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

Authors Gedfew M , Ayenew T , Mengst B , Yirga T , Zelalem M, Worku Y, Liknaw T

Received 25 August 2020

Accepted for publication 4 November 2020

Published 1 December 2020 Volume 2020:11 Pages 65—79

DOI https://doi.org/10.2147/RRCC.S274942

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Richard Kones



Mihretie Gedfew,1 Temesgen Ayenew,1 Belayneh Mengst,1 Tadesse Yirga,1 Mengistu Zelalem,1 Yichalem Worku,1 Tilksew Liknaw2

1College of Health Science, Debre Markos University, Debre Markos, Ethiopia; 2College of Health Science, Salale University, Salale, Ethiopia

Correspondence: Mihretie Gedfew Tel +251 925193433
Email [email protected]

Background: Heart failure is the cumulative and progressive result of conditions that cause structural defects and functional abnormalities in the heart. It is affects at least 26 million people worldwide and is increasing in prevalence especially among hemodialysis patients with severe renal failure.
Objective: To assess the incidence and predictors of congestive heart failure among hemodialysis patients at Felege Hiote Referral Hospital, Northwest Ethiopia.
Methods: This institutionally based retrospective cohort study was undertaken among 205 hemodialysis patients of Felege Hiote Referral Hospital from January 1, 2016 to February 29, 2020. All eligible hemodialysis patients who fulfilled the inclusion criteria were included in the study. Data were entered using Epi-data Version 4.1 and analyzed using STATA Version 14. The survival time of hemodialysis patients was estimated using the Kaplan–Meier survival curve, and the survival time between different categorical variables was compared using the log rank test. Both bivariable and multivariable Cox-proportional hazard regression models were fitted to identify independent predictors of congestive heart failure among hemodialysis patients.
Results: Among a cohort of 205 hemodialysis patients at Felege Hiote Referral Hospital, 12 (5.9%) developed congestive heart failure during the follow-up time. The overall congestive heart failure incidence rate was 2.9 per 100 person-years (PY) with 95% CI. The total time allotted to follow up the study participants was 4968 PY. Using multivariable Cox-regression analysis, we found that male sex, rural residence, no formal education, low body mass index (< 18.5), presence of comorbidity, and anemia during dialysis initiation significantly increased the risk of heart failure.
Conclusion: In this study, we found a high rate of congestive heart failure among hemodialysis patients. Factors significantly linked with increased risk of heart failure included male sex, rural residence, no education, low body mass index (< 18.5), presence of comorbidity, and anemia during dialysis initiation. Early screening and treatment for heart failure are highly recommended at hemodialysis follow-up for patients with the above risk factors.

Keywords: incidence, heart failure, predictors of congestive heart failure

Background

Heart failure (HF) is the cumulative and progressive result of conditions that cause structural defects and functional abnormalities in the heart. It is a clinical syndrome characterized by dyspnea, fatigue, and clinical signs of congestion leading to frequent hospitalizations, poor quality of life, and shortened life expectancy.1 It is a life-threatening syndrome with substantial morbidity and mortality and its burden to patients, their careers, and health systems is high.2 HF is a global phenomenon affecting at least 26 million people worldwide and is increasing in prevalence. The resulting health expenditures are considerable and increase dramatically with renal failure patients. Despite the significant advances in therapies and prevention, mortality and morbidity are still high and quality of life is poor among renal patients.3

A variety of strategies has been proposed to support patient self-management, such as early and targeted screening of patients to identify and resolve capacity deficits (limited literacy, cognitive and physical impairment) and psychosocial obstacles (depression and anxiety) that may undermine self-care. As a result self-management engagement training involves an assessment of patient needs, motivation and capacity, shared decision making and agreement on a management plan to support patient self-care to improve patient outcomes in chronic disease settings such as chronic kidney disease (CKD).4

