Key result
Novel multi-biomarker nomogram markedly improves heart failure identification over standard clinical variables.
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 in hospitalized patients?
Observational (n=1,279)
No
Does a multi-biomarker nomogram integrating HBP, sST2, MLR, and clinical variables improve the identification of baseline heart failure in hospitalized patients?
A novel nomogram integrating HBP, sST2, MLR, and routine clinical variables provides good discrimination for identifying baseline heart failure in hospitalized patients, offering a complementary tool to standard diagnostic pathways.
Answers are grounded in this paper and cite their sources.
May enhance baseline HF identification in hospitalized patients; hypothesis-generating and should not yet change practice pending prospective validation.
Purpose This study aimed to develop and validate a clinical-biomarker nomogram integrating heparin-binding protein (HBP), soluble suppression of tumorigenicity-2 (sST2), monocyte-to-lymphocyte ratio (MLR), and routine clinical variables for identifying baseline HF status during hospitalization. Methods This retrospective two-district study included 1279 hospitalized patients. A total of 737 patients from the Central District were randomly split into training and internal validation cohorts at a 7:3 ratio, and 542 patients from the South District served as the external validation cohort. Predictors were selected using LASSO logistic regression and multivariable logistic regression. Model performance was assessed by discrimination, calibration, clinical utility, and reclassification analyses. Training-derived thresholds were used for probability stratification, and a web-based calculator was developed. Results The final model included HBP, sST2, MLR, age, creatinine, aspartate aminotransferase, and albumin. The AUCs were 0.895 (95% CI 0.869–0.921), 0.901 (95% CI 0.860–0.942), and 0.816 (95% CI 0.782–0.851) in the training, internal validation, and external validation cohorts, respectively. The corresponding Brier scores were 0.132, 0.124, and 0.185. Compared with the prespecified clinical reference model, adding HBP, sST2, and MLR improved model performance, with a continuous net reclassification improvement of 1.140 (95% CI 1.001–1.277, P < 0.001) and an integrated discrimination improvement of 0.311 (95% CI 0.276–0.351, P < 0.001). Observed HF proportions increased progressively across low-, intermediate-, and high-probability groups in all cohorts. Conclusion This two-district retrospective study developed and externally validated a practical clinical-biomarker nomogram for estimating individualized prevalent HF probability during hospitalization. By integrating HBP, sST2, MLR, and routine clinical variables, this tool may support preliminary screening and probability-based triage using complementary non-imaging information. It should be used as an auxiliary tool rather than a replacement for guideline-based HF diagnosis, and further prospective multicenter validation is warranted.
No takes yet. Share an insight, caveat, or question.
Liu et al. (2026) conducted an observational in Heart failure (n=1,279). Multi-biomarker nomogram (HBP, sST2, MLR, and clinical variables) vs. Clinical reference model was evaluated on Improvement in model performance for identifying baseline heart failure (NRI 1.140, 95% CI 1.001-1.277, p=<0.001). A multi-biomarker nomogram integrating HBP, sST2, MLR, and clinical variables significantly improved the identification of baseline heart failure compared to a clinical reference model (NRI 1.140, P<0.001).
Synapse has enriched 10 related papers. Consider them for comparative context: