AimsThis study explored the association between socio-economic status (SES) and mortality among patients hospitalized for heart failure (HF) in China. Methods and resultsWe used data from the China Patient-centred Evaluative Assessment of Cardiac Events-Prospective Heart Failure Study (China PEACE 5p-HF Study), which enrolled patients hospitalized primarily for HF from 52 hospitals between 2016 and 2018. SES was measured using the income, employment status, educational attainment, and partner status. Individual socio-economic risk factor (SERF) scores were assigned based on the number of coexisting SERFs, including low income, unemployed status, low education, and unpartnered status. We assessed the effects of SES on 1 year all-cause mortality using Cox models. We used the Harrell c statistic to investigate whether SES added incremental prognostic information for mortality prediction. A total of 4725 patients were included in the analysis. The median (interquartile range) age was 67 (57-76) years; 37.6% were women. In risk-adjusted analyses, patients with low/middle income [low income: hazard ratio (

Background The patterns of depressive symptom change during the first month after discharge, as well as their prognostic implications, and predictors of persistent or new‐onset depressive symptoms are not well characterized. Methods and Results We included patients hospitalized for heart failure undergoing Patient Health Questionnaire‐2 before discharge and at 1 month after discharge in a multicenter prospective cohort. We characterized 4 patterns of change in depressive symptoms—persistent, new‐onset, remitted depressive symptoms, and no depressive symptom—and examined the associations between the 4 patterns and 1‐year clinical outcomes. We analyzed the factors associated with persistent or new‐onset depressive symptoms. A total of 4130 patients were included. Among 1175 (28.5%) symptomatic patients and 2955 (71.5%) symptom‐free patients before discharge, 817 (69.5%) had remission, and 366 (12.2%) had new‐onset depressive symptoms, respectively. Compared with no depressive symptom, persistent depressive symptoms were associated with an increased risk of cardiovascular death (hazard ratio [HR], 2.10 [95% CI, 1.59–2.79]) and heart failure rehospitalization (HR, 1.56 [95% CI, 1.30–1.87]); new‐onset depressive symptoms were associated with an increased risk of cardiovascular death (HR, 1.78 [95%CI, 1.32–2.40]) and heart failure rehospitalization (HR, 1.54 [95% CI, 1.29–1.83]). Remitted depressive symptoms were associated with a slightly increased risk of cardiovascular death but had no significant association with heart failure rehospitalization. Patients who were female or had poor socioeconomic status, stroke history, renal dysfunction, or poor health status had a higher risk of persistent or new‐onset depressive symptoms. Conclusions Sex, socioeconomic status, clinical characteristics, and health status help identify patients with high risks of depressive symptoms at 1 month after discharge. Dynamic capture of depressive symptom change during this period informs long‐term risk stratifications and targets patients who require psychological interventions and social support to improve clinical outcomes. Registration URL: https://www.clinicaltrials.gov ; Unique identifier (NCT02878811).

Aims Mortality risk assessment in patients with heart failure (HF) with preserved ejection fraction (HFpEF) presents a major challenge. We sought to construct a polygenic risk score (PRS) to accurately predict the mortality risk of HFpEF. Methods and results We first carried out a microarray analysis of 50 HFpEF patients who died and 50 matched controls who survived during 1-year follow-up for candidate gene selection. The HF-PRS was developed using the independent common (MAF > 0.05) genetic variants that showed significant associations with 1-year all-cause death (P < 0.05) in 1442 HFpEF patients. Internal cross-validation and subgroup analyses were performed to evaluate the discrimination ability of the HF-PRS. In 209 genes identified by microarray analysis, 69 independent variants (r < 0.1) were selected to develop the HF-PRS model. This model yielded the best discrimination capability for 1-year all-cause mortality with an area under the curve (AUC) of 0.852 (95% CI 0.827–0.877), which outperformed the clinical risk score consisting of 10 significant traditional risk factors for 1-year all-cause mortality (AUC 0.696, 95% CI 0.658–0.734, P = 4 × 10−11), with net reclassification improvement (NRI) of 0.741 (95% CI 0.605–0.877; P < 0.001) and integrated discrimination improvement (IDI) of 0.181 (95% CI 0.145–0.218; P < 0.001). Individuals in the medium and the highest tertile of the HF-PRS had nearly a five-fold (HR = 5.3, 95% CI 2.4–11.9; P = 5.6 × 10−5) and 30-fold (HR = 29.8, 95% CI 14.0–63.5; P = 1.4 × 10−18) increased risk of mortality compared to those in the lowest tertile, respectively. The discrimination ability of the HF-PRS was excellent in cross validation and throughout the subgroups regardless of comorbidities, gender, and patients with or without a history of heart failure. Conclusion The HF-PRS comprising 69 genetic variants provided an improvement of prognostic power over the contemporary risk scores and NT-proBNP in HFpEF patients.