The disease burden and health deterioration undermine adherence, so although self-management behaviors are commonly recommended in congestive heart failure (CHF) patients, 60% of patients do not adhere to their medication regimens as prescribed and up to 80% do not adhere to lifestyle recommendations.5 The disease prevalence is between 2–3% of the population and 6.5 million people are hospitalized as a result of CHF annually, with 1–2% of the health-care budget being spent on this disease. Heart failure is a challenge for health-care teams and the effects on physical, psychological, social functions and daily living activities are high for patients. The treatment of CHF mainly focuses on self-care activities that have a major role in disease management, reducing re-hospitalization and improving quality of life, although self-care is not addressed yet.6,7 This study aimed to investigate the incidence and predictors of CHF among hemodialysis patients.

Methods

Study Area and Period

The study was conducted at Felege Hiote Referral Hospital in Amhara region from February 1 to May 30, 2020. Felege Hiwot Referral Hospital is in the capital city of Amhara Regional State, which is 565 km away from the capital city of Ethiopia, Addis Ababa. It was established in 1963 as the district hospital, and it was upgraded to a referral hospital in 1994. The hospital has Surgery, Medical, Pediatrics, Obstetrics and Gynecology, Psychiatry, Dental, Orthopedics and Oncology units with outpatient, inpatient and follow-up departments. The hospital has a total of 415 health professionals (Minyichil Birhanu, 2018).

Study Design

Retrospective cohort study design was used.

Study Population

The study population was all dialysis patients who were registered at Felege Hiote Referral Hospital dialysis center from January 1, 2016 to February 29, 2020.

Inclusion Criteria and Exclusion Criteria

All hemodialysis patients who were registered at Felege Hiote Referral Hospital dialysis centers from January 1, 2016 to February 29, 2020 were included in the study. However, patients with incomplete data, with lost medical records, patients who developed CHF at the initiation of hemodialysis or were transferred in were excluded from the study.

Study Variables

The dependent variable for this study was incidence of CHF among hemodialysis patients. The independent variables were: socio-demographic factors (age, sex, marital status, educational status, employment), personal behaviors (smoking, alcohol use and both smoking and alcohol use) and clinical characteristics (time since CKD diagnosed, time since first dialysis started, presence of comorbidity, BMI (kg/m2), re-hospitalizations (in last year), family history of CHD, heart rate, systolic blood pressure, diastolic blood pressure, history of kidney transplantation, medication at admission for CKD, hemoglobin, creatinine, HDL cholesterol, LDL cholesterol, BUN and triglycerides).

Operational Definition

Congestive Heart Failure

Hemodialysis patients who were recorded as having CHF among hemodialytic patients.8

Dialytic Congestive Heart Failure (DCHF)

Hemodialysis patients who were diagnosed for CHF after the initiation of hemodialysis procedure.

Event

Hemodialysis patients who developed CHF within the study period.

Censored

Hemodialysis patients who did not develop CHF because of death, being transferred out or loss to follow up before CHF development.

Transfer Out

Hemodialysis patients who moved by taking their full medical records before completion of the study from the hospital to other health institutions for care and treatment.

Transfer in

Hemodialysis patients who came from other health institutions to the hospital by transfer for care and management purposes.

Data Collection Procedures

A five-year institution-based retrospective follow-up study was conducted using chart review at Felege Hiote Referral Hospital dialysis care follow-up clinic among hemodialysis patients who had been registered from January 1, 2016 to February 29, 2020. All eligible hemodialysis patients were included in the study (census method) after ethical clearance was received from the Institutional Review Committee of the College of Health Sciences, Debre Markos University (Ref. Res/Com/ser/andPost gra/Coor/Off: 781/11/10) and verbal informed consent was obtained from the patients. The medical record number (MRN) of the patients was identified from electronic database and health management information system (HMIS) registry books that had been used for the routine care of patients from January 1, 2016 to February 29, 2020. Then by using the MRN of the hemodialysis patients their medical records were identified and their status was assessed for the development of CHF starting from the date of hemodialysis initiation (first follow-up visit) to the end of the study period using validated data collection checklists.

