&lt;p&gt;Incidence and Predictors of Congestive Heart Failure Among Hemodialysis Patients at Felege Hiote Referral Hospital, Northwest Ethiopia, 2020: Retrospective Cohort Study&lt;/p&gt;

Gedfew, Mihretie; Ayenew, Temesgen; Mengst, Belayneh; Akalu, Tadesse Yirga; Mengistu, Zelalem; Worku, Yichalem; Liknaw, Tiliksew

doi:10.2147/rrcc.s274942

Cited by 2 publications

(2 citation statements)

References 27 publications

(28 reference statements)

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“…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%

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: 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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“…Patients with specific chronic conditions are referred to the hospital's specialty chronic illness clinics for follow-up (18). FHCSH is a tertiary referral and teaching hospital with 400-bed and around 15 adult OPDs that serve over 7 million people in the surrounding area (19). UoGCSH is a tertiary teaching and referral hospital in Northwest Ethiopia that has over 450 inpatient beds and provides referral health services to over 5 million people.…”

Section: Study Area and Study Designmentioning

confidence: 99%

Time to Death and Its Determinant Factors Among Patients With Chronic Heart Failure in Northwest Ethiopia: A Retrospective Study at Selected Referral Hospitals

Moyehodie

Muluneh

Belay

et al. 2022

Front. Cardiovasc. Med.

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BackgroundHeart failure (HF) is a major health problem that affects patients and healthcare systems worldwide. It is the leading cause of morbidity and death and negatively impacts the quality of life, healthcare costs, and longevity. However, the causes of death were not well defined. This study aimed to identify the determinants of death among patients with HF in the Amhara Region, Northwest Ethiopia.MethodsA multicenter retrospective cohort study was conducted on 285 patients in the age group 15 years or older under follow-up from 1 January 2015 to 31 December 2019. Descriptive analyses were summarized using the Kaplan–Meier survival curve and the log-rank test. Then, the Cox-proportional hazard regression model was employed to estimate the hazard of death up to 5 years after they were admitted to the HF department to follow up on their treatment.ResultsOut of 285 patients with HF, 93(32.6%) of the respondents were dying within 5 years of follow-up. Anemia was the common comorbid disease (30.5%), and valvular heart disease was the most common etiology (33.7%) of chronic heart failure in this study. This study showed a significant mortality difference between hospitals. HF patients with hypertension [adjusted hazard ratio (AHR): 3.5076, 95% confidence interval (CI): 1.43, 8.60], anemia (AHR: 2.85, 95% 1.61, 5.03), pneumonia (AHR: 2.02, 95% 1.20, 3.39), chronic kidney disease (2.23, CI: 1.31, 3.77), and diabetes mellitus (AHR: 2.42, 95% CI: 1.43, 4.09) were at a higher risk of death. Moreover, patients with symptoms listed in the New York Heart Association Class (III and IV), Ischemic Heart Disease and unknown etiologies, men (AHR: 2.76, 95%:1.59, 4.78), and those with a high pulse rate (AHR: 1.02, 95%:1.00, 1.04) were at a higher risk of death.ConclusionThere was a mortality difference between hospitals. This study has revealed that HF patients with anemia, diabetes mellitus, pneumonia, hypertension, chronic kidney disease, HF etiologies, severe New York Heart Association Class (III and IV), men, and high pulse rate were the main factors associated with death. Health professionals could give more attention to patients whose pulse rate is high, men, and a patient who had comorbidities in the ward.

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<p>Incidence and Predictors of Congestive Heart Failure Among Hemodialysis Patients at Felege Hiote Referral Hospital, Northwest Ethiopia, 2020: Retrospective Cohort Study</p>

Cited by 2 publications

References 27 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

Time to Death and Its Determinant Factors Among Patients With Chronic Heart Failure in Northwest Ethiopia: A Retrospective Study at Selected Referral Hospitals

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