Predicting Term Induction to Delivery Intervals Utilizing Machine Learning [14N]. (May 2017)

• Record Type: Journal Article
• Authors: Clifford, Corey; Namaky, Devin; Holbert, Michael
• Is Part Of: Obstetrics and gynecology. Volume 129 (2017)Supplement 1
• Publication Date: 2017-05
• Subjects: Obstetrics -- Periodicals; Gynecology -- Periodicals; 618
• DOI: 10.1097/01.AOG.0000514724.50125.a5
• Languages: English
• ISSNs: 0029-7844

Abstract

Abstract : INTRODUCTION: Machine learning methodologies, such as artificial neural networks, are increasingly being used to improve clinical decision making. They can be used to discover complex relationships in clinical data. The goal of this study was to evaluate their ability to predict term induction to delivery intervals. METHODS: A retrospective cohort of 439 term inductions that resulted in an unassisted vaginal delivery was used to develop a predictive artificial neural network. This model utilized factors known at the time of induction, such as maternal demographics, pregnancy history, cervical examination, and current pregnancy complications. A separate cohort of 233 patients was then used to evaluate the efficiency of the model. RESULTS: On the cohort of 233 patients, the model exhibited a predictive odds ratio for delivery within 12 hours of 5.76 (95% CI 3.17-10.43, P < .001). Furthermore, it exhibited a sensitivity, specificity, positive predictive value, and negative predictive value of 78%, 62%, 80%, and 59% respectively. The five elements that contributed the greatest were Bishop score-position, maternal race, parity, presence of diabetes in the current pregnancy, and presence of hypertensive disease in the current pregnancy. Of the remaining factors; gestational age, cervical dilation, gravidity, and maternal BMI demonstrated moderate weighting. The remainder were less important. CONCLUSION: A machine learning approach is moderately successful in predicting induction to delivery intervals in term patients. This technique could be used to guide the timing of the initiation of induction in these patients to maximize staffing availability and improve outcomes.