Key result
Very frail and comorbid older adults discharged after acute decompensated heart failure had a significantly higher risk of 6-month mortality or HF re-hospitalization compared to non-frail patients (HR 3.53).
Why the study?
Machine-learning techniques have been used to predict unfavorable outcomes in elderly patients with HF, but none had integrated assessments for frailty and comorbidity.
Do machine-learning-based phenogroups incorporating frailty, comorbidity, and BNP predict 6-month mortality or heart failure readmission in older patients discharged for acute decompensated heart failure?
Cohort (n=571)
No
Do machine-learning-based phenogroups incorporating frailty, comorbidity, and BNP predict 6-month mortality or heart failure readmission in older patients discharged for acute decompensated heart failure?
Hazard Ratio: 3.53 (95% CI 2.3–5.39)
Absolute Event Rate: 52.3% vs 17.8%
p-value: p=<0.001
Machine learning-based clustering using frailty, comorbidities, and BNP successfully stratifies 6-month risk of death or readmission in older patients with acute decompensated heart failure.
May aid post-discharge risk stratification in older ADHF patients; hypothesis-generating for ML phenogroups before prospective validation.
BACKGROUND: Machine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital. METHODS: In this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By k-means and hierarchical clustering, those predictors were utilized to phenomapping the cohort in four different clusters. RESULTS: A total of 571 patients were included in the study. Cluster analysis identified four different clusters according to frailty, burden of comorbidities and BNP. As compared with Cluster 4, we found an increased 6-month risk of poor outcomes patients in Cluster 1 (very frail and comorbid; HR 3.53 [95% CI 2.30-5.39]), Cluster 2 (pre-frail with low levels of BNP; HR 2.59 [95% CI 1.66-4.07], and in Cluster 3 (pre-frail and comorbid with high levels of BNP; HR 3.75 [95% CI 2.25-6.27])). CONCLUSIONS: In older patients discharged for ADHF, the cluster analysis identified four distinct phenotypes according to frailty degree, comorbidity, and BNP levels. Further studies are warranted to validate these phenogroups and to guide an appropriate selection of personalized, model of care.
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Okoye et al. (2023) conducted a cohort in Acute decompensated heart failure (n=571). Very frail and comorbid phenotype (Cluster 1) vs. Non-frail phenotype (Cluster 4) was evaluated on Composite of re-hospitalization for management of HF or all-cause death within 6 months (HR 3.53, 95% CI 2.30-5.39, p=<0.001). Very frail and comorbid older adults discharged after acute decompensated heart failure had a significantly higher risk of 6-month mortality or HF re-hospitalization compared to non-frail patients (HR 3.53).
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