Predictive Modeling for Perinatal Mortality in Resource-Limited Settings

Abstract

Importance: The overwhelming majority of fetal and neonatal deaths occur in low- and middle-income countries. Fetal and neonatal risk assessment tools may be useful to predict the risk of death.

Objective: To develop risk prediction models for intrapartum stillbirth and neonatal death.

Design, setting, and participants: This cohort study used data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Global Network for Women's and Children's Health Research population-based vital registry, including clinical sites in South Asia (India and Pakistan), Africa (Democratic Republic of Congo, Zambia, and Kenya), and Latin America (Guatemala). A total of 502 648 pregnancies were prospectively enrolled in the registry.

Exposures: Risk factors were added sequentially into the data set in 4 scenarios: (1) prenatal, (2) predelivery, (3) delivery and day 1, and (4) postdelivery through day 2.

Main outcomes and measures: Data sets were randomly divided into 10 groups of 3 analysis data sets including training (60%), test (20%), and validation (20%). Conventional and advanced machine learning modeling techniques were applied to assess predictive abilities using area under the curve (AUC) for intrapartum stillbirth and neonatal mortality.

Results: All prenatal and predelivery models had predictive accuracy for both intrapartum stillbirth and neonatal mortality with AUC values 0.71 or less. Five of 6 models for neonatal mortality based on delivery/day 1 and postdelivery/day 2 had increased predictive accuracy with AUC values greater than 0.80. Birth weight was the most important predictor for neonatal death in both postdelivery scenarios with independent predictive ability with AUC values of 0.78 and 0.76, respectively. The addition of 4 other top predictors increased AUC to 0.83 and 0.87 for the postdelivery scenarios, respectively.

Conclusions and relevance: Models based on prenatal or predelivery data had predictive accuracy for intrapartum stillbirths and neonatal mortality of AUC values 0.71 or less. Models that incorporated delivery data had good predictive accuracy for risk of neonatal mortality. Birth weight was the most important predictor for neonatal mortality.

Figure 1.. Participant Flow Diagram

The flow diagram denotes the number of participants for each data set prior to the removal of missing covariate data. The data sets were censored for both deaths occurring before the time points and missing data. Ineligible participants included those who were enrolled early and later found not to be pregnant and those who were residing outside the study clusters. 502 648/578 633 deliveries had outcome data, 487 642/502 648 deliveries had complete predictor data for prenatal scenario, 487 537/502 648 deliveries had complete predictor data for predelivery scenario, 469 516/487 326 neonates alive on day 2 had complete predictor data for delivery/day 1 scenario, and 468 356/485 966 neonates alive on day 3 had complete predictor data for postdelivery/day 2 scenario.

Figure 2.. Mean (95% CI) for Validation AUC by Scenario for Outcomes of Intrapartum Stillbirth and Neonatal Mortality

EN indicates logistic elastic net; GBE, gradient boosted ensemble; NN, neural network; RF, random forest; and SVM, support vector machine with radial basis function kernel.

Figure 3.. Probability of Mortality as a Function of Birth Weight, Delivery/Day 1 Scenario