Key result
Machine learning using clinical and Holter data predicts SCD in Chagas patients with ~91% sensitivity.
Why the study?
Sudden cardiac death affects approximately 65% of Chagas heart disease patients, and its occurrence in this population needs to be better exploited.
Does a machine learning classification system based on clinical data and 24-hour ECG monitoring accurately predict sudden cardiac death in patients with Chagas heart disease?
Observational (n=315)
No
Does a machine learning classification system based on clinical data and 24-hour ECG monitoring accurately predict sudden cardiac death in patients with Chagas heart disease?
Effect estimate: AUC 80.55%
A machine learning model utilizing 13 clinical and ECG-Holter features can effectively predict the risk of sudden cardiac death in patients with Chagas heart disease.
May support SCD risk stratification in Chagas heart disease; leaves open prospective validation before clinical use.
About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.
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Cavalcante et al. (2023) conducted an observational in Chagas heart disease (n=315). Machine learning classification system (Naive Bayes) was evaluated on Sudden cardiac death risk classification (AUC 80.55%). A Naive Bayes machine learning model using clinical and 24-hour ECG-Holter data predicted sudden cardiac death risk in Chagas heart disease patients with 90.63% sensitivity and 80.55% AUC.
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