• mortality [ Time Frame: 5 Years ]
  To identify predictors for mortality among End-stage renal disease patients undergoing maintenance hemodialysis at UDOM (University of Dodoma) health center and Benjamin Mkapa Hospital using by binary logistic regression.
• Survival [ Time Frame: 5 Years ]
  To analyze survival of End-stage renal disease undergoing maintenance hemodialysis at 1, 3 and 5 years, at UDOM health center and Benjamin Mkapa Hospital, using Kaplan-Meier survival curves.

• Infection incidence [ Time Frame: 5 Years ]
  To analyze incidences for common causes of infection among End-stage renal disease undergoing maintenance hemodialysis at UDOM health center and Benjamin Mkapa Hospital.
• Infection prevalence [ Time Frame: 5 Years ]
  To analyze prevalence of common causes of infection among End-stage renal disease undergoing maintenance hemodialysis at UDOM health center and Benjamin Mkapa Hospital.
• Time-to-infection survival [ Time Frame: 5 Years ]
  To analyze time-to-infection survival of common causes of infection among End-stage renal disease undergoing maintenance hemodialysis at UDOM health center and Benjamin Mkapa Hospital, by Kaplan-Maier survival curves.
• Incidences of rehospitalization [ Time Frame: 5 Years ]
  To analyze incidences of common causes of rehospitalization among End-stage renal disease undergoing maintenance hemodialysis at UDOM health center and Benjamin Mkapa Hospital.
• Prevalence of rehospitalization causes [ Time Frame: 5 Years ]
  To analyze prevalence of common causes of rehospitalization among End-stage renal disease undergoing maintenance hemodialysis at UDOM health center and Benjamin Mkapa Hospital.
• Time-to-rehospitalization survival [ Time Frame: 5 years ]
  To analyze time-to-rehospitalization survival in End-stage renal disease patients undergoing maintenance hemodialysis at UDOM health center and Benjamin Mkapa Hospital, by Kaplan-Maier survival curves.

Background: In the last 2 decades, Tanzania made great improvements in the renal replacement therapy infrastructure and services. However, renal replacement therapy remains a challenge in the developing world in terms of inadequate renal registries, and limited published literature.

Objectives: This study will identify predictors of mortality, identify common causes of infection and hospitalization, their incidences, prevalence, and time-to-event analysis and analyze short and long-term survival of end-stage renal disease (ESRD) patients on hemodialysis in two hemodialysis centers in Dodoma, Tanzania. Furthermore, this study will establish a registry to be called Tanzania Registry for Chronic Renal Failure (TRCRF).

Methodology: This will be a prospective-observational study (Patient registry). It will be conducted in Tanzania, a developing world country involving two hemodialysis centers, namely Benjamin Mkapa Hospital and UDOM Health center, both affiliated with the University of Dodoma. Data will be collected by accessing patients' records receiving hemodialysis due to ESRD in the two centers from September 2019 to September 2024. Patients' demographics, medical history, investigation findings, and hemodialysis adequacy will be extracted as independent outcomes. In contrast, the outcome (i.e., Death) during the follow-up will be extracted as a primary dependent outcome. Binary logistic regression will be applied to come up with statistically significant predictors of deaths. Other outcomes will be incidences, prevalence, and time-to-event analysis of common causes of infection and re-hospitalization. Kaplan-Meier survival curves will be constructed from statistically significant predictors of deaths, and patients' survival at 1, 3, and 5 years will be illustrated.

1. Background

   1.1 End-stage renal disease:

   End-stage renal disease is the eighteenth cause of deaths worldwide. Despite relatively stable incidences, the prevalence of End-stage renal disease on renal replacement therapy has doubled from 1990 to 2010, and the toll is expected to rise further in the coming decade raising global health concerns. These findings are in line with the reported increase in the global burden of diabetes mellitus and hypertension, the two diseases accounting for the majority of etiologies for End-stage renal disease. The number of diabetes patients worldwide is projected to be 300 million by the year 2025, from 135 million reported in 1995. By 170%, this increase is projected to be more in the developing world as compared to a 42% expected increase in the developed world. Approximately a billion people every year are diagnosed with hypertension, and nearly 7.1 million dies from its complications, including cerebrovascular disease, ischemic heart disease, and End-stage renal disease.

