A machine-learning model using clinical and ECG features accurately predicted left ventricular diastolic dysfunction with an AUC of 0.88 in the internal test set and 0.94 in the external test set.
Observational (n=1,202)
Yes
Does a machine-learning model using clinical and ECG features accurately estimate myocardial relaxation and detect LV diastolic dysfunction compared to echocardiography?
A machine-learning model utilizing ECG and clinical features can accurately estimate myocardial relaxation and detect LV diastolic dysfunction, providing a potential cost-effective screening tool.
Effect estimate: AUC 0.88 and 0.94
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 R2 = 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.
“The echo is not a difficult test. It's the most proven usable tool that we have in cardiology because it's easy to reproduce, low cost, and noninvasive – so we have all that we want in medicine.”
Kagiyama et al. (Sat,) conducted a observational in Left ventricular diastolic dysfunction (n=1,202). Machine-learning models using clinical and ECG features vs. Echocardiography was evaluated on Prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC 0.88 and 0.94). A machine-learning model using clinical and ECG features accurately predicted left ventricular diastolic dysfunction with an AUC of 0.88 in the internal test set and 0.94 in the external test set.