Ilia Rattsev1,2, Natalie Flaks-Manov3, Angie C Jelin4,5, Jiawei Bai6, Casey Overby Taylor1,2,3. 1. Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA. 2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA. 3. Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 4. Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 5. Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. 6. Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
Abstract
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 naïve model (concordance 0.76 for the spontaneous model, 0.87 for the indicated model, and 0.72 for the naïve 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.
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 naïve model (concordance 0.76 for the spontaneous model, 0.87 for the indicated model, and 0.72 for the naïve 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.
Authors: Lidewij van de Mheen; Ewoud Schuit; Arianne C Lim; Martina M Porath; Dimitri Papatsonis; Jan J Erwich; Jim van Eyck; Charlotte M van Oirschot; Piet Hummel; Johannes J Duvekot; Tom H M Hasaart; Rolf H H Groenwold; Karl G M Moons; Christianne J M de Groot; Hein W Bruinse; Maria G van Pampus; Ben W J Mol Journal: J Obstet Gynaecol Can Date: 2014-04
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