Data Processing and Analysis

Data collection checklist tools adapted from previous studies in Ethiopia were used for the data collection. We used Epi-data Version 4.1 for data entry and STATA Version 14 statistical software for data analysis. The necessary assumption of Cox-proportional hazard regression model was checked using the Schoenfeld residual test and the Log-Log plot. The hemodialysis cohort characteristics of continuous data were described in terms of central tendency (mean or median), dispersion (standard deviation or interquartile range) and in the frequency distribution for categorical data. Finally, the outcomes of hemodialysis patients were dichotomized into censored or event categories. The Kaplan–Meier survival curve was used to estimate survival time, and log rank test was used to compare the survival curves. Bivariable Cox proportional hazard regression model was fitted for each explanatory variable and those variables having p-value ≤0.25 in bivariate analysis were fitted into the multivariable Cox proportional hazard regression model. Hazard ratio with 95% confidence interval and p-values were used to measure the strength of association and to identify statistically significant predictors. In the multivariable analysis, variables having P-value <0.05 were considered as significant predictors of congestive heart failure.

Ethical Considerations

The study was conducted after getting ethical clearance from the research and ethical committee of the Health Science College of Debre Markos University. Permission was taken from Felege Hiote Referral Hospital. The information collected from the medical records was used only for research purposes and kept confidential. In addition to this in order to maintain confidentiality, study subjects are not identified by name.

Result

Socio Demographic Characteristics of Study Participants

In this retrospective cohort study, a total of 205 hemodialysis patients at Felege Hiote Referral Hospital from the period of January 1, 2016 to February 29, 2020 were included. From the total respondents more than half, 115 (56.1%) of them were males and 143 (69.8%) were rural residents. Regarding their occupation about 107 (52.2%) were merchants followed by farmers (46, 22.44%) (see Table 1).

Table 1 Baseline Socio Demographic and Behavioral Characteristics of Dialysis Patients at Felege Hiote Referral Hospital, Ethiopia .

Clinical Characteristics of Study Participants

This study revealed that about 20 (9.76%) of the study participants had comorbidities (40% COPD followed by 35% hypertension) and 16 (7.80%) had a history of hospitalization after the initiation of dialysis. In addition to this 19 (9.27%) participants had a family history of CHF and 124 (60.49%) of them were taking prescribed medication (58.4% diuretics followed by 27.2% beta blockers) at dialysis initiation (see Table 2).

Table 2 Baseline Clinical Characteristics of the Dialysis Patients at Felege Hiote Referral Hospital, Ethiopia

Incidence of CHF Among Dialysis Patients

The patients were followed for 4968 person years (PY). The mean, median and range of the follow-up time was found to be 2.1, 2 and 4 years (IQR=3) respectively. During the follow-up period, about 12 (5.9%) of the patients were CHF cases (events). The overall incidence rate ratio of CHF was found to be 2.9 per 100 PY with 95% CI. Among the 12 individuals reporting CHF 8 (66.7%) of them were males, 9 (75%) were from urban residences and about 5 (42%) of them were employers. Moreover, a relatively higher proportion of CHF, 6 (50%) was diagnosed among the age group of 18–35 years. In addition, the incidence rate was higher among patients with comorbidities (9; 75%) and who had a duration of 1–2 years (8; 66.7%) follow-up. In this study more than half (10; 83.3%) of the dialysis-congestive heart failure patients started medication during admission, commonly diuretics (58.4%) (Figure 1).

Figure 1 The overall Kaplan–Meier survival curve with 95% confidence interval showing the survival time of patients with CHF at Felege Hiote Referral Hospital, Northwest Ethiopia, 2020.

