Aims: Acute heart failure (AHF) is a major cause of hospitalizations and death in the elderly. However, elderly patients are often underrepresented in randomized clinical trials. We analysed the impact of age on clinical outcomes and response to treatment in patients enrolled in Relaxin in Acute Heart Failure (RELAX-AHF-2), a study that included older patients than in previous AHF trials.
Methods and results: The RELAX-AHF-2 randomized patients admitted for AHF to infusion of serelaxin or placebo. We examined the association of pre-specified clinical outcomes and treatment effect according to age categories [(years): <65 (n = 1411), 65-74 (n = 1832), 75-79 (n = 1222), 80-84 (n = 1156) and ≥85 (n = 924)]. The mean age of the 6545 patients enrolled in RELAX-AHF-2 was 73.0 ± 11 years. The risk of all-cause and cardiovascular (CV) death (all p < 0.001) as well as the composite endpoint of CV death or heart failure/renal failure rehospitalization through 180 days (p = 0.002) and hospital discharge through day 60 (p = 0.013) were all directly associated with age categories. Age remained independently associated with outcomes after adjustment for clinical confounders and the results were consistent when age was analysed continuously. No clinically significant change in treatment effects of serelaxin was observed across age categories for the pre-specified endpoints (interaction p > 0.05).
Conclusion: Elderly patients are at higher risk of short- and long-term CV outcomes after a hospitalization for AHF. Further efforts are needed to improve CV outcomes in this population.
Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning-based clustering approach to identify the high-risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters.
Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high-risk DbCM phenotype was identified based on the incidence of HF on follow-up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community-based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup-3 (n = 324, 27% of the cohort) had significantly higher 5-year HF incidence than other phenogroups (12.1% vs. 4.6% [phenogroup 2] vs. 3.1% [phenogroup 1]) and was identified as the high-risk DbCM phenotype. The key echocardiographic predictors of high-risk DbCM phenotype were higher NT-proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16% and 29% of participants with DbCM, respectively. Participants with (vs. without) high-risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95% confidence interval] 1.61 [1.18–2.19] in CHS and 1.34 [1.08–1.65] in the UT Southwestern EHR cohort).
Machine learning-based techniques may identify 16% to 29% of individuals with diabetes as having a high-risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies.
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