Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records

Amanda B. Zheutlin, Luciana Vieira, Ryan A. Shewcraft, Shilong Li, Zichen Wang, Emilio Schadt, Susan Gross, Siobhan M. Dolan, Joanne Stone, Eric Schadt, Li Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objective: Postpartum hemorrhage (PPH) remains a leading cause of preventable maternal mortality in the United States. We sought to develop a novel risk assessment tool and compare its accuracy to tools used in current practice. Materials and Methods: We used a PPH digital phenotype that we developed and validated previously to identify 6639 PPH deliveries 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 with 3 clinical risk-assessment tools and 1 previously published model. Results: Our 24-feature model achieved an area under the receiver-operating characteristic curve (AUROC) of 0.71 (95% confidence interval [CI], 0.69-0.72), higher than all other tools (research-based AUROC, 0.67 [95% CI, 0.66-0.69]; clinical AUROCs, 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 upon admission. Additionally, we identified inflection points for vital signs and labs where risk rose substantially. Most notably, patients with median intrapartum systolic blood pressure above 132 mm Hg had an 11% (95% CI, 8%-13%) median increase in relative risk for PPH. Conclusions: We developed a novel approach for predicting PPH and identified clinical feature thresholds that can guide intrapartum monitoring for PPH risk. These results suggest that our model is an excellent candidate for prospective evaluation and could ultimately reduce PPH morbidity and mortality through early detection and prevention.

Original languageEnglish
Pages (from-to)296-305
Number of pages10
JournalJournal of the American Medical Informatics Association : JAMIA
Volume29
Issue number2
DOIs
StatePublished - 1 Feb 2022

Keywords

  • clinical decision support
  • electronic medical records
  • phenotype
  • postpartum hemorrhage
  • risk assessment

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