Peer-reviewed article
Optimal timing of induction of labour to improve maternal and perinatal outcomes: protocol for an individual participant data and network meta-analysis.
Meacham H, Ona-Igbru A, McNeill R, Ajayi R, Pickering E et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This is a research protocol describing a planned meta-analysis of randomised trials on the timing of labour induction to improve maternal and perinatal outcomes. The study will use individual participant data from multiple trials to assess whether inducing labour at different times benefits pregnant women, particularly those at higher risk of stillbirth. The analysis will examine whether effects differ by maternal age, ethnicity, weight and other characteristics. This is a protocol for future research, not an evaluation of completed work.
Key points
- Stillbirth disproportionately affects Black and Asian mothers, those with obesity, and women over 35
- The study will conduct an individual participant data meta-analysis of randomised trials on labour induction timing
- Primary outcomes include a composite of stillbirth, neonatal death, or severe morbidity requiring neonatal admission
- Analysis will examine differential effects by maternal age, ethnicity, parity, socioeconomic status, body mass index and method of conception
- The protocol is registered on PROSPERO and data collection is planned through June 2025
Limitations
This is a protocol document describing planned research, not completed findings. No actual results or evidence about intervention effectiveness are presented, so the effectiveness of labour induction timing cannot be assessed from this document.
Abstract
Introduction Despite advances in maternity care, stillbirth remains a major burden. It disproportionately affects black and Asian mothers, those with obesity and women over the age of 35 years. Induction of labour may benefit these women, but there is no clear evidence to guide recommendations on optimal timing of induction because of variations in the intervention and insufficient power in primary trials for rare outcomes such as stillbirth and perinatal mortality, or to assess whether effects differ by maternal characteristics. We will conduct an individual participant data (IPD) meta-analysis of randomised trials to assess the overall and differential effect of induction of labour, according to timing of induction and maternal characteristics, on adverse perinatal and maternal outcomes. We will also rank induction of labour timing strategies by their effectiveness to inform clinical and policy decision-making. Methods and analysis We will identify randomised trials on induction of labour by searching MEDLINE, CINAHL, EMBASE, BIOSIS, LILACS, Pascal, SCI, CDSR, ClinicalTrials.gov, ICTRP, ISRCTN registry, CENTRAL, DARE and Health Technology Assessment Database, without language restrictions, from inception to June 2025. Primary researchers of identified trials will be invited to join the OPTIMAL Collaboration and share the original trial data. Data integrity and trustworthiness assessment will be performed on all eligible trials. We will check each study's IPD for consistency with the original authors before standardising and harmonising the data. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool. We will perform a series of one-and-two-stage random-effects meta-analyses to obtain the summary intervention effect on composite adverse perinatal outcome (stillbirth, neonatal death or severe morbidity requiring admission to neonatal unit) with 95% CIs and summary treatment-covariate interactions (maternal age, ethnicity, parity, socioeconomic status, body mass index and method of conception). Heterogeneity will be summarised using tau2, I2 and 95% prediction intervals for effect in a new study. Sensitivity analysis to explore robustness of statistical and clinical assumptions will be carried out. Small study effects (potential publication bias) will be investigated using funnel plots. Ethics and dissemination The study is registered on PROSPERO (CRD420251066346) and ethics approval is not required. We will disseminate findings widely to women, healthcare professionals and policymakers through academic, professional bodies and social media channels, and in peer-reviewed journals to achieve impact. Prospero registration number CRD420251066346.