Preventing unnecessary imaging in patients suspect of coronary artery disease through machine learning of electronic health records

Malin Overmars, Bram van Es, Floor Groepenhoff, Mark de Groot, Gerard Pasterkamp, Hester M. den Ruijter, W.W. van Solinge, IE Höfer, Saskia Haitjema*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
7 Downloads (Pure)

Abstract

Aims
With the ageing European population, the incidence of coronary artery disease (CAD) is expected to rise. This will likely result in an increased imaging use. Symptom recognition can be complicated, as symptoms caused by CAD can be atypical, particularly in women. Early CAD exclusion may help to optimize use of diagnostic resources and thus improve the sustainability of the healthcare system.
To develop sex-stratified algorithms, trained on routinely available electronic health records (EHRs), raw electrocardiograms, and haematology data to exclude CAD in patients upfront.

Methods and results
We trained XGBoost algorithms on data from patients from the Utrecht Patient-Oriented Database, who underwent coronary computed tomography angiography (CCTA), and/or stress cardiac magnetic resonance (CMR) imaging, or stress single-photon emission computerized tomography (SPECT) in the UMC Utrecht. Outcomes were extracted from radiology reports. We aimed to maximize negative predictive value (NPV) to minimize the false negative risk with acceptable specificity. Of 6808 CCTA patients (31% female), 1029 females (48%) and 1908 males (45%) had no diagnosis of CAD. Of 3053 CMR/SPECT patients (45% female), 650 females (47%) and 881 males (48%) had no diagnosis of CAD. On the train and test set, the CCTA models achieved NPVs and specificities of 0.95 and 0.19 (females) and 0.96 and 0.09 (males). The CMR/SPECT models achieved NPVs and specificities of 0.75 and 0.041 (females) and 0.92 and 0.026 (males).

Conclusion
Coronary artery disease can be excluded from EHRs with high NPV. Our study demonstrates new possibilities to reduce unnecessary imaging in women and men suspected of CAD.
Original languageEnglish
Pages (from-to)11-19
Number of pages9
JournalEuropean Heart Journal - Digital Health
Volume3
Issue number1
Early online date7 Dec 2021
DOIs
Publication statusPublished - Mar 2022

Keywords

  • Cardiovascular diseases
  • Clinical decision support
  • EHR data
  • Machine learning
  • Sex differences

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