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

Journal of Perinatology volume 43, pages 10–14 (2023)Cite this article

Subjects

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.

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Data availability

Data are available are upon reasonable request. Deidentified data are available from Geisinger upon completion of legal and ethical processes.

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Funding

The research was supported and funded by Geisinger Clinic.

Author information

Authors and Affiliations

  1. Women’s Health Service Line, Division of Maternal-Fetal Medicine, Geisinger, 100N. Academy Avenue, Danville, PA, 17822, USA

    Kajal Angras, Victoria E. Boyd, Michael J. Paglia & A. Dhanya Mackeen

  2. Phenomic Analytics and Clinical Data Core, Geisinger, 100N. Academy Avenue, Danville, PA, 17822, USA

    Celia Gray

  3. Biostatistics Core, Department of Population Health Sciences, Geisinger, 100N. Academy Avenue, Danville, PA, 17822, USA

    Amanda J. Young

Authors
  1. Kajal Angras
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  2. Victoria E. Boyd
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  3. Celia Gray
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  4. Amanda J. Young
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  5. Michael J. Paglia
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  6. A. Dhanya Mackeen
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Contributions

The study idea was created by ADM and KA. The protocol was drafted by KA and edited by ADM and MJP. Data collection was performed by CG and edited by all authors. Chart review was completed by VB and KA. AY performed statistical analysis. Paper was prepared by KA and edited and approved by all authors.

Corresponding author

Correspondence to Kajal Angras.

Ethics declarations

Competing interests

Neither the study team nor any of its family members received specific grant from any funding agency in the public, commercial or not-for-profit sectors. The authors report no competing interests.

Ethics approval

This study was approved by the Geisinger IRB. IRB#2017-0520. Meets exemption under 45 CFR 46.104, where the research involves only information collection and analysis from health information for research purposes.

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Angras, K., Boyd, V.E., Gray, C. et al. Retrospective application of algorithms to improve identification of pregnancy outcomes from the electronic health record. J Perinatol 43, 10–14 (2023). https://doi.org/10.1038/s41372-022-01496-1

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