ArticleAging clinical and experimental research2023
Predicting mortality and re-hospitalization for heart failure: a machine-learning and cluster analysis on frailty and comorbidity.
Article in Aging clinical and experimental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 15 citations in OpenAlex.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Clinical outcomes from robotic transabdominal preperitoneal inguinal hernia repair in patients under and over 70 years old: a single institution retrospective cohort study with a comprehensive systematic review on behalf of TROGSS - The Robotic Global Surgical Society.Aging clinical and experimental research · 2024Pooled it
- Automatic phenotyping of emergency department patients with incidental hepatic steatosis: A machine learning clustering analysis.The American journal of emergency medicine · 2026Article
- Explainable machine learning model for early prediction of ICU death in chronic heart failure with pulmonary infection.BMC medical informatics and decision making · 2026Article
- Malnutrition-induced frailty and risk of early rehospitalization in heart failure: A Survival analysis from a prospective cohort study.Pakistan journal of medical sciences · 2026Article
- Identifying frailty trajectories in older patients with acute myocardial infarction using structural entropy clustering: a prospective cohort study.BMC geriatrics · 2026Article
- Article
- Metabolomic characterization of frailty identifies subtype-specific management strategies.NPJ digital medicine · 2025Article
- Untangling the prognostic value of diastolic dysfunction, NT-proBNP, and frailty in older patients with preserved left ventricular ejection fraction without valvular disease.BMC geriatrics · 2025Observational
- Outcomes of early post-discharge cardio-geriatric care in frail patients after acute heart failure: a before-and-after study.BMC geriatrics · 2025Article
- Usefulness of the SARC-F questionnaire and the measurement of the hand grip strength in predicting short-term mortality in older patients hospitalized for acute heart failure.European geriatric medicine · 2024Article
- SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.Scientific reports · 2024Article
- Clustering of Cardiovascular Risk Factors and Heart Failure in Older Adults from the Brazilian Far North.Healthcare (Basel, Switzerland) · 2024Article
- Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine-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.
methodsIn 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.
resultsA 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])).
conclusionsIn 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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