Journal article
Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study
European heart journal. Digital health, Vol.1(1), pp.51-61
11/01/2020
DOI: 10.1093/ehjdh/ztaa008
PMCID: PMC10087019
PMID: 37056293
Abstract
Aims Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.Methods and results In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and >= 400. Both CAC = 0 and CAC >= 400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC >= 400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (>= 15%) to predict CAC >= 400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.Conclusion ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.
Details
- Title: Subtitle
- Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study
- Creators
- Peter D. Farjo - West Virginia UniversityNaveena Yanamala - West Virginia UniversityNobuyuki Kagiyama - West Virginia UniversityHeenaben B. Patel - West Virginia UniversityGrace Casaclang-Verzosa - West Virginia UniversityNegin Nezarat - UCLA Medical CenterMatthew J. Budoff - Ronald Reagan UCLA Medical CenterPartho P. Sengupta - West Virginia University
- Resource Type
- Journal article
- Publication Details
- European heart journal. Digital health, Vol.1(1), pp.51-61
- Publisher
- Oxford Univ Press
- DOI
- 10.1093/ehjdh/ztaa008
- PMID
- 37056293
- PMCID
- PMC10087019
- ISSN
- 2634-3916
- eISSN
- 2634-3916
- Number of pages
- 11
- Grant note
- National Science Foundation; National Science Foundation (NSF)
- Language
- English
- Date published
- 11/01/2020
- Academic Unit
- Internal Medicine
- Record Identifier
- 9984691514102771
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