Plasma Steroids as Biomarkers of Congestion and Predictors of Prognosis in Acute Heart Failure During the Vulnerable Phase.

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.