Key result
Lower socioeconomic status is linked to ~2.4-fold higher mortality after decompensated heart failure.
Why the study?
The long-term prognostic impact of social determinants of health in patients with decompensated heart failure treated with standardized protocols in private tertiary hospitals remains poorly characterized, especially in highly unequal Brazilian urban settings.
Do social determinants of health (hospital socioeconomic profile and ethnicity) increase all-cause mortality more than conventional clinical variables in patients hospitalized with decompensated heart failure?
Cohort (n=1,023)
Yes
Do social determinants of health (hospital socioeconomic profile and ethnicity) increase all-cause mortality more than conventional clinical variables in patients hospitalized with decompensated heart failure?
In patients hospitalized for decompensated heart failure receiving standardized care, social determinants of health—specifically hospital socioeconomic context and race/ethnicity—are stronger predictors of long-term mortality than traditional clinical variables.
Background Decompensated heart failure (DHF) carries high short-term lethality, but the long-term prognostic impact of social determinants of health (SDH) in patients treated with standardized protocols in private tertiary hospitals remains poorly characterized, particularly in highly unequal Brazilian urban settings. Purpose To quantify the association of SDH with in-hospital and long-term all-cause mortality after DHF hospitalization, and compare their prognostic weight against conventional clinical variables using advanced survival modeling. Methods Retrospective analysis of a prospective cohort of 1.023 consecutive patients admitted with DHF (2011–2021) at two private tertiary centers serving contrasting socioeconomic profiles. Patients received standardized HF protocols. Primary outcome was all-cause mortality (in-hospital + post-discharge) over median 6.5-year follow-up (IQR 3.8–9.1). We used Kaplan–Meier curves stratified by hospital unit (Figure 1), survival trees identifying high-risk phenotypes (Figure 2), and multivariable Cox regression with Elastic Net variable selection to compare SDH (hospital unit, ethnicity) versus clinical predictors. Results Mean age was 76.3±13.4 years; 53.9% men. In-hospital mortality: 10.8%; post-discharge: 64.1%. Kaplan–Meier curves showed markedly inferior survival at the hospital serving lower socioeconomic populations (Figure 1). In the final Cox model, admission to this hospital (HR 2.43, 95% CI 2.06–2.86), Brown/Black ethnicity (HR 1.44, 95% CI 1.12–1.86), older age (HR 1.02/year, 95% CI 1.02–1.03), prior diabetes (HR 1.18, 95% CI 1.01–1.38), and prior HF (HR 1.32, 95% CI 1.10–1.59) independently predicted mortality; higher LVEF and systolic dysfunction (vs. preserved) were protective. Survival trees identified high-risk phenotypes combining hospital unit, age >79 years, and prior arrhythmias (Figure 2), with SDH hazards exceeding classical predictors. Conclusions In this Brazilian urban DHF cohort under standardized care, SDH—particularly hospital unit reflecting intra-urban inequality and race/skin color—were the strongest long-term mortality predictors, outperforming traditional clinical variables. Integrating SDH into risk models could refine post-discharge stratification and promote equitable cardiovascular careFigure 1Figure 2
No takes yet. Share an insight, caveat, or question.
Dutra et al. (2026) conducted a cohort in Decompensated heart failure (n=1,023). Social determinants of health (lower socioeconomic profile hospital unit and Brown/Black ethnicity) vs. Higher socioeconomic profile hospital unit, non-Brown/Black ethnicity, and conventional clinical predictors was evaluated on All-cause mortality (in-hospital and post-discharge) (HR 2.43, 95% CI 2.06-2.86). Social determinants of health—specifically lower-SES hospital admission (HR 2.43; 95% CI 2.06-2.86) and Brown/Black ethnicity (HR 1.44; 95% CI 1.12-1.86)—strongly predicted mortality after DHF.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: