Abstract
To predict preterm birth in nulliparous women using logistic regression and machine learning. Population-based retrospective cohort. Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. We used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample. During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters. Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.
Highlights
Preterm birth (PTB), birth before 37 weeks, is the leading cause of neonatal death and disability [1]
Logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23–5.42; Type II: AOR: 2.68; 95% CI: 2.05–3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80– 2.30)
The maximum AUC was 60% with artificial neural networks in the validation sample
Summary
Preterm birth (PTB), birth before 37 weeks, is the leading cause of neonatal death and disability [1]. 50% of all perinatal deaths are caused by PTB [2]. In the U.S, almost 10% of babies are born preterm [3], costing the healthcare system at least $26 billion yearly [4]. In Canada, PTB comprises 8% of all births and results in direct costs of $580 million annually [5]. Risk factors for PTB are heterogeneous and include previous PTB, race, age, nulliparity, urinary tract infection, smoking, and bleeding during early pregnancy [6,7,8]. Prediction of PTB would facilitate the use of therapeutic interventions to reduce infant morbidity and mortality, thereby benefitting families, society, and the healthcare system
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