Prevalence and predictors of heart failure among patients on maintenance hemodialysis therapy at Muhimbili National Hospital in Tanzania: a cross-sectional study

Prevalence, clinical correlates and outcomes of cardiorenal anemia syndrome among patients with heart failure attending tertiary referral hospital in Dodoma, Tanzania: A protocol of a prospective observational study

BACKGROUND

Cardiorenal anemia syndrome (CRAS) is a common complication among patients with heart failure and is associated with poor clinical outcomes. However, there is a paucity of published data concerning CRAS, despite of significant increase in heart failure patients attending medical services in developing countries. This study aims to assess the prevalence, clinical correlates, and outcomes of CRAS among patients with heart failure attending the Benjamin Mkapa Hospital in Dodoma, Tanzania.

METHODOLOGY

A prospective observational study is ongoing at the Benjamin Mkapa Hospital in Dodoma, Tanzania. Currently, 92 patients have been recruited into this study and process is not yet completed. The socio-demographic data, clinical correlates, and prevalence of CRAS will be determined at baseline meanwhile, the outcomes of CRAS will be determined during a follow-up period of six months from the date of enrollment. CRAS is the primary outcome of the study. Data will be categorized into CRAS and non-CRAS during statistical analysis. Mean and standard deviation will be used for normally distributed continuous variables while median and interquartile range will be used for skewed data. Frequencies and percentages will summarize categorical variables. Clinical correlates and outcomes of CRAS will be analyzed and compared by using univariate and multivariate logistic regression and Cox proportional hazards models. A two-tailed p-value of less than 0.05 will indicate statistical significance.

Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method

Background: Heart failure (HF) is the main cause of mortality in hemodialysis (HD) patients. However, it is still a challenge for the prediction of HF in HD patients. Therefore, we aimed to establish and validate a prediction model to predict HF events in HD patients.

Methods: A total of 355 maintenance HD patients from two hospitals were included in this retrospective study. A total of 21 variables, including traditional demographic characteristics, medical history, and blood biochemical indicators, were used. Two classification models were established based on the extreme gradient boosting (XGBoost) algorithm and traditional linear logistic regression. The performance of the two models was evaluated based on calibration curves and area under the receiver operating characteristic curves (AUCs). Feature importance and SHapley Additive exPlanation (SHAP) were used to recognize risk factors from the variables. The Kaplan–Meier curve of each risk factor was constructed and compared with the log-rank test.

Results: Compared with the traditional linear logistic regression, the XGBoost model had better performance in accuracy (78.5 vs. 74.8%), sensitivity (79.6 vs. 75.6%), specificity (78.1 vs. 74.4%), and AUC (0.814 vs. 0.722). The feature importance and SHAP value of XGBoost indicated that age, hypertension, platelet count (PLT), C-reactive protein (CRP), and white blood cell count (WBC) were risk factors of HF. These results were further confirmed by Kaplan–Meier curves.

Conclusions: The HF prediction model based on XGBoost had a satisfactory performance in predicting HF events, which could prove to be a useful tool for the early prediction of HF in HD.