Background: Previous studies have failed to implement risk
stratification in patients with heart failure (HF) who are eligible for secondary
implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate
whether machine learning-based phenomapping using routinely available clinical
data can identify subgroups that differ in characteristics and prognoses.
Methods: A total of 389 patients with chronic HF implanted with an ICD
were included, and forty-four baseline variables were collected. Phenomapping was
performed using hierarchical k-means clustering based on factor analysis of mixed
data (FAMD). The utility of phenomapping was validated by comparing the baseline
features and outcomes of the first appropriate shock and all-cause death among
the phenogroups. Results: During a median follow-up of 2.7 years for
device interrogation and 5.1 years for survival status, 142 (36.5%) first
appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12
principal components extracted using the FAMD, explaining 60.5% of the total
variability, were left for phenomapping. Three mutually exclusive phenogroups
were identified. Phenogroup 1 comprised the oldest patients with ischemic
cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension,
and hyperlipidemia; and had the most favorable cardiac structure and function
among the phenogroups. Phenogroup 2 included the youngest patients, mostly those
with non-ischemic cardiomyopathy, who had intermediate heart dimensions and
function, and the fewest comorbidities. Phenogroup 3 had the worst HF
progression. Kaplan–Meier curves revealed significant differences in the first
appropriate shock (p = 0.002) and all-cause death (p
