The role of machine learning in the modern management of heart failure

Abstract

Introduction: The use of machine learning is exploding in all areas of healthcare, including the diagnosis and treatment of heart failure. Supervised machine learning can help predict the onset of heart failure, establish the diagnosis, and even predict decompensations. Conversely, unsupervised machine learning is chiefly used for phenotyping of the heart failure population. Several studies have identified distinctive groups of heart failure patients, but the widespread clinical implementation is still lacking. Aims: Our study aims to identify groups with similar characteristics among patients cared for HFrEF at the City Major Heart and Vascular Clinic of Semmelweis University using unsupervised machine learning and to describe the characteristic features of the resulting groups. We then examine the differences in outcome between the resulting groups. Methods: data from outpatients with reduced left ventricular ejection fraction heart failure were collected in a prospective registry. A total of 27 parameters included anamnestic data, laboratory tests, echocardiographic parameters and EQ5D quality of life questionnaire scores. The composite of hospitalization for heart failure and all-cause mortality was considered as the endpoint of the study. Spectral clustering was used to divide the population into three groups. The groups were plotted spatially using principal component analysis. Finally, we compared the groups in terms of parameters and endpoint occurrence. Results: Three characteristic groups were identified in the analysis of 259 patients. The first group consisted of 89 patients with ischemic etiology, more complaining, renal failure, and requiring duck diuretic therapy. The second group of 99 patients consisted of predominantly younger patients with atrial fibrillation, non-ischemic cardiomyopathy, dilated left ventricle, and a lower ejection fraction, almost exclusively on ARNI therapy. The third group of 71 patients included patients with the best ejection fraction, frequently taking ACE inhibitors and MRAs, and not requiring loop diuretics. Group 1 had significantly worse prognosis than group 2 (p=0.013) with a trend to worse prognosis compared to group 3. Conclusion: Our study identified three groups of patients with different characteristics based on a prospectively managed HFrEF registry. After validation on a larger patient cohort, our data may provide a basis for developing targeted treatment strategies.

Keywords

• heart failure
• hfref
• artificial intelligence
• machine learning
• spectral clustering