Retrospective application of algorithms to improve identification of pregnancy outcomes from the electronic health record

J Perinatol. 2023 Jan;43(1):10-14. doi: 10.1038/s41372-022-01496-1. Epub 2022 Sep 1.

Abstract

Objective: To improve upon the accuracy of ICD codes for identifying maternal and neonatal outcomes by developing algorithms that incorporate readily available EHR data.

Study design: Algorithms were developed for gestational hypertension (GHTN), pre-eclampsia (PreE), gestational diabetes mellitus (GDM) and were compared to ICD codes and chart review. Accuracy and sensitivity analyses were calculated with their respective 95% confidence limits for each of the comparisons between algorithms, ICD codes alone, and chart review.

Results: Sensitivity of GHTN ICD codes was 8.1% vs. 83.8% for the algorithm when compared to chart review. In comparison to chart review, sensitivity of ICD codes for PreE was 7.5% vs. 71.4% for the algorithm. GDM had similar sensitivity rates for both ICD codes and the algorithm.

Conclusion: Application of algorithms, validated by chart review, enhanced capture of several outcomes. Algorithms should be obligatory adjunct tools to the ICD codes for identification of outcomes of interest.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Diabetes, Gestational* / diagnosis
  • Electronic Health Records
  • Female
  • Humans
  • Hypertension, Pregnancy-Induced*
  • Infant, Newborn
  • International Classification of Diseases
  • Pre-Eclampsia* / diagnosis
  • Pregnancy
  • Pregnancy Outcome
  • Retrospective Studies