The Clinical Usefulness of Predictive Models for Preterm Birth with Potential Benefits: A KOrean Preterm collaboratE Network (KOPEN) Registry-Linked Data-Based Cohort Study

Background: Preterm birth is strongly associated with increasing mortality, incidence of disability, intensity of neonatal care required, and consequent costs. We examined the clinical utility of the potential preterm birth risk factors from admitted pregnant women with symptomatic preterm labor and developed prediction models to obtain information for prolonging pregnancies. Methods: This retrospective study included pregnant women registered with the KOrean Preterm collaboratE Network (KOPEN) who had symptomatic preterm labor, between 16 and 34 gestational weeks, in a tertiary care center from March to November 2016. Demographics, obstetric and medical histories, and basic laboratory test results obtained at admission were evaluated. The preterm birth probability was assessed using a nomogram and decision tree according to birth gestational age: early preterm, before 32 weeks; late preterm, between 32 and 37 weeks; and term, after 37 weeks. Results: Of 879 registered pregnant women, 727 who gave birth at a designated institute were analyzed. The rates of early preterm, late preterm, and term births were 18.16%, 44.02%, and 37.83%, respectively. With the developed nomogram, the concordance index for early and late preterm births was 0.824 (95% CI: 0.785-0.864) and 0.717 (95% CI: 0.675-0.759) respectively. Preterm birth was significantly more likely among women with multiple pregnancy and had water leakage due to premature rupture of membrane. The prediction rate for preterm birth based on decision tree analysis was 86.9% for early preterm and 73.9% for late preterm; the most important nodes are watery leakage for early preterm birth and multiple pregnancy for late preterm birth. Conclusion: This study aims to develop an individual overall probability of preterm birth based on specific risk factors at critical gestational times of preterm birth using a range of clinical variables recorded at the initial hospital admission. Therefore, these models may be useful for clinicians and patients in clinical decision-making and for hospitalization or lifestyle coaching in an outpatient setting.