little time to contribute to the study because of the competing demands of their young family.

Reported compliance was 68.6% (95% CI 65.8% to 71.5%). This rate is similar to or better than compliance rates seen when drugs are taken for clinical indications; hence, we believe that efficacy is as good or better as would be achieved in ‘real-world’ situations. 21 Although other studies11 have reported higher compliance, this is based on counting returned unused medication, a strategy likely to overestimate compliance.

Some commentators have noted that the ORs for the obstetric and neonatal outcome are in the direction of benefit, and have suggested that OPPTIMUM was underpowered to show benefit. We powered the study carefully as described in the protocol and in the statistical analysis plan (see Appendix 2), and we ultimately recruited to the planned sample size. Post hoc, we compared the planned with the actual event rate for the obstetric outcome in the placebo group. In planning our sample size, we calculated that the obstetric outcome event rate would be 40% for those in the fFN-positive group and 10% for those in the fFN-negative group for a study power of 81% (see power calculation in Appendix 2 and published). 17 We anticipated recruiting 375 women in the fFN-positive group and 750 women in the fFN-negative group in the study as whole. Assuming half of these women were randomised to the placebo group, the number of outcome events in the placebo group would be 0.5 × [(0.4 × 375) + (0.1 × 750)] = 112.5. Once OPPTIMUM was complete, the event rate in the fFN-positive group was a little lower and the event rate in the fFN-negative group was a little higher than expected, with the actual number of obstetric outcome events in the placebo group being 108. The failure to show an effect of progesterone (at least for the obstetric outcome) was not because the sample size was too small, but because the effect size (an OR of 0.86 for the obstetric outcome) was less than anticipated; in other words, because progesterone was much less effective than anticipated. Hence, OPPTIMUM’s failure to demonstrate benefit (at least for the obstetric outcome) is not because it is underpowered.

Progesterone is endorsed for preterm birth prevention in women with a short cervix by several expert guideline groups including the Society for Maternal Fetal Medicine in the USA (that recommend its use in women with a cervical length of ≤ 20 mm)20 and the National Institute for Health and Care Excellence in the UK (that endorse its use in women with a cervical length of ≤ 25 mm). 21 Both of these guidelines (generated before the publication of OPPTIMUM) are likely to be revisited to take into account the data described here. We believe that a comprehensive individual patient-level data meta-analysis, evaluating the effect of progesterone in a variety of ‘at-risk’ subgroups, is likely to be helpful in determining the appropriate role of progesterone for preterm birth prevention.

Acknowledgements

References

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Appendix 1 Study drugs

Two SmPCs are shown. The first with arachis oil as the excipient and the second with sunflower oil as the excipient.

(PDF download)

Appendix 2 Statistical analysis plan

(PDF download)

Appendix 3 Statistical analysis output