葛怡兰
中国医学科学院阜外医院 国家心血管疾病临床医学研究中心
Background:Prior 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.Methods:We 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.Results:Among 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.Conclusion:Higher LBM, not FM, was associated with lower 1-year mortality among HF patients.
Frontiers in cardiovascular medicine 2022
AIMS:This study explored the association between socio-economic status (SES) and mortality among patients hospitalized for heart failure (HF) in China.METHODS AND RESULTS:We 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 (HR) 1.61, 95% confidence interval (CI) 1.21-2.14; middle income: HR 1.32, 95% CI 1.00-1.74], unemployment status (HR 1.43, 95% CI 1.10-1.86), low education (HR 1.25, 95% CI 1.03-1.53), and unpartnered status (HR 1.22, 95% CI 1.03-1.46) had a higher risk of death than patients with high income, who were employed, who had a high education level, and who had a partner, respectively. Compared with the patients without SERFs, those with 1, 2, 3, and 4 SERFs had 1.52-, 2.01-, 2.45-, and 3.20-fold increased risk of death, respectively. The addition of SES to fully adjusted model improved the mortality prediction, with increments in c statistic of 0.01 (P < 0.01).CONCLUSIONS:In a national Chinese cohort of patients hospitalized for HF, low income, unemployment status, low education, and unpartnered status were all associated with a higher risk of death 1 year following discharge. In addition, incorporating SES into a clinical-based model could better identify patients at risk for death. Tailored clinical interventions are needed to mitigate the excess risk experienced by those socio-economic deprived HF patients.
ESC heart failure 2022
