Integrating clinical factors and parity-specific models with molecular biomarkers to better predict the risk of preterm birth in asymptomatic women

BackgroundPrior spontaneous preterm birth (sPTB) and short cervical length predict the occurrence of sPTB with low sensitivity, highlighting the need for better detectors of at-risk pregnancies. PreTRM(R) is a validated, biomarker-based sPTB predictor that we aimed to improve in this study by developing models that incorporate parity and key risk factors. MethodsA Model was developed and validated through retrospective analysis of a cohort of singleton pregnancies that resulted in live term or preterm birth (PTB). The Models ability to predict sPTB and PTB was assessed and its clinical utility compared to PreTRM. ResultsThe Model predicted sPTB with 77.1% sensitivity, 74.4% specificity, 21.4% positive predictive value (PPV) and 97.3% negative predictive value (NPV), an improvement over PreTRMs sensitivity (75.0%) and PPV (14.6%), and higher PPV than short cervix (16.2%). PTB was predicted by the Model with 76.8% sensitivity, 74.6% specificity, 31.6% PPV and 95.5% NPV. The Model predicted a neonatal hospital stay [≥]5 days with a significantly higher area under the receiver operating characteristic curve (AUC) than PreTRM associated with PTB (p = 0.001) and sPTB (p = 0.044). The Model also achieved significantly higher sensitivity than PreTRM at predicting a [≥]5 day hospital stay associated with PTB (p = 0.009) with improved sensitivity for sPTB, showing overall that the Model performs better than PreTRM in regard to clinical utility. ConclusionsThe Model achieved substantially higher performance than standard of care risk predictors, and an improvement in clinical utility over PreTRM, demonstrating the robustness of the Model as a PTB predictor.