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Use of a machine learning framework to predict severe maternal morbidity

Associate Name: Andreea Creanga

Funding Source/Period of the Grant: NICHD R03 4/1/2018 - 3/31/2020

Project Description

Severe maternal morbidity (SMM) is on the rise in the United States. Such morbidity is accompanied by delivery complications and adverse pregnancy outcomes, and can have long- term health consequences for women. To date, only a handful of studies examined risk factors for SMM in the United States, and even fewer considered the site of delivery care as potentially influencing SMM occurrence. This study will test the use of machine learning techniques to develop models for predicting women’s risk of experiencing SMM. We will use population-based data from a family of Maryland state databases linked with American Hospital Association Annual Survey data for the 2010-2014 period. Our primary analytic sample will be comprised of all delivery hospitalizations in Maryland hospitals during 2010-2014. Two SMM outcome measures will be employed: Centers for Disease Control and Prevention (CDC)’s SMM algorithm, and a composite measure that includes any of the codes in the CDC SMM algorithm, ICU admission and/or blood transfusion during the delivery hospitalization. Separately for each of the two outcome measures, we will first develop multi-stage least absolute shrinkage and selection operator (LASSO) models to predict SMM and then employ Multiple Additive Regression Tree to maximize the predictive ability of the LASSO models. Next, we will fit Logit regression models for SMM adjusting for LASSO-selected predictor variables and compare LASSO and Logit models’ performance using standard metrics such as sensitivity, specificity, area under the curve of receiver operator characteristic. The proposal has several areas of innovation. Classical analytics tools are not well suited to capture the full value of large data. In contrast, machine learning techniques are unconstrained by preset statistical assumptions and expected to make predictions with higher degrees of accuracy. The success of this pilot study will open up new avenues of study into the potential for machine learning to aid clinical care. Obstetrics is one of the areas that can greatly benefit from its use by predicting maternal risks early and optimizing pathways to the best possible outcomes for women and their newborns. Identifying key predictors of SMM can serve to ascertain health disparities, strengths and weaknesses in obstetric care, and prevent adverse maternal and neonatal outcomes.