Why the study?
To develop and validate a clinical-biomarker nomogram integrating HBP, sST2, MLR, and routine clinical variables for identifying baseline HF status during hospitalization.
Does a multi-biomarker nomogram integrating HBP, sST2, MLR, and clinical variables improve the identification of baseline heart failure status in hospitalized patients compared to a clinical reference model?
Population
1279 hospitalized patients across two districts
Comparison
Multi-biomarker model vs prespecified clinical reference model
Design
Retrospective two-district derivation and validation study
Key result
A multi-biomarker nomogram integrating HBP, sST2, and MLR improved identification of baseline heart failure compared to a clinical reference model (NRI 1.140; 95% CI 1.001-1.277; P<0.001).
Authors
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Nomogram may aid hospitalized HF identification; hypothesis-generating and requires prospective validation before practice change.
Observational (n=1,279)
Yes
Does a multi-biomarker nomogram integrating HBP, sST2, MLR, and clinical variables improve the identification of baseline heart failure status in hospitalized patients compared to a clinical reference model?
Effect estimate: continuous net reclassification improvement 1.140 (95% CI 1.001-1.277)
p-value: p=<0.001
A novel multi-biomarker nomogram integrating HBP, sST2, MLR, and routine clinical variables provides good discrimination for identifying baseline heart failure in hospitalized patients.
Liu et al. (2026) conducted an observational in Heart failure (n=1,279). Multi-biomarker nomogram (HBP, sST2, MLR, and clinical variables) vs. Prespecified clinical reference model was evaluated on Model performance for identifying baseline heart failure (continuous net reclassification improvement 1.140, 95% CI 1.001-1.277, p=<0.001). A multi-biomarker nomogram integrating HBP, sST2, and MLR improved identification of baseline heart failure compared to a clinical reference model (NRI 1.140; 95% CI 1.001-1.277; P<0.001).