Why the study?
This study aimed to develop and evaluate a biomarker-informed, computable phenotype of the HF continuum aligned with the 2026 Universal Definition of Heart Failure and determine whether it identifies subgroups with distinct mortality risks.
Does a biomarker-informed computable phenotype of the heart failure continuum incorporating NT-proBNP identify subgroups with distinct mortality risks and improve prognostic discrimination compared to a conventional model in U.S. adults?
Cohort (n=11,477)
Does a biomarker-informed computable phenotype of the heart failure continuum incorporating NT-proBNP identify subgroups with distinct mortality risks and improve prognostic discrimination compared to a conventional model in U.S. adults?
Answers are grounded in this paper and cite their sources.
Operationalizing the 2026 Universal Definition of Heart Failure with NT-proBNP in a national cohort identifies a pre-HF (Stage B) population whose adjusted mortality risk is comparable to that of individuals with overt Stage C heart failure, although adding Stage B yields only marginal gains in overall model discrimination.
Objective: To develop and evaluate a biomarker-informed, computable phenotype of the heart failure (HF) continuum, aligned with 2026 AHA/ACC/ESC/WHF Expert Consensus, using NHANES and linked mortality data, and to determine whether this phenotype identifies subgroups with distinct mortality risks. Methods: We analyzed 11,477 U.S. adults aged ≥20 years from the 1999-2004 NHANES cycles who participated in the surplus-sera biomarker substudy and were linked to National Death Index follow-up. Participants were classified into four phenotypes according to the 2026 Universal Definition of Heart Failure: no apparent risk, Stage A (at risk), Stage B (elevated NT-proBNP without self-reported HF), and Stage C (self-reported HF). Survey-weighted Kaplan-Meier curves and nested, multiply imputed Cox proportional hazards models, with sequential adjustment for demographic, socioeconomic, and cardiometabolic covariates, were used to estimate associations with mortality. Pairwise phenotype comparisons, standardized adjusted survival estimates, and restricted mean survival times were also calculated. Incremental prognostic value was evaluated by comparing discrimination and reclassification metrics between the full phenotype and a conventional three-level model that excluded NT-proBNP. Results: Weighted prevalences were 16.9%, 70.0%, 10.7%, and 2.4% for no apparent risk, Stage A, Stage B, and Stage C, respectively. In fully adjusted models, the hazard ratios (HRs) relative to no apparent risk were 1.58 (95% CI, 1.08-2.30), 2.88 (95% CI, 2.00-4.16), and 2.56 (95% CI, 1.61-4.09), respectively. Stage B was associated with significantly higher mortality risk than Stage A (HR, 1.83; 95% CI, 1.62-2.07), whereas the difference between Stage C and Stage B was not statistically significant (HR, 0.89; 95% CI, 0.71-1.11). The association for Stage B attenuated less across sequentially adjusted models than that for Stage C. Adjusted 10-year mortality risks were 15.2% for Stage B and 14.2% for Stage C. Incorporating Stage B into the conventional model yielded only marginal improvements in discrimination (ΔC-statistic, 0.002; 95% CI, 0.001-0.002). Conclusion: A biomarker-informed phenotype of the HF continuum can be operationalized using national survey data and identifies a pre-HF subgroup with an adjusted mortality risk not significantly different from that of individuals with overt HF, despite only marginal gains in prognostic discrimination.
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Chen et al. (2026) conducted a cohort in Heart Failure Continuum (n=11,477). Stage B heart failure phenotype (elevated NT-proBNP without self-reported HF) vs. No apparent heart failure risk (and Stage A or Stage C) was evaluated on All-cause mortality (HR 2.88, 95% CI 2.00-4.16).
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