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

Bramania, Puneet Kishore; Ruggajo, Paschal; Fredrick, Francis

doi:10.1186/s43044-021-00223-z

Cited by 6 publications

(9 citation statements)

References 20 publications

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“…In our study, the proportion of hypertension in HF patients was significantly greater than that in the non-HF group, which is consistent with a recent study ( Bramania et al, 2021 ). The analysis of characteristics showed that hypertension was a risk factor for HF events, which is consistent with the results of a recent study ( Cozzolino et al, 2017 ).…”

Section: Discussionsupporting

confidence: 94%

“…Therefore, in this research, XGBoost, an integrated machine learning algorithm, was applied to identify the complex non-linear relationship between HF and clinical variables, as well as to evaluate the importance of the variables to the HF. Although traditional multivariable analysis methods have been applied for the identification of HF in HD patients ( Gedfew et al, 2020 ; Bramania et al, 2021 ), to our knowledge, this was the first machine learning model for the prediction of HF. Our results showed that the performance of the XGBoost model was better than the traditional logistic regression model in prediction of HF.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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“…Diabetes mellitus will be defined as either known patient with type 1 or 2; and for unknown diabetic status, is when random blood glucose by using blood from finger prick is ≥11.1mmol/L and glycated hemoglobin is ≥6.5% [15,34]…”

Section: Methodsmentioning

confidence: 99%

“…There is a significantly increasing burden of HF patients in Tanzania. However, there is unpublished data from hospital registries pertaining cardiorenal anemia syndrome [5,9,15]. This study is the first to be conducted in our settings and will determine the prevalence, clinical correlates and outcomes of cardiorenal anemia syndrome among the patients with heart failure irrespective of ejection fraction attending the Benjamin Mkapa hospital in Dodoma, Tanzania.…”

Section: Introductionmentioning

confidence: 99%

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

Edwin,

Alphonce,

Meremo

et al. 2023

Preprint

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BackgroundCardiorenal 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.MethodologyA prospective observational study is ongoing at the Benjamin Mkapa Hospital in Dodoma, Tanzania. Currently, the study has enrolled 68 patients. 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 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 models. A two-tailed p-value of less than 0.05 will indicate statistical significance.

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“…There is a significantly increasing burden of HF patients in Tanzania. In our settings, approximate 20 to 50 patients with heart failure attend daily in respective clinics; however, there is unpublished data from hospital registries pertaining to cardiorenal anemia syndrome [ 5 , 8 , 14 ]. This study is the first to be conducted in our settings and will determine the prevalence, clinical correlates and outcomes of cardiorenal anemia syndrome among the patients with heart failure irrespective of ejection fraction attending the Benjamin Mkapa hospital in Dodoma, Tanzania.…”

Section: Introductionmentioning

confidence: 99%

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

Edwin,

Alphonce,

Meremo

et al. 2024

PLoS ONE

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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.

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

Cited by 6 publications

References 20 publications

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

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

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

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

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