Incremental prognostic value of biomarkers in PARADIGM-HF
Related Experiment Video
Updated: Jul 30, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Kirsty McDowell1, Ross Campbell1, Joanne Simpson1
1BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.
Insights
Candidate biomarkers did not significantly improve heart failure risk prediction when added to existing clinical and laboratory models. Routine clinical data remains the most effective predictor of outcomes in heart failure patients.
Area of Science:
- Cardiology
- Biomarker Discovery
- Prognostic Modeling
Background:
- Accurate risk prediction in heart failure (HF) is crucial for guiding treatment decisions.
- The incremental value of novel biomarkers over established clinical and laboratory variables in HF risk stratification remains uncertain.
Purpose of the Study:
- To evaluate whether a panel of candidate biomarkers improves the PREDICT-HF prognostic model for predicting primary endpoint, cardiovascular mortality, and all-cause mortality in patients with heart failure.
Main Methods:
- 1559 participants from the PARADIGM-HF trial had aldosterone, cystatin C, hs-TnT, galectin-3, GDF-15, KIM-1, MMP-2, MMP-9, sST2, TIMP-1, and UACR measured.
- These biomarkers were tested individually and collectively to assess their impact on the PREDICT-HF model's performance.
Main Results:
- Individually, hs-TnT, GDF-15, cystatin C, and TIMP-1 were independently associated with outcomes.
- When added simultaneously, only hs-TnT remained an independent predictor for all endpoints; GDF-15 predicted the primary endpoint, and TIMP-1 predicted mortality.
- No significant improvements in discrimination or reclassification were observed when biomarkers were added to the model.
Conclusions:
- The candidate biomarkers evaluated did not provide meaningful improvements in outcome prediction beyond existing clinical, laboratory, and natriuretic peptide data in heart failure.
- Current comprehensive models incorporating routine variables appear sufficient for risk prediction in this patient cohort.
Aims:
It is uncertain how much candidate biomarkers improve risk prediction when added to comprehensive models including routinely collected clinical and laboratory variables in heart failure.
Methods And Results:
Aldosterone, cystatin C, high-sensitivity troponin T (hs-TnT), galectin-3, growth differentiation factor-15 (GDF-15), kidney injury molecule-1, matrix metalloproteinase-2 and -9, soluble suppression of tumourigenicity-2, tissue inhibitor of metalloproteinase-1 (TIMP-1) and urinary albumin to creatinine ratio were measured in 1559 of PARADIGM-HF participants. We tested whether these biomarkers, individually or collectively, improved the performance of the PREDICT-HF prognostic model, which includes clinical, routine laboratory, and natriuretic peptide data, for the primary endpoint and cardiovascular and all-cause mortality. The mean age of participants was 67.3 ± 9.9 years, 1254 (80.4%) were men and 1103 (71%) were in New York Heart Association class II. During a mean follow-up of 30.7 months, 300 patients experienced the primary outcome and 197 died. Added individually, only four biomarkers were independently associated with all outcomes: hs-TnT, GDF-15, cystatin C and TIMP-1. When all biomarkers were added simultaneously to the PREDICT-HF models, only hs-TnT remained an independent predictor of all three endpoints. GDF-15 also remained predictive of the primary endpoint; TIMP-1 was the only other predictor of both cardiovascular and all-cause mortality. Individually or in combination, these biomarkers did not lead to significant improvements in discrimination or reclassification.
Conclusions:
None of the biomarkers studied individually or collectively led to a meaningful improvement in the prediction of outcomes over what is provided by clinical, routine laboratory, and natriuretic peptide variables.
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