Subgroup identification of early preterm birth (ePTB): informing a future prospective enrichment clinical trial design

BMC Pregnancy and Childbirth

Research Article

Byron Gajewski¹  John Keighley¹  Chuanwu Zhang¹  Susan Carlson²  Lili Garrard³ 

[1] Department of Biostatistics, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., 66160, Kansas City, KS, USA;Department of Dietetics and Nutrition, School of Health Professions, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., 66160, Kansas City, KS, USA;Division of Biometrics III, OB/OTS/CDER, U.S. Food and Drug Administration, 20993, Silver Spring, MD, USA;

关键词: Early preterm birth;    Risk factor;    Interaction;    Classification and regression tree;    Logistic regression;    Enrichment trial design;   

DOI  :  10.1186/s12884-016-1189-0

 received in 2016-09-14, accepted in 2016-12-08,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundDespite the widely recognized association between the severity of early preterm birth (ePTB) and its related severe diseases, little is known about the potential risk factors of ePTB and the sub-population with high risk of ePTB. Moreover, motivated by a future confirmatory clinical trial to identify whether supplementing pregnant women with docosahexaenoic acid (DHA) has a different effect on the risk subgroup population or not in terms of ePTB prevalence, this study aims to identify potential risk subgroups and risk factors for ePTB, defined as babies born less than 34 weeks of gestation.MethodsThe analysis data (N = 3,994,872) were obtained from CDC and NCHS’ 2014 Natality public data file. The sample was split into independent training and validation cohorts for model generation and model assessment, respectively. Logistic regression and CART models were used to examine potential ePTB risk predictors and their interactions, including mothers’ age, nativity, race, Hispanic origin, marital status, education, pre-pregnancy smoking status, pre-pregnancy BMI, pre-pregnancy diabetes status, pre-pregnancy hypertension status, previous preterm birth status, infertility treatment usage status, fertility enhancing drug usage status, and delivery payment source.ResultsBoth logistic regression models with either 14 or 10 ePTB risk factors produced the same C-index (0.646) based on the training cohort. The C-index of the logistic regression model based on 10 predictors was 0.645 for the validation cohort. Both C-indexes indicated a good discrimination and acceptable model fit. The CART model identified preterm birth history and race as the most important risk factors, and revealed that the subgroup with a preterm birth history and a race designation as Black had the highest risk for ePTB. The c-index and misclassification rate were 0.579 and 0.034 for the training cohort, and 0.578 and 0.034 for the validation cohort, respectively.ConclusionsThis study revealed 14 maternal characteristic variables that reliably identified risk for ePTB through either logistic regression model and/or a CART model. Moreover, both models efficiently identify risk subgroups for further enrichment clinical trial design.

【 授权许可】

CC BY   
© The Author(s). 2017