Integrating Clinical Factors and Parity-Specific Models with Molecular Biomarkers to Better Predict the Risk of Preterm Birth in Asymptomatic Women.

Background/Objectives: Prior 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® 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. Methods: A Model was developed and validated through retrospective analysis of a cohort of singleton pregnancies that resulted in a live term or preterm birth (PTB) (n = 976). The Model's ability to predict sPTB and PTB was assessed and its clinical utility compared to PreTRM. Results: The 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 PreTRM's sensitivity (75.0%) and PPV (14.6%), and a 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. Relative to PreTRM, the Model achieved significantly higher area under the receiver operating characteristic curve (AUC) results when predicting whether a PTB or sPTB would be associated with a neonatal hospital stay ≥5 days (p = 0.001 for PTB; p = 0.044 for sPTB). The Model also achieved significantly higher sensitivity than PreTRM in predicting a ≥5 day hospital stay associated with PTB (p = 0.009) and higher sensitivity for a ≥5 day hospital stay associated with sPTB, showing improved clinical utility over PreTRM. Conclusions: The 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 sPTB and PTB predictor.