What does this research mean for the field?

Existing heart failure risk scores and machine learning models perform poorly in predicting 1-year mortality for real-world patients with severe heart failure, particularly underestimating risk in those at the highest risk. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 20, 2025European Journal of Heart FailureOpen Access

Predicting Survival in Patients with Severe Heart Failure: Risk Score Validation in the HELP-HF Cohort

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Why the study?

Accurate selection of patients with severe HF who might benefit from advanced therapies is crucial, prompting evaluation of available mortality risk scores.

Do available risk scores and machine learning models accurately predict 1-year mortality in patients with severe heart failure?

Structured PICO

Do available risk scores and machine learning models accurately predict 1-year mortality in patients with severe heart failure?

Population

1149 patients with severe heart failure enrolled in the HELP-HF cohort

Intervention

Risk scores (MAGGIC, 3-CHF, ADHF/NT-proBNP, GWTG-HF, HFA-ESC definition of advanced HF, I NEED HELP markers, frailty index, INTERMACS profile) and machine learning (ML)-based models

Outcome

1-year mortalityhard clinical

Most available risk scores and machine learning models perform poorly in predicting 1-year mortality in real-world patients with severe heart failure, particularly in those at highest risk.

Implication

Risk scores warrant caution in severe HF; leaves open need for refined models validated in high-risk cohorts.

Abstract

AIMS: Accurate selection of patients with severe heart failure (HF) who might benefit from advanced therapies is crucial. The present study investigates the performance of the available risk scores aimed at predicting the risk of mortality in patients with severe HF. METHODS AND RESULTS: The risk of 1-year mortality was estimated in patients with severe HF enrolled in the HELP-HF cohort according to the MAGGIC, 3-CHF, ADHF/NT-proBNP, and GWTG-HF risk scores, the number of criteria of the 2018 HFA-ESC definition of advanced HF, I NEED HELP markers, domains fulfilled of the 2019 HFA-ESC definition of frailty, the frailty index, and the INTERMACS profile. In addition, we tested the performance of different machine learning (ML)-based models to predict 1-year mortality. At 1-year follow-up, 265 patients (23.1%) died. The prognostic accuracy, tested in the subgroup of patients with completeness of all data regarding the variables included in the scores (497/1149 patients), resulted moderate for MAGGIC, GWTG-HF, and ADHF/NT-proBNP scores (area under the curve [AUC] ≥0.70) and only poor for the other tools. All the scores lost accuracy in estimating the rate of 1-year mortality in patients at the highest risk. Support vector machine-based model had the best AUC among ML-based models, slightly outperforming most of the tested risk scores. CONCLUSION: Most of the scores used to predict the risk of mortality in HF performed poorly in real-world patients with severe HF and provided inaccurate estimate of the risk of 1-year mortality in patients at the highest risk. ML-based models did not significantly outperform the currently available risk scores and their use must be validated in large cohort of patients.