Reviews in cardiovascular medicine · 2026
Plasma Steroids as Biomarkers of Congestion and Predictors of Prognosis in Acute Heart Failure During the Vulnerable Phase.
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Fan Yangkai, Cui Jing, Ren Lu, Tan Xin, Chen Weiyao, Wang Yuan
Аннотация
BACKGROUND: Patients with heart failure (HF) are at high risk of mortality and readmission during the 90-day "vulnerable phase" post-discharge, often because of persistent congestion. However, tools effective for assessing risk are lacking. Thus, this study aimed to develop a steroid-based congestion score from the plasma steroid profile and determine the associated prognostic value during the vulnerable phase in patients with acute HF.
METHODS: Plasma levels of 18 steroids were measured by liquid chromatography-tandem mass spectrometry in patients from the Registry Study of Biomarkers in Heart Failure. Patients were divided into a discovery cohort for biomarker screening and model development (n = 691), a cohort for validation of prognostic value (validation cohort 1, n = 267), and a cohort with paired discharge and 90-day plasma samples available for assessment of dynamic changes in biomarkers (validation cohort 2, n = 46). Steroids independently associated with the clinical congestion score (CCS) were identified by correlation analysis and multivariable logistic regression and were used to construct the "steroid-based" congestion score (SCS). The 90-day prognostic value of the SCS was assessed using Cox regression, and the incremental predictive ability of the SCS was quantified using integrated discrimination improvement.
RESULTS: The SCS included cortisol, dehydroepiandrosterone sulfate, and pregnenolone. In the discovery cohort, an SCS ≥-0.563 was associated with a 2.02-fold increase in the risk of the 90-day composite endpoint of all-cause death and HF readmission (95% confidence interval, 1.42-2.88). Adding the SCS to clinical, B-type natriuretic peptide, and clinical congestion score model increased the integrated discrimination improvement by 1.2%, 0.8%, and 1.0%, respectively (all p < 0.05). The prognostic value of the SCS for 90-day and long-term outcomes was confirmed in validation cohort 1. In validation cohort 2, a higher baseline SCS was associated with higher 90-day congestion rates, and SCS dynamics paralleled congestion progression.
CONCLUSIONS: The SCS independently predicts adverse outcomes during the vulnerable phase in patients with HF, offering incremental prognostic value beyond traditional indicators and potentially supporting dynamic monitoring for individualized management.
METHODS: Plasma levels of 18 steroids were measured by liquid chromatography-tandem mass spectrometry in patients from the Registry Study of Biomarkers in Heart Failure. Patients were divided into a discovery cohort for biomarker screening and model development (n = 691), a cohort for validation of prognostic value (validation cohort 1, n = 267), and a cohort with paired discharge and 90-day plasma samples available for assessment of dynamic changes in biomarkers (validation cohort 2, n = 46). Steroids independently associated with the clinical congestion score (CCS) were identified by correlation analysis and multivariable logistic regression and were used to construct the "steroid-based" congestion score (SCS). The 90-day prognostic value of the SCS was assessed using Cox regression, and the incremental predictive ability of the SCS was quantified using integrated discrimination improvement.
RESULTS: The SCS included cortisol, dehydroepiandrosterone sulfate, and pregnenolone. In the discovery cohort, an SCS ≥-0.563 was associated with a 2.02-fold increase in the risk of the 90-day composite endpoint of all-cause death and HF readmission (95% confidence interval, 1.42-2.88). Adding the SCS to clinical, B-type natriuretic peptide, and clinical congestion score model increased the integrated discrimination improvement by 1.2%, 0.8%, and 1.0%, respectively (all p < 0.05). The prognostic value of the SCS for 90-day and long-term outcomes was confirmed in validation cohort 1. In validation cohort 2, a higher baseline SCS was associated with higher 90-day congestion rates, and SCS dynamics paralleled congestion progression.
CONCLUSIONS: The SCS independently predicts adverse outcomes during the vulnerable phase in patients with HF, offering incremental prognostic value beyond traditional indicators and potentially supporting dynamic monitoring for individualized management.