Key result
MARKER-HF machine learning model predicts 1-year mortality in HF patients with ~0.73 AUC.
Why the study?
Most risk prediction models are confined to specific medical conditions, limiting their application to general medical populations.
Can the MARKER-HF risk model predict 1-year mortality in a general medical population with and without heart failure?
Cohort (n=41,749)
No
Can the MARKER-HF risk model predict 1-year mortality in a general medical population with and without heart failure?
Effect estimate: AUC (95% CI 0.706-0.752 (HF); 0.760-0.780 (non-HF))
Absolute Event Rate: 0.729% vs 0.77%
The MARKER-HF machine learning model, originally developed for heart failure, accurately predicts 1-year mortality across a broad population of patients with and without heart failure.
May support MARKER-HF for mortality risk stratification in general populations; leaves open prospective validation before clinical adoption.
Background: Most risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations. Objectives: The MARKER-HF (Machine learning Assessment of RisK and EaRly mortality in Heart Failure) risk model was developed in heart failure (HF) patients. We assessed the ability of MARKER-HF to predict 1-year mortality in a large community-based hospital registry database including patients with and without HF. Methods: This study included 41,749 consecutive patients who underwent echocardiography in a tertiary referral hospital (4,640 patients with and 37,109 without HF). Patients without HF were further subdivided into those with (n = 22,946) and without cardiovascular disease (n = 14,163) and also into cohorts based on recent acute coronary syndrome or history of atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, hypertension, or malignancy. Results: The median age of the 41,749 patients was 65 years, and 56.2% were male. The receiver operated area under the curves for MARKER-HF prediction of 1-year mortality of patients with HF was 0.729 (95% CI: 0.706-0.752) and for patients without HF was 0.770 (95% CI: 0.760-0.780). MARKER-HF prediction of mortality was consistent across subgroups with and without cardiovascular disease and in patients diagnosed with acute coronary syndrome, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, or hypertension. Patients with malignancy demonstrated higher mortality at a given MARKER-HF score than did patients in the other groups. Conclusions: MARKER-HF predicts mortality for patients with HF as well as for patients suffering from a variety of diseases.
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Jang et al. (2023) conducted a cohort in Heart failure and general medical populations (n=41,749). MARKER-HF risk model was evaluated on 1-year mortality (AUC, 95% CI 0.706-0.752 (HF); 0.760-0.780 (non-HF)). The MARKER-HF machine learning model predicted 1-year mortality with an AUC of 0.729 (95% CI 0.706-0.752) in patients with heart failure and 0.770 (95% CI 0.760-0.780) in those without.
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