Key result
Machine learning heart sound analysis detects HF decompensation with 72% accuracy, outperforming expert cardiologists.
Why the study?
Clinical interpretation of heart sound changes to detect heart failure decompensation is subjective and unreliable, highlighting the need for objective early detection methods.
Does machine learning analysis of heart sounds improve the classification accuracy of decompensation episodes in chronic heart failure patients compared to cardiologist interpretation?
Cohort (n=37)
No
Does machine learning analysis of heart sounds improve the classification accuracy of decompensation episodes in chronic heart failure patients compared to cardiologist interpretation?
Absolute Event Rate: 72% vs 50%
Machine learning algorithms analyzing heart sounds can identify heart failure decompensation with higher accuracy than standard clinical interpretation by cardiologists.
Early detection of pre-symptomatic HF decompensation may reduce hospitalizations; leaves open need for validated strategies in prospective trials.
Decompensation episodes in chronic heart failure patients frequently result in unplanned outpatient or emergency room visits or even hospitalizations. Early detection of these episodes in their pre-symptomatic phase would likely enable the clinicians to manage this patient cohort with the appropriate modification of medical therapy which would in turn prevent the development of more severe heart failure decompensation thus avoiding the need for heart failure-related hospitalizations. Currently, heart failure worsening is recognized by the clinicians through characteristic changes of heart failure-related symptoms and signs, including the changes in heart sounds. The latter has proven to be largely unreliable as its interpretation is highly subjective and dependent on the clinicians' skills and preferences. Previous studies have indicated that the algorithms of artificial intelligence are promising in distinguishing the heart sounds of heart failure patients from those of healthy individuals. In this manuscript, we focus on the analysis of heart sounds of chronic heart failure patients in their decompensated and recompensated phase. The data was recorded on 37 patients using two types of electronic stethoscopes. Using a combination of machine learning approaches, we obtained up to 72% classification accuracy between the two phases, which is better than the accuracy of the interpretation by cardiologists, which reached 50%. Our results demonstrate that machine learning algorithms are promising in improving early detection of heart failure decompensation episodes.
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Susič et al. (2022) conducted a cohort in Chronic heart failure (n=37). Machine learning analysis of heart sounds vs. Cardiologist expert auscultation was evaluated on Classification accuracy of decompensated vs recompensated phases (95% CI 0.61-0.83). A logistic regression machine learning model analyzing heart sounds identified heart failure decompensation episodes with 72% accuracy, outperforming the 50% accuracy of cardiologist experts.
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