Journal article
Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features
Journal of the American College of Cardiology, Vol.76(8), pp.930-941
08/25/2020
DOI: 10.1016/j.jacc.2020.06.061
PMID: 32819467
Abstract
BACKGROUND Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise.
OBJECTIVES This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction.
METHODS A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability.
RESULTS Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e') measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R-2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated e' discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated e' allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively).
CONCLUSIONS A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients. (C) 2020 by the American College of Cardiology Foundation.
Details
- Title: Subtitle
- Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features
- Creators
- Nobuyuki Kagiyama - West Virginia UniversityMarco Piccirilli - West Virginia UniversityNaveena Yanamala - Carnegie Mellon UniversitySirish Shrestha - West Virginia UniversityPeter D. Farjo - West Virginia UniversityGrace Casaclang-Verzosa - West Virginia UniversityWadea M. Tarhuni - Windsor Cardiac Ctr, Windsor, ON, CanadaNegin Nezarat - UCLA Medical CenterMatthew J. Budoff - Harbor–UCLA Medical CenterJagat Narula - Icahn School of Medicine at Mount SinaiPartho P. Sengupta - West Virginia University
- Resource Type
- Journal article
- Publication Details
- Journal of the American College of Cardiology, Vol.76(8), pp.930-941
- Publisher
- Elsevier
- DOI
- 10.1016/j.jacc.2020.06.061
- PMID
- 32819467
- ISSN
- 0735-1097
- eISSN
- 1558-3597
- Number of pages
- 12
- Grant note
- HeartSciences 1920920 / National Science Foundation (NSF); National Research Foundation of Korea Hitachi Healthcare Heart Test Laboratories, Inc.
- Language
- English
- Date published
- 08/25/2020
- Academic Unit
- Internal Medicine
- Record Identifier
- 9984691517102771
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