Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records

Abstract

ObjectivePostpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the US. Our goal was to develop a novel risk assessment tool and compare its accuracy to those used in current practice.Materials and MethodsWe used a PPH digital phenotype we developed and validated previously to identify 6,639 cases from our delivery cohort (N=70,948). Using a vast array of known and potential risk factors extracted from electronic medical records available prior to delivery, we trained a gradient boosting model in a subset of our cohort. In a held-out test sample, we compared performance of our model to three clinical risk tools and one previously published model.ResultsOur 24-feature model achieved an area under the curve (AUC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUC: 0.67 [95%CI, 0.66-0.69], clinical AUCs: 0.55 [95%CI, 0.54-0.56] to 0.61 [95%CI, 0.59-0.62]). Five features were novel including red blood cell indices and infection markers measured standardly upon admission. Additionally, we identified inflection points for several vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132mmHg had an 11% [interquartile range, 4%] median increase in relative risk for PPH.ConclusionsWe developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. Our results suggest our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.