   1.2 Renal replacement therapy:

   Regardless of etiology, patients with ESRD would need renal replacement therapy (RRT) such as hemodialysis, peritoneal dialysis, or a kidney transplant. Hemodialysis and peritoneal dialysis account for initial modalities of renal replacement therapies before renal transplantation is opted. It is predicted that a decade from now, the number of patients on Renal Replacement Therapy (RRT) to be 5439 million worldwide. Of three modalities, renal transplantation is reported to be superior in terms of patients' survival and quality of life. Despite kidney transplant's superiority, hemodialysis is a widely utilized of three renal replacement therapy modalities. It is estimated that of all patients who received RRT in 2008, eighty-nine percent (89%) received hemodialysis. Despite the decrease in the population of ESRD on peritoneal dialysis in developed countries, the number had doubled in ten years by the year 2008 in developing countries.

   1.3 Patients' survival on renal replacement therapy:

   Despite revolutionizing End-stage renal disease treatment, complications associated with renal replacement therapy, and patients' quality of lives worth consideration. Cardiovascular diseases and infections account for significant causes of deaths directly related to dialyzes, while immunosuppression and comorbidities are accounting for substantial causes of fatalities in ESRD patients undergone renal transplant. Other factors predicting mortality include demographic factors, comorbidities, ESRD etiology, nutritional status, and biochemical indices. Caucasians and patients below 45 years have better survival outcomes. Ongoing evidence of inflammation, increased cardiac enzymes, extreme potassium levels, low hemoglobin levels, inadequate dialysis, and lower BMI are associated with poor survival outcomes.

   1.4 RRT challenges in developing countries:

   Access to renal replacement therapies is by far different in developing from the developed world. About 80% of ESRD patients receiving renal replacement therapy worldwide are in developed countries only, meaning it covers only about 12% of the world's population. Though not impossible, the provision of renal replacement therapy in developing countries is challenging. The challenges can range from patient factors like high costs and poor health-seeking behavior, the low budget allocated in non-communicable diseases to technological complexity out of reach of developing countries. Challenges are categorized into three levels of factors, i.e., patient factors; Healthcare and policy and policy factors, and renal registries.

   1.4.1 Patient' factors:

   Adding to the limited coverage of health insurance services, financially constrained patients who constitute a large population of people in developing countries to find it overwhelming to afford high costs of RRT. Furthermore, poor health-seeking behaviors, patients' remoteness, traditional and religious beliefs hinder RRT provision. Literatures report, about 30% of adults are unaware of having hypertension, more than 40% of people with hypertension have not initiated treatment, while 70% of hypertensive patients have not had their hypertension controlled. Also, in some countries, renal transplantation is less often because kidney donation is considered against religious or traditional beliefs.

   1.4.2 Healthcare system factors:

   In most developing countries, renal replacement therapies are centralized in that they are provided at tertiary referral hospitals, which are few for the population they serve, unlike in developed world where satellite hospitals reduce patients load in tertiary hospitals. In Tanzania, for instance, unevenly distributed three public and nine private dialysis offering hospitals serving over fifty-five million residents. In central Tanzania, the first renal replacement facility, UDOM hemodialysis unit was established in 2013, the first renal transplant was done in 2018, collaborating with BMH, and only one kidney transplants have been done ever since. Furthermore, developing countries are still struggling to vaccinate, controlling the spread, and managing past and newly emerging infectious diseases. Increasing incidences and prevalence of non-communicable diseases like ESRD further cripple and deem healthcare budget inadequate.

   1.4.3 Renal registries:

   Only a few African countries have established and organized renal registries. The majority of countries have not had or have failed to maintain them due to several reasons, including financial constraints. This hinders auditing of treatment and quality of care delivery, as well as limiting sources of information and data for researches. This has led developing countries to depend on information and data obtained from developed countries to make significant health decisions. International comparisons of data on ESRD patients and renal replacement therapy may not be correct in terms of validity due to differences in acceptance of the treatment, patients' demographics, socioeconomic burdens, and national health care policies.

   1.5 Research Problem:

   Incidences and prevalence of ESRD have increased in developing countries including, Sub-Saharan African countries. Of different RRT modalities utilized, hemodialysis accounts for the majority. Unlike in developed countries, inadequate renal health facilities, equipment and personnel, insufficient health budget allocation, lack of renal registries, and few published literature regarding hemodialysis in developing countries hinder the provision of hemodialysis to ESRD patients. Furthermore, patient's factors like hemodialysis-treatment costs, poor health-seeking behavior, traditional beliefs, and residency remoteness, further cripple renal replacement therapy provision. Taking Tanzania into account, no renal registry is currently available in the country, leave alone not having any published literature in the region to report short and long-term survival analysis for ESRD patients on maintenance hemodialysis.