CHF Incidence Density

Two hundred and five study participants who were followed for different periods in 5 years produced 4968 PY of observation. Within the follow-up period, 12 patients were found to have post hemodialysis CHF. The overall CHF incidence density (ID) was 2.9 per 100 PY (12/418) and the mean survival time was 2 years (see Tables 3 and 4).

Table 3 Baseline Socio-Demographic, Clinical and Behavioral Characteristics of Dialysis Patients at Felege Hiote Referral Hospital from January 1, 2016 to February 29, 2020

Table 4 CHF Incidence Density Rate Stratified by Socio-Demographic, Clinical and Behavioral Characteristics of Dialysis Patients at Felege Hiote Referral Hospital from January 1, 2016 to February 29, 2020

Predictors of Time to CHF Occurrence Among Dialysis Patients

In the bivariable Cox-regression analysis, significant predictors with p-value ≤0.25 of CHF were included: place of residence, occupation, presence of comorbidity, BMI, history of hospitalization, education, age, history of alcohol anemia during dialysis initiation, and medication at admission. To determine the independent predictors of CHF a multivariable Cox-proportional hazard adjusted model was fitted after the proportional hazard assumptions were checked with global test = 0.94, log rank test for significantly associated variables at the multivariable analysis (sex = 0.04, place of residence = 0.03, education = 0.001, BMI = 0.001, presence of comorbidity = 0.02, anemia during dialysis initiation = 0.03) and by graphical assessment method for categorical variables. Finally, sex, place of residence, education, BMI, presence of comorbidity, and anemia during dialysis initiation time were predictors of CHF among dialysis patients. Accordingly, male hemodialysis patients have 1.5 times higher risk of developing CHF compared with female patients (incidence rate ratio (95% CI: 1.5 (1.8 −12.1), p= 0.04) (Figure 2).

Figure 2 The Kaplan–Meier survival curves comparing the CHF incidence probabilities of hemodialysis patients based on their sex.

In addition to sex, type of residence was significantly associated with CHF. Accordingly, patients from rural areas had 2.5 times higher risk of developing CHF compared with urban residents (95% CI: 2.5 (2 −14.4), p= 0.01) (Figure 3).

Figure 3 The Kaplan–Meier survival curves comparing the CHF incidence probabilities of hemodialysis patients based on their place residence.

Body mass index is another determining factor for CHF. Accordingly, underweight patients (BMI < 18.5) are 1.6 times more likely to develop CHF than those with normal body weight (BMI 18.5–25) (95% CI: 1.6 (8 −16), p = 002) (Figure 4).

Figure 4 The Kaplan–Meier survival curves comparing the CHF incidence probabilities of hemodialysis patients based on comorbidity status.

On the other hand, patients with comorbidity are 6.9 times more likely to develop CHF than those without (95% CI: 6.9 (2.1 −12), p = 0.02) (Figure 5).

Figure 5 The Kaplan–Meier survival curves comparing the CHF incidence probabilities of hemodialysis patients based on their anemic status at the time of hemodialysis initiation.

Moreover, hemodialysis patients with anemia at the time of hemodialysis initiation are 2.2 times more likely to develop CHF than those without (95% CI: 2.2 (2–11), p = 0.04) (Figure 6).

Figure 6 The Kaplan–Meier survival curves comparing the CHF incidence probabilities of hemodialysis patients based on body mass index.

Discussion

Despite numerous interventions made to prevent CHF, it remains a serious global public health concern, especially in low- and middle-income countries. Therefore, we conducted this retrospective cohort study to determine the incidence of CHF among dialysis patients at Felege Hiote Referral Hospital, Ethiopia. The overall incidence rate of CHF at Felege Hiote Referral Hospital was found to be 2.9 per 100 PY. This finding is lower than the results of studies conducted in America (7.9/100 PY, 22 per 1000 PY).9,10 However, this finding is higher than findings reported from Spain (0.17 to 0. 21)11 and America (0.38).12 The above variations between studies could be explained, in part, by the differences in sample size, study settings, follow-up period, and socio-demographic characteristics of study participants. In addition, geographic differences might explain in part the differences in age expectancy and prevalence of comorbidities (diabetes, CAD and other cardiovascular diseases), as could the different criteria for diagnosing and defining CHF.

