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 (

BackgroundPrior studies have found an unexplained inverse or U-shaped relationship between body mass index (BMI) and mortality in heart failure (HF) patients. However, little is known about the independent effects of each body component, i.e., lean body mass (LBM) and fat mass (FM), on mortality.MethodsWe used data from the China Patient-centered Evaluative Assessment of Cardiac Events-Prospective Heart Failure Study. LBM and FM were calculated using equations developed from the National Health and Nutrition Examination Survey. LBM and FM index, calculated by dividing LBM or FM in kilograms by the square of height in meters, were used for analysis. We used restricted cubic spline and Cox model to examine the association of LBM and FM index with 1-year all-cause mortality.ResultsAmong 4,305 patients, median (interquartile range) age was 67 (57–76) years, 37.7% were women. During the 1-year follow-up, 691 (16.1%) patients died. After adjustments, LBM index was inversely associated with mortality in a linear way (P-overall association < 0.01; P-non-linearity = 0.52), but no association between FM index and mortality was observed (P-overall association = 0.19). Compared with patients in the 1st quartile of the LBM index, those in the 2nd, 3rd, and 4th quartiles had lower risk of death, with hazard ratio of 0.80 (95% CI 0.66–0.97), 0.65 (95% CI 0.52–0.83), and 0.61 (95% CI 0.45–0.82), respectively. In contrast, this association was not observed between FM index quartiles and mortality.ConclusionHigher LBM, not FM, was associated with lower 1-year mortality among HF patients.

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Introduction: Socioeconomic status (SES) has been shown to affect the survival of patients with heart failure (HF) in the developed countries. However, the association between SES with mortality has been scarcely investigated in China. Methods: We studied a national sample of 4717 patients admitted to 52 hospitals for HF who had SES record and were discharged alive from the China PEACE-Prospective Heart Failure Study. SES were obtained through questionnaires during hospitalization, and were measured by self-reported employment status (employed, retired, never work), education level (low educational attainment defined as primary school or below; intermediate educational attainment defined as junior high school; high educational attainment defined as senior high school or above) and partner status (having a partner, no partner). We assessed the association between SES and 1-year all-cause mortality using Cox models. Results: The median age of study population was 67 (57,76) years, 37.6% were women.17.6% of patients had a job, 72.5% retired and 9.9% never worked. The proportions of patients who had low, intermediate and high educational attainment were 43.0%, 29.0% and 28.0%,respectively. Additionally, 80.8% of patients had a partner. In unadjusted analysis, compared with patients who had a job, who had high educational attainment and who had a partner, respectively, currently no work status (retired group: HR 2.08, 95%CI 1.64 to 2.63; never work group: HR 2.23, 95%CI 1.78 to 3.22), low educational attainment (HR 1.52, 95%CI 1.28 to 1.81) and no partner (HR 1.31, 95%CI 1.12 to 1.54) were all associated to higher mortality. After adjusting for patient characteristics, medication use at discharge and mutually adjusting for socioeconomic status, education level and partner status were not associated with mortality, However, patients without work currently (retired group: adjusted HR 1.52, 95%CI 1.17 to 1.97; never work group: adjusted HR 1.60, 95%CI 1.15 to 2.22) were still more likely to die within 1 year. Conclusions: In a national Chinese cohort of patients hospitalized for HF, currently no work status was associated with higher 1-year mortality risk, while the education level and partner status were not correlated with mortality.

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Introduction: Although 30-day readmission has been used as a quality metric for treatment of patients hospitalized for heart failure (HF), the association between the timing of readmission with mortality after discharge has been scarcely investigated. Methods: We studied a national sample of 4875 patients admitted to 52 hospitals for HF between 2016 and 2018 who were discharged alive from the China PEACE-Heart Failure Study. The timing of readmission was defined as the timing of first readmission from all cause after discharge. Mortality analyses across the timing of readmission were performed using Kaplan-Meier curves and log-rank tests. The associations between the timing of readmission and 1-year all-cause mortality was determined using Cox models. Results: The median participant age was 67 (57-76) years, and 37.5% were female. The median duration from discharge to readmission was 88 (28-194) days. Kaplan-Meier analysis revealed 1-year mortality did not differ between patients admitted within 0-30 and 31-60 days, and between patients admitted within 61-90 and 91-180 days after discharge (Figure1A). Then we classified the timing of readmission as early (0-60 days), midrange (61-180 days) and late (181-365 days). The 1-year all-cause mortality for patients experiencing early, midrange and late readmission was 31.8%, 23.2% and 12.0%, respectively (Figure1B). After adjusting for patient characteristics and treatment during hospitalization, the HR for 1-year all-cause mortality was 2.89 (95% CI 2.22-3.77) for patients with early readmission, and 2.07 (95% CI 1.56-2.75) for patients with midrange readmission when compared with patients with late readmission, respectively. Conclusions: Earlier readmission are associated with increased risk for 1-year all-cause mortality. Not only 30-day readmission but also readmission within 60 days after discharge could be perceived as an alarming sign of higher risk of death in patients with hospitalized HF in China.