2. Methodology

2.1 Data collection and procedure: Patients' information regarding demographics, medical history, physical examination, causes of ESRD, laboratory findings, imaging results, dialysis adequacy, and follow-up will be sought from electronic and physical patients' files. Other information not captured by patients' hospital-record files will be collected separately by the researcher using a separately-designed form. All data will be recorded in a Microsoft Excel spreadsheet and stored in a registry-office computer as well as a backup in OneDrive cloud storage.

2.2 Data Variables:

This study will have dependent and independent variables. The main dependent variable will be death status, classified as nominal (i.e., dead or alive). Other dependent variables were infection (i.e. nominal), hospitalization (i.e. nominal) and time-to-event (i.e. continuous).

Independent variables consisted of both nominal and continuous. These were Age, Sex, ethnicity, smoking status, marital status, education level, health insurance type, residence, comorbidities, employment status, amount of income, duration of illness before referral, initial pulse rate, initial blood pressure, BMI, waist-hip circumference ratio and definitive diagnosis. Other were initial values of Hemoglobin level, total white blood count, platelets level, thyroid-stimulating hormone, serum thyroxine, serum triiodothyronine, serum electrolytes (i.e., potassium, sodium, chloride, magnesium, inorganic phosphates, CO2-combining power, and anion gap), random blood glucose, Liver function tests (Alanine aminotransferases, Aspartate aminotransferases, Total serum protein, serum albumin, total bilirubin, and direct bilirubin and Alkaline phosphatase), muscle enzymes (myoglobin, lactate dehydrogenase, and creatine kinase), urinalysis (albuminuria, proteinuria, urine-pH, glucosuria, urine sedimentation, and 24-hours urine protein). Moreover, initial results for inflammation indices (procalcitonin, erythrocyte sedimentation rate, C-reactive protein, and hypersensitive C-reactive protein), coagulation indices (prothrombin activity, prothrombin time, international normalized ratio, activated partial prothrombin time, thrombin time and d-dimer), lipid profile (low-density lipoprotein, high-density lipoprotein, lipoprotein-alpha, triglycerides, and total cholesterol). Finally, chronic kidney disease stage, type of dialysis catheter, and dialysis adequacy will further be recorded.

2.3 Bias Management:

Bias in this study will be addressed in two levels; study level and outcome level. A potential source of bias foreseen is regarding the patient's record system. UDOM hemodialysis unit utilizes both electronic (newly established) and physical-paper files, meaning every patient will have two records. To eliminate typing errors during feeding data into the electronic system, a registry-officer will extract data from physical-paper files followed by crosschecking with the electronic database by a second registry-officer, for completeness.

To improve data collection accuracy and efficiency, the two dedicated registry-officers will receive one-month training before the official opening of participants' enrollment.

To minimize reporting biases, STROBE checklist-tool (Strengthening the Reporting of Observational Studies in Epidemiology) customized for cohort and case-control studies, will be used in the report write-up of this study.

To minimize publication biases, this protocol is anticipated to be registered to https://clinicaltrials.gov/, before enrollment of participants.

3.4 Data analysis:

Statistical analysis will be done according to the objectives in question. Firstly, predictors of deaths will be identified by binary logistic regression, using death status as a dependent variable and demographics, medical history, clinical picture, and dialysis adequacy as independent variables. Comparisons will be made by odds ratio between predictors and results tested for statistical significance, at 95% using independent t-test. Computer software SPSS will be used. Independent variables showing significant statistical significance to be predicting deaths will be utilized to construct survival curves by Kaplan-Meier. Survival at 1, 3, and 5 years will be deducted from Kaplan-Meier curves and reported. Computer software SAS will be used here.

A descriptive analysis will be conducted to depict common causes of infection and re-hospitalization, their incidences, and prevalence. Time-to-event analysis for first infection and rehospitalization will be constructed using Kaplan-Maier survival curves. Time-to-event comparison between common causes of diseases and rehospitalization will be compared using the log-rank test. All statistical analyses will be done by computer software SPSS, using a 95% level of significance.

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Inclusion Criteria:

• Participants diagnosed with end-stage renal disease due to any etiology.
• Participants initiated on maintenance dialysis.
• Participants attending Benjamin Mkapa Hospital or UDOM health center.
• Eligible participants consented to participate in the study.

Exclusion Criteria:

• Patients diagnosed with acute kidney injury.
• Patients with CKD stage other than stage 5.
• Patients underwent kidney transplantation.
• Patients on dialysis other than hemodialysis.