In this cohort study, male sex is significantly associated with CHF. Accordingly, male hemodialysis patients are 1.5 times at higher risk of developing CHF compared with female patients (95% CI: 1.5 (1.8 −12.1), p = 0.04). This study finding is consistent with other findings reported from the UK,13 Australia,14 China,15 Canada16 and Nigeria17 which showed that male sex is a predictor for onset of CHF and its survival rate. On the other hand, patients who are rural residents are 2.5 times at higher risk of developing CHF compared with urban residents (95% CI: 2.5 (2 −14.4), p = 0.01). However, a study in Brazil reported that differences between urban and rural residency.18 The variations between studies could be explained, in part, by the differences in sample size, study settings, follow-up period, and socio-demographic characteristics as well as socio-economic characteristics of study participants.

Hemodialysis patients having comorbidity were 6.9 times more likely to develop CHF than those with no other diagnoses (95% CI: 6.9 (2.1 −12), p = 0.02. The finding of this study is consistent with studies conducted in America,12,19 Australia,14 New Zealand,20 Germany21 and Tanzania.12

Anemia during dialysis initiation was significantly associated with CHF. Accordingly, patients with anemia at the time of dialysis initiation are 2.2 times more likely to develop CHF than non-anemic patients (95% CI: 2.2 (2–11), p = 0.04). This is consistent with studies in the USA9 and Tanzania.22 In this study level of education is an important predictor for CHF. Accordingly patients who are unable to read and write were 1.9 times more likely to develop CHF than those who are educated to college level and above (95% CI: 1.9 (5–16.9), p = 0.04). It is already known that as levels of education increase so do patients’ awareness of their illness and self-management. A similar study in Ethiopia reported statistically significant positive associations between self-management and education.23

In addition, our study demonstrated that underweight patients (BMI <18.5) are 1.6 times more likely to develop CHF than those who have normal weight (BMI 18.5–25). This finding is in line with a systematic review in Asia,24 the USA25,26, Sweden25 and Germany.27 According to the above studies extremely low BMI may continue to pose greater hemodynamic stress on the heart that could be deleterious and compromise the pumping ability of the heart. This situation highly compromises the blood perfusion of the body and the heart itself and can cause heart failure. However, this study finding is inconsistent with some findings reported from Denmark28 and the USA.29 The variations between studies could be explained, in part, by the differences in sample size, study settings, follow-up period, and socio-demographic characteristics as well as socio-economic characteristics of study participants.

Limitations of the Study

This study has a number of limitations. Firstly, secondary data were used, consequently important variables could be missed. Furthermore, the impact of providers’ training, supplies, equipment, and hospital service contexts has not been explored.

Conclusion

In five years of a hemodialysis cohort, the overall incidence of CHF was high among patients at Felege Hiote Referral Hospital. Finally, male sex, place of residence, education (unable to read and write), BMI, presence of comorbidity and anemia during dialysis initiation time were predictors of CHF among hemodialysis patients.

Special attention should be given for male patients, rural residence, patients who cannot read and write, with low BMI (≤18.5), with comorbidities, and anemic during dialysis initiation time to reduce the risk of CHF incidence by improving modifiable risk factors. Furthermore, a prospective cohort study should be conducted to make clear relations between predictors and CHF incidence among hemodialysis patients.

Abbreviations

ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; CKD, chronic kidney disease; HF, heart failure; CHF, congestive heart failure; MICU, Medical Intensive Care Unit; NCD, non communicable disease.

