Recurrent preterm birth risk assessment for two delivery subtypes: A multivariable analysis

Objective: The study sought to develop and apply a framework that uses a clinical phenotyping tool to assess risk for recurrent preterm birth. Materials and Methods: We extended an existing clinical phenotyping tool and applied a 4-step framework for our retrospective cohort study. The study was based on data collected in the Genomic and Proteomic Network for Preterm Birth Research Longitudinal Cohort Study (GPN-PBR LS). A total of 52 sociodemographic, clinical and obstetric history-related risk factors were selected for the analysis. Spontaneous and indicated delivery subtypes were analyzed both individually and in combination. Chi-square analysis and Kaplan-Meier estimate were used for univariate analysis. A Cox proportional hazards model was used for multivariable analysis. Results: A total of 428 women with a history of spontaneous preterm birth qualified for our analysis. The predictors of preterm delivery used in multivariable model were maternal age, maternal race, household income, marital status, previous caesarean section, number of previous deliveries, number of previous abortions, previous birth weight, cervical insufficiency, decidual hemorrhage, and placental dysfunction. The models stratified by delivery subtype performed better than the naive model (concordance 0.76 for the spontaneous model, 0.87 for the indicated model, and 0.72 for the naive model). Discussion: The proposed 4-step framework is effective to analyze risk factors for recurrent preterm birth in a retrospective cohort and possesses practical features for future analyses with other data sources (eg, electronic health record data). Conclusions: We developed an analytical framework that utilizes a clinical phenotyping tool and performed a survival analysis to analyze risk for recurrent preterm birth.

Automated summary

This study developed a 4-step framework using a clinical phenotyping tool to assess risk factors for recurrent preterm birth, showing that the models stratified by delivery subtype performed better in predicting preterm birth risk.