Background: Muscle wasting can be accelerated by chronic diseases such as heart failure and is one of the major causes of disability, morbidity, and mortality in this population. We aimed to investigate the incidence of muscle wasting and its associated factors in dilated cardiomyopathy patients younger than 55 years of age.Methods: Between April 2014 and December 2015, all symptomatic patients with a diagnosis of non‐ischaemic dilated cardiomyopathy who were referred to heart failure clinic were included in our study.Dual energy X‐ray absorptiometry was used to evaluate body composition and identify muscle wasting. Muscle mass was calculated as the ratio of an individual's total lean mass of legs and arms (also called appendicular skeletal muscle) to their squared height (kg/m²). The muscle mass values of less than 5.45 kg/m² for women and 7.26 kg/m² for men were considered low.Results: A total of 55 patients (32 male) were included. The mean (standard deviation) of age was 37.3 (10.1) years, and the mean of left ventricular ejection fraction was 21.4%. Most of the patients were in the New York Heart Association classes of II and II–III. Twenty‐six patients (47.3%) met criteria for muscle wasting. Patients with muscle wasting had lower left ventricular ejection fraction, lower 6‐min walk distance, and higher New York Heart Association function class and hospitalization rate.Conclusions: We concluded that muscle wasting might be present in younger patients with heart failure, particularly in those who are in worse clinical condition.

This study intends to predict in-hospital and 6-month mortality, as well as 30-day and 90-day hospital readmission, using Machine Learning (ML) approach via conventional features. A total of 737 patients remained after applying the exclusion criteria to 1101 heart failure patients. Thirty-four conventional features were collected for each patient. First, the data were divided into train and test cohorts with a 70–30% ratio. Then train data were normalized using the Z-score method, and its mean and standard deviation were applied to the test data. Subsequently, Boruta, RFE, and MRMR feature selection methods were utilized to select more important features in the training set. In the next step, eight ML approaches were used for modeling. Next, hyperparameters were optimized using tenfold cross-validation and grid search in the train dataset. All model development steps (normalization, feature selection, and hyperparameter optimization) were performed on a train set without touching the hold-out test set. Then, bootstrapping was done 1000 times on the hold-out test data. Finally, the obtained results were evaluated using four metrics: area under the ROC curve (AUC), accuracy (ACC), specificity (SPE), and sensitivity (SEN). The RFE-LR (AUC: 0.91, ACC: 0.84, SPE: 0.84, SEN: 0.83) and Boruta-LR (AUC: 0.90, ACC: 0.85, SPE: 0.85, SEN: 0.83) models generated the best results in terms of in-hospital mortality. In terms of 30-day rehospitalization, Boruta-SVM (AUC: 0.73, ACC: 0.81, SPE: 0.85, SEN: 0.50) and MRMR-LR (AUC: 0.71, ACC: 0.68, SPE: 0.69, SEN: 0.63) models performed the best. The best model for 3-month rehospitalization was MRMR-KNN (AUC: 0.60, ACC: 0.63, SPE: 0.66, SEN: 0.53) and regarding 6-month mortality, the MRMR-LR (AUC: 0.61, ACC: 0.63, SPE: 0.44, SEN: 0.66) and MRMR-NB (AUC: 0.59, ACC: 0.61, SPE: 0.48, SEN: 0.63) models outperformed the others. Reliable models were developed in 30-day rehospitalization and in-hospital mortality using conventional features and ML techniques. Such models can effectively personalize treatment, decision-making, and wiser budget allocation. Obtained results in 3-month rehospitalization and 6-month mortality endpoints were not astonishing and further experiments with additional information are needed to fetch promising results in these endpoints.