Data Sharing Statement

The dataset will not be shared in order to protect the participants’ identities but it is available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

Ethical clearance was obtained from the Institutional Review Committee of the College of Health Sciences, Debre Markos University. Oral permission was obtained from the hospital administrations. To ensure confidentiality, all collected data were coded and locked in a separate room prior to the data entry process. After entering of data into the computer, all data were locked by password. Participant names were not included in the data collection format, and the data were not disclosed to any person other than principal investigators.

Acknowledgment

We would like to acknowledge Debre Markos University for the financial funding of this research. We extend our special thanks to all data collectors and supervisors.

Author Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

Funding

Thereis no funding to report.

Disclosure

The authors report no conflicts of interest for this work.

References

1. Adebayo SO, Olunuga T, Durodola A, et al. Heart failure: definition, classification, and pathophysiology – a mini-review. Nig J Cardiol. 2017;14(1):9. doi:10.4103/0189-7969.201913

2. Zannad F. Rising incidence of heart failure demands action. Lancet. 2018;391(10120):518–519. doi:10.1016/S0140-6736(17)32873-8

3. Savarese G, Lund LH. Global public health burden of heart failure. Cardiac Failure Review. 2017;3(1):7. doi:10.15420/cfr.2016:25:2

4. Kennedy A, Bower P, Reeves D, et al. Implementation of self management support for long term conditions in routine primary care settings: cluster randomised controlled trial. BMJ. 2013;346(may13 3):f2882. doi:10.1136/bmj.f2882

5. van der Wal MHL, Jaarsma T. Adherence in heart failure in the elderly: problem and possible solutions. Int J Cardiol. 2008;125(2):203–208. doi:10.1016/j.ijcard.2007.10.011

6. Sigurþórsson ÞM, Jónsdóttir S. Heilsufar Einstaklinga Með Alvarlega Geðsjúkdóma.

7. Manwere A, Saburi G, Charumbira A, Mukona D, Zvinavashe M. The Relationship Between Self-Care Practices and Readmissions Among Adults with Chronic Heart Failure. 2013.

8. Leger DY, Moreau S, Signol N, et al. Polypharmacy, potentially inappropriate medications and drug-drug interactions in geriatric patients with hematologic malignancy: observational single-center study of 122 patients. J Geriatr Oncol. 2018;9(1):60–67. doi:10.1016/j.jgo.2017.07.015

9. He J, Shlipak M, Anderson A, et al. Risk factors for heart failure in patients with chronic kidney disease: the CRIC (Chronic Renal Insufficiency Cohort) study. J Am Heart Assoc. 2017;6(5):e005336. doi:10.1161/JAHA.116.005336

10. Bansal N, Katz R, Robinson-Cohen C, et al. Absolute rates of heart failure, coronary heart disease, and stroke in chronic kidney disease: an analysis of 3 community-based cohort studies. JAMA cardiol. 2017;2(3):314–318. doi:10.1001/jamacardio.2016.4652

11. Romero-González G, Ravassa S, González O, et al. Burden and challenges of heart failure in patients with chronic kidney disease. A call to action. Nefrología. 2020;40(3):223–236.

12. Bhatti NK, Karimi Galougahi K, Paz Y, et al. Diagnosis and management of cardiovascular disease in advanced and end-stage renal disease. J Am Heart Assoc. 2016;5(8):e003648. doi:10.1161/JAHA.116.003648

13. Li K, Fu W, Bo Y, et al. Effect of albumin-globulin score and albumin to globulin ratio on survival in patients with heart failure: a retrospective cohort study in China. BMJ Open. 2018;8(7):e022960. doi:10.1136/bmjopen-2018-022960

14. Okonko DO, Mandal AKJ, Missouris CG, et al. Disordered iron homeostasis in chronic heart failure: prevalence, predictors, and relation to anemia, exercise capacity, and survival. J Am Coll Cardiol. 2011;58(12):1241–1251. doi:10.1016/j.jacc.2011.04.040

