TY - JOUR
T1 - Recurrent preterm birth risk assessment for two delivery subtypes
T2 - A multivariable analysis
AU - Rattsev, Ilia
AU - Flaks-Manov, Natalie
AU - Jelin, Angie C.
AU - Bai, Jiawei
AU - Taylor, Casey Overby
N1 - Funding Information:
This work was supported in part by a Microsoft Investigator Fellowship awarded to COT.
Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - 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.
AB - 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.
KW - medical informatics
KW - pregnancy complications
KW - premature birth
KW - proportional hazards models
KW - risk factors
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U2 - 10.1093/jamia/ocab184
DO - 10.1093/jamia/ocab184
M3 - Article
C2 - 34559221
AN - SCOPUS:85123647143
SN - 1067-5027
VL - 29
SP - 306
EP - 320
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 2
ER -