Prediction models for determining the success of labor induction: A systematic review
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AbstractIntroductionThe purpose of this study was to systematically identify and compare clinical models using universally accessible clinical and demographic factors that were derived and/or validated to predict the success of labor induction with a view to making recommendations for practice.Material and methodsMEDLINE, Embase, www.clinicaltrials.gov, and PubMed (for non‐MEDLINE and studies in‐progress) were searched from inception to November 2017. Only studies that derived and/or validated clinical prediction models using variables obtained through antenatal history and digital cervical examination were included. Two reviewers independently screened titles and abstracts and extracted data from eligible studies into a standardized form. Extracted data included: participant characteristics, sample size, variables considered and included, endpoint definitions, study design and model performance. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to appraise included studies. In view of clinical and methodologic heterogeneity between studies, only descriptive analysis was possible. The protocol was registered with the PROSPERO International prospective register of systematic reviews [CRD42017081548].ResultsThe search identified 16 studies describing 14 prediction models derived between 1966 and 2018. Models varied and demonstrated major limitations with regard to methodology, scope and performance. Of the derived models, six were internally validated and three were externally validated. Performance was most commonly measured using the area under the receiver operator characteristic curve, which ranged from 0.68 to 0.79, 0.67 to 0.77 and 0.61 to 0.73 for derived, internally validated and externally validated models, respectively. The risk‐of‐bias of included studies ranged from some studies fulfilling only 36% and some others fulfilling 86% of eligible PROBAST items.ConclusionsNo published model can be recommended for use at the bedside to determine the success of vaginal birth after labor induction. Based on the limitations of included models, a list of recommendations for improving model performance and utilization is provided, as well as measures for encouraging appropriate use of prediction models. The attitudes of women and care providers, and the clinical and resource implications must be explored prior to recommending the use of prediction models for determining the success of labor induction.
