Background

The external validity of randomised trials can be compromised when trial participants differ from real-world populations. In the Antenatal Late Preterm Steroids (ALPS) trial of antenatal corticosteroids at late preterm ages, participants had systematically younger gestational ages than those outside the trial setting. As risk of respiratory morbidity (the primary trial outcome) is higher at younger gestations, absolute benefits of corticosteroids calculated in the trial population may overestimate real-world treatment benefits.

Objectives

To estimate the real-world absolute risk reduction and number-needed-to-treat (NNT) for antenatal corticosteroids at late preterm ages, accounting for gestational age differences between the ALPS and real-world populations.

Methods

Individual participant data from the ALPS trial (which recruited 2831 women with imminent preterm birth at 34+0–36+5 weeks') was appended to population-based data for 15,741 women admitted for delivery between 34+0 and 36+5 weeks' from British Columbia, Canada, 2000–2013. We used logistic regression to calculate inverse odds of sampling weights for each trial participant and re-estimated treatment effects of corticosteroids on neonatal respiratory morbidity in ALPS participants, weighted to reflect the gestational age distribution of the population-based (real-world) sample.

Results

The real-world absolute risk reduction was estimated to be −2.2 (95% CI −4.6, 0.0) cases of respiratory morbidity per 100, compared with −2.8 (95% CI −5.3, −0.3) in original trial data. Corresponding NNTs were 46 in the real-world setting vs 35 in the trial. Our focus on absolute measures also highlighted that the benefits of antenatal corticosteroids may be meaningfully greater at 34 weeks vs. 36 weeks (eg risk reductions of −3.7 vs. −1.2 per 100 respectively).

Conclusions

The absolute risk reductions and NNTs associated with antenatal corticosteroid administration at late preterm ages estimated in our study may be more appropriate for patient counselling as they better reflect the anticipated benefits of treatment when used in a real-world situation.