15. Liu X, Yu H, Pei J, et al. Clinical characteristics and long-term prognosis in patients with chronic heart failure and reduced ejection fraction in China. Heart Lung Circ. 2014;23(9):818–826. doi:10.1016/j.hlc.2014.02.022

16. Tran DTT, Tu JV, Dupuis J-Y, et al. Association of frailty and long-term survival in patients undergoing coronary artery bypass grafting. J Am Heart Assoc. 2018;7(15):e009882. doi:10.1161/JAHA.118.009882

17. Ogah OS, Stewart S, Falase AO, et al. Contemporary profile of acute heart failure in Southern Nigeria: data from the abeokuta heart failure clinical registry. JACC Heart Fail. 2014;2(3):250–259.

18. Santos PR, Arcanjo FPN. Distance between residence and the dialysis unit does not impact self-perceived outcomes in hemodialysis patients. BMC Res Notes. 2012;5(1):458. doi:10.1186/1756-0500-5-458

19. Banerjee D, Ma JZ, Collins AJ, et al. Long-term survival of incident hemodialysis patients who are hospitalized for congestive heart failure, pulmonary edema, or fluid overload. Clin J Am Soc Nephrol. 2007;2(6):1186–1190. doi:10.2215/CJN.01110307

20. Brunner–la Rocca HP, Buser PT, Schindler R, et al. Management of elderly patients with congestive heart failure—design of the Trial of Intensified versus standard Medical therapy in Elderly patients with Congestive Heart Failure (TIME-CHF). Am Heart J. 2006;151(5):949–955. doi:10.1016/j.ahj.2005.10.022

21. Corletto A, Fröhlich H, Täger T, et al. Beta blockers and chronic heart failure patients: prognostic impact of a dose targeted beta blocker therapy vs. heart rate targeted strategy. Clin Res Cardiol. 2018;107(11):1040–1049. doi:10.1007/s00392-018-1277-4

22. Chillo P, Mujuni E. Prevalence and predictors of left ventricular dysfunction among patients with chronic kidney disease attending Muhimbili National Hospital in Tanzania — a cross-sectional study. Res Rep Clin Cardiol. 2018;9:11–21. doi:10.2147/RRCC.S159472

23. Gela D, Mengistu D. Self-management and associated factors among patients with end-stage renal disease undergoing hemodialysis at health facilities in Addis Ababa, Ethiopia. Int J Nephrol Renovasc Dis. 2018;11:329. doi:10.2147/IJNRD.S184671

24. Liu J, Zeng X, Hong HG, Li Y, Fu P. The association between body mass index and mortality among Asian peritoneal dialysis patients: a meta-analysis. PLoS One. 2017;12:2.

25. Agarwal R. Body mass index-mortality paradox in hemodialysis: can it be explained by blood pressure? Hypertension. 2011;58(6):1014–1020. doi:10.1161/HYPERTENSIONAHA.111.180091

26. Lu JL, Kalantar-Zadeh K, Ma JZ, et al. Association of body mass index with outcomes in patients with CKD. J Am Soc Nephrol. 2014;25(9):2088–2096. doi:10.1681/ASN.2013070754

27. Zamora E, Lupón J, Urrutia A, et al. Does body mass index influence mortality in patients with heart failure? Rev Esp Cardiol (Engl Ed). 2007;60(11):1127–1134. doi:10.1016/S1885-5857(08)60042-0

28. Christensen HM, Schou M, Goetze JP, et al. Body mass index in chronic heart failure: association with biomarkers of neurohormonal activation, inflammation and endothelial dysfunction. BMC Cardiovasc Disord. 2013;13(1):80. doi:10.1186/1471-2261-13-80

29. Brainin P, Claggett B, Lewis EF, et al. Body mass index and B-lines on lung ultrasonography in chronic and acute heart failure. ESC Heart Fail. 2020;7(3):1201–1209. doi:10.1002/ehf2.12640

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