Key result
LIFE-HF model shows good discrimination but systematically underpredicts 1-year mortality risk in North American patients.
Why the study?
Accurate prognostication is central to HF decision-making, but the performance of the recently-developed LIFE-HF models had not been independently externally validated.
Does the LIFE-HF clinical prediction model accurately predict 1-year mortality and composite death/HF hospitalization in a North American HFrEF cohort?
Observational (n=801)
Yes
Does the LIFE-HF clinical prediction model accurately predict 1-year mortality and composite death/HF hospitalization in a North American HFrEF cohort?
The LIFE-HF mortality model showed good discrimination but systematically underpredicted 1-year risk in a North American HFrEF cohort.
Answers are grounded in this paper and cite their sources.
LIFE-HF mortality model may underestimate risk in North American HFrEF without recalibration; leaves open need for further validation before clinical adoption.
Aims Accurate prognostication is central to decision-making in heart failure (HF). The recently-developed LIFE-HF models offer promise, but their performance has not been independently externally validated. The aim of this study was to assess the external validity of the LIFE-HF models in a contemporary North American population. Methods We externally validated the LIFE-HF models for 1-year all-cause death and the composite of death or HF hospitalization using patient-level data from the GUIDE-IT trial. All LIFE-HF predictors were available; missing data were handled using multiple imputation. Predicted risks were calculated using original LIFE-HF model equations. We assessed model discrimination using time-dependent area under the receiver operating characteristic curve (AUROC), calibration (observed-to-expected (O/E) ratios, calibration slopes and curves) , overall prediction error, and net benefit using decision curve analysis. Results The validation cohort included 801 participants (median age 64 years, 69% male). The mortality model demonstrated good discrimination (AUROC 0.77, 95% confidence interval [CI]: 0.72-0.82), but underprediction (O/E 1.28, 95% CI: 1.01-1.54) and underfitting (calibration slope 1.58, 95% CI: 1.20-1.95). The composite model showed moderate discrimination (AUROC 0.69, 95% CI: 0.64-0.73), underprediction (O/E 1.40, 95% CI: 1.25-1.54), and overfitting (calibration slope 0.82, 95% CI: 0.63-1.00). Decision curve analysis showed net clinical benefit for the mortality model, but not the composite model, over a broad range of risk thresholds. Conclusions In a contemporary, high-risk North American HFrEF cohort, the LIFE-HF models showed good discrimination, but systematically underpredicted 1-year risk. The mortality model may support risk-informed decision-making, whereas the composite model requires further validation.
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Turgeon et al. (2026) conducted an observational in Heart failure with reduced ejection fraction (HFrEF) (n=801). LIFE-HF clinical prediction model vs. Observed outcomes was evaluated on 1-year all-cause death and the composite of death or HF hospitalization (AUROC 0.77, 95% CI 0.72-0.82). The LIFE-HF mortality model showed good discrimination for 1-year all-cause death (AUROC 0.77; 95% CI 0.72-0.82) but systematically underpredicted risk in a North American HFrEF cohort.
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