Prognostication for prelabor rupture of membranes and the time of delivery in nationwide insured women: development, validation, and deployment

Abstract

AbstractImportancePrognostic predictions of prelabor rupture of membranes lack proper sample sizes and external validation.ObjectiveTo develop, validate, and deploy statistical and/or machine learning prediction models using medical histories for prelabor rupture of membranes and the time of delivery.DesignA retrospective cohort design within 2-year period (2015 to 2016) of a single-payer, government-owned health insurance database covering 75.8% individuals in a countrySettingNationwide healthcare providers (n=22,024) at primary, secondary, and tertiary levelsParticipants12-to-55-year-old women that visit healthcare providers using the insurance from ∼1% random sample of insurance holders stratified by healthcare provider and category of family: (1) never visit; (2) visit only primary care; and (3) visit all levels of carePredictorsMedical histories of diagnosis and procedure (International Classification of Disease version 10) before the latest visit of outcome within the database periodMain Outcomes and MeasuresPrelabor rupture of membranes prognostication (area under curve, with sensitivity, specificity, and likelihood ratio), the time of delivery estimation (root mean square error), and inference time (minutes), with 95% confidence intervalResultsWe selected 219,272 women aged 33 ± 12 years. The best prognostication achieved area under curve 0.73 (0.72 to 0.75), sensitivity 0.494 (0.489 to 0.500), specificity 0.816 (0.814 to 0.818), and likelihood ratio being positive 2.68 (2.63 to 2.75) and negative 0.62 (0.61 to 0.63). This outperformed models from previous studies according to area under curve of an external validation set, including one using a biomarker (area under curve 0.641; sensitivity 0.419; sensitivity 0.863; positive likelihood ratio 3.06; negative likelihood ratio 0.67; n=1177). Meanwhile, the best estimation achieved ± 2.2 and 2.6 weeks respectively for predicted events and non-events. Our web application only took 5.14 minutes (5.11 to 5.18) per prediction.Conclusions and RelevancePrelabor rupture of membranes and the time of delivery were predicted by medical histories; but, an impact study is required before clinical application.Key PointsQuestionCan we use medical histories of diagnosis and procedure in electronic health records to predict prelabor rupture of membranes and the time of delivery before the day in nationwide insured women?FindingsIn this prognostic study applying retrospective cohort paradigm, a significant predictive performance was achieved and validated. The area under receiver operating characteristics curve was 0.73 with the estimation errors of ± 2.2 and 2.6 weeks for the time of delivery.MeaningPreliminary prediction can be conducted in a wide population of insured women to predict prelabor rupture of membranes and estimate the time of delivery.