The Effect of Recurrent Heart Failure Hospitalizations on the Risk of Cardiovascular and all-Cause Mortality: a Systematic Review and Meta-Analysis.

INTRODUCTION: Heart failure (HF) is a significant worldwide concern due to its substantial impact on mortality rates and recurrent hospitalizations. The relationship between recurrent hospitalizations and mortality in individuals diagnosed with heart failure has been the subject of conflicting findings in previous studies. A meta-analysis was conducted to investigate the association between recurrent heart failure hospitalizations (HFHs) and mortality. METHODS: We conducted a systematic search across various online databases, such as PubMed, Embase, Web of Science, ProQuest, Scopus, Science Direct, and Google Scholar, to locate studies that examined the connection between recurrent HFHs and cardiovascular (CV) mortality as well as all-cause mortality until January 2023. To evaluate the heterogeneity among the studies, we employed I2 and Cochran's Q test. RESULTS: In total, 143,867 participants from seven studies were included in the analysis. Recurrent HFHs were found to be strongly associated with elevated risks of both cardiovascular (CV) mortality and all-cause mortality. The pooled hazard ratios (HRs) indicated a non-significant association for CV mortality (HR = 4.28, 95% CI: 0.86-7.71) but a significant association for all-cause mortality (HR = 2.76, 95% CI: 2.05-3.48). Subgroup analyses revealed a reduction in heterogeneity when stratified by factors such as quality score, sample size, hypertension comorbidity, number of recurrent HFHs, and follow-up time. A clear correlation was observed between the frequency of HFH and the mortality risk. Various subgroups, including those with diabetes, atrial fibrillation, and chronic kidney disease, showed significant associations between recurrent HFHs and all-cause mortality. Additionally, recurrent HFHs were significantly associated with CV mortality in subgroups such as heart failure with reduced ejection fraction (HFrEF), atrial fibrillation, and diabetes. CONCLUSION: This meta-analysis provides evidence of an association between recurrent HFH and elevated risk of both CV mortality and all-cause mortality. The findings consistently indicate that a higher frequency of HFH is strongly associated with an increased likelihood of mortality.

Predicting the risk of mortality and rehospitalization in heart failure patients: A retrospective cohort study by machine learning approach.

BACKGROUND: Heart failure (HF) is a global problem, affecting more than 26 million people worldwide. This study evaluated the performance of 10 machine learning (ML) algorithms and chose the best algorithm to predict mortality and readmission of HF patients by using The Fasa Registry on Systolic HF (FaRSH) database. HYPOTHESIS: ML algorithms may better identify patients at increased risk of HF readmission or death with demographic and clinical data. METHODS: Through comprehensive evaluation, the best-performing model was used for prediction. Finally, all the trained models were applied to the test data, which included 20% of the total data. For the final evaluation and comparison of the models, five metrics were used: accuracy, F1-score, sensitivity, specificity and Area Under Curve (AUC). RESULTS: Ten ML algorithms were evaluated. The CatBoost (CAT) algorithm uses a series of decision tree models to create a nonlinear model, and this CAT algorithm performed the best of the 10 models studied. According to the three final outcomes from this study, which involved 2488 participants, 366 (14.7%) of the patients were readmitted to the hospital, 97 (3.9%) of the patients died within 1 month of the follow-up, and 342 (13.7%) of the patients died within 1 year of the follow-up. The most significant variables to predict the events were length of stay in the hospital, hemoglobin level, and family history of MI. CONCLUSIONS: The ML-based risk stratification tool was able to assess the risk of 5-year all-cause mortality and readmission in patients with HF. ML could provide an explicit explanation of individualized risk prediction and give physicians an intuitive understanding of the influence of critical features in the model.