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Data Set : Prevalence, characterization and response to chronic kidney disease in an urban and rural setting in South Africa
(2016-11-18) Naicker, Saraladevi; Fabian, June; Jaya A George; Harriet R Etheredge; Manuel van Deventer; Robert Kalyesubula; Alisha N Wade; Laurie A Tomlinson; Stephen Tollman
Globally, chronic kidney disease (CKD) is an emerging public health challenge but accurate data on its true prevalence are scarce, particularly in poorly resourced regions such as sub-Saharan Africa (SSA). Limited funding for population-based studies, poor laboratory infrastructure and the absence of a validated estimating equation for kidney function in Africans are contributing factors. Consequently, most available studies used to estimate population prevalence are hospital-based, with small samples of participants who are at high risk for kidney disease. While serum creatinine is most commonly used to estimate glomerular filtration, there is considerable potential bias in the measurement of creatinine that might lead to inaccurate estimates of kidney disease at individual and population level. To address this, the Laboratory Working Group of the National Kidney Disease Education Program published recommendations in 2006 to standardize the laboratory measurement of creatinine. The primary objective of this review was to appraise implementation of these recommendations in studies conducted in SSA after 2006. Secondary objectives were to assess bias relating to choice of estimating equations for assessing glomerular function in Africans and to evaluate use of recommended diagnostic criteria for CKD. This study was registered with Prospero (CRD42017068151), and using PubMed, African Journals Online and Web of Science, 5845 abstracts were reviewed and 252 full-text articles included for narrative analysis. Overall, two-thirds of studies did not report laboratory methods for creatinine measurement and just over 80% did not report whether their creatinine measurement was isotope dilution mass spectroscopy (IDMS) traceable. For those reporting a method, Jaffe was the most common (93%). The four-variable Modification of Diet in Renal Disease (4-v MDRD) equation was most frequently used (42%), followed by the CKD Epidemiology Collaboration (CKD-EPI) equation for creatinine (26%). For the 4-v MDRD equation and CKD-EPI equations, respectively, one-third to one half of studies clarified use of the coefficient for African-American (AA) ethnicity. When reporting CKD prevalence, <15% of studies fulfilled Kidney Disease: Improving Global Outcomes criteria and even fewer used a population-based sample. Six studies compared performance of estimating equations to measured glomerular filtration rate (GFR) demonstrating that coefficients for AA ethnicity used in the 4-v MDRD and the CKD-EPI equations overestimated GFR in Africans. To improve on reporting in future studies, we propose an 'easy to use' checklist that will standardize reporting of kidney function and improve the quality of studies in the region. This research contributes some understanding of the factors requiring attention to ensure accurate assessment of the burden of kidney disease in SSA. Many of these factors are difficult to address and extend beyond individual researchers to health systems and governmental policy, but understanding the burden of kidney disease is a critical first step to informing an integrated public health response that would provide appropriate screening, prevention and management of kidney disease in countries from SSA. This is particularly relevant as CKD is a common pathway in both infectious and non-communicable diseases, and multimorbidity is now commonplace, and even more so when those living with severe kidney disease have limited or no access to renal replacement therapy.
Dataset from: Chronic kidney disease (CKD) and associated risk in rural South Africa: a population-based cohort study
(2022-07-13) Fabian, June; Gondwe, Mwawi; Mayindi, Nokthula; Khoza, Bongekile; Gaylard, Petra; Wade, Alisha N.; Gómez‑Olivé, F. Xavier; Tomlinson, Laurie A.; Ramsay, Michele; Tollman, Stephen Meir; Winkler, Cheryl; George, Jaya Anna; Naicker, Saraladevi; Study data were collected and managed using opensource REDCap electronic data capture tools hosted at the University of the Witwatersrand
Study Methods This longitudinal cohort study was conducted from November 2017 to September 2018 in the Medical Research Council (MRC)/Wits Rural Public Health and Health Transitions Research Unit (otherwise referred to as "Agincourt") in Bushbuckridge, a rural subdistrict of the Mpumalanga province in north-eastern South Africa. Agincourt is a Health and Socio-Demographic Surveillance System (HDSS) site that includes approximately 115,000 people. For this study, a minimum sample size of 1800 was required to provide at least 80% power to determine CKD prevalence of at least 5%, provided the true prevalence was equal to or more than 6.5%. Proportional allocation of Black African adults aged 20 to 79 years ensured a representative sample based on the most recent annual population census. Sample size was increased proportionately to 2759 individuals to accommodate a 25% non-participation rate. Dataset is 2022 cases Variables are: 1. age 2. Gender 3. Years of Education (refers to completed years of schooling) 4. Height (cm) (one decimal place) 5. weight (kg) (one decimal place) 6. BMI (body mass index) 7. POC random cholesterol (mmol/L) (2 decimal places) 8. POC random glucose (mmol/L) (1 decimal place) 9. HIV status is: Based on (i) prior HIV testing history OR (ii) HIV PCR testing for ARK 10. Using the urine pregnancy test, is this participant pregnant? ( 11. ERY (erythrocytes, blood) 12. Hb (haemoglobin, blood) LEU (leucocytes) 13. NIT (nitrites) 14. PRO (protein) 15. hepatitis B surface antigen 16. Serum creatinine (umol/L) 17. Systolic BP(1) 18. Diastolic BP (1) 19. Systolic BP(2) 20. Diastolic BP (2) 21. Systolic BP(3) 22. Diastolic BP (3) 23. Serum creatinine (umol/L) 24. Urine microalbumin (mg/L) 25. Urine creatinine (mmol/L) 26. Urine microalbumin (mg/L) 27. Urine creatinine (mmol/L) 28. APOL1 haplotype
Dataset From: Evaluation of potential kidney donors and outcomes post-donation at Charlotte Maxeke Johannesburg Academic Hospital (1983-2015)
(Division of Nephrology, Charlotte Maxeke Johannesburg Academic Hospital, 2022-03-23) Dayal, Chandni; Davies, Malcolm; Diana, Nina Elisabeth; Meyers, Anthony M.
Data was collected from existing clinical records of the Living Donor Clinic held by the Division of Nephrology at Charlotte Maxeke Johannesburg Academic Hospital. This was performed by the primary investigator / first author in a pseudo-anonymized fashion and stored securely in an Excel database to which only the primary investigator had access along with the data key. Procedures pertaining to original data capturing to clinical records by the data manager (Sister Nancy Makoe), were in accordance with the standard operating procedure set out by the Transplant Unit at Charlotte Maxeke Johannesburg Academic Hospital. Objectives of research Primary Objective • To determine donor morbidity and mortality after donation. • Analysis of morbidity will focus on the development of - New onset hypertension following donation (BP ≥140/90) - Chronic kidney disease following donation, defined as the development of either of the following - New onset proteinuria (AER >300mg/day) - An eGFR <60 ml/min/1.73 m² (using the CKD-EPI formula) Secondary Objectives • To determine the reasons for exclusion of potential donors from living kidney donation • To determine the prevalence of ESKD following donation (eGFR <15 ml/min/1.73 m² using the CKD-EPI formula) • To determine potential risk factors associated with proteinuria and/or a reduced eGFR post kidney donation, by evaluating a. donor demographics b. the presence of isolated medical abnormalities prior to donation, defined by: - a borderline pre-donation 51Cr-EDTA GFR (<80 ml/min/1.73 m²) - pre-existing hypertension (well controlled on a single agent with no end-organ damage) - class I obesity (BMI 30-35 kg/m²) • To determine the proportion of patients lost to follow-up post donation 5.2 Study design A single centre retrospective observational study was conducted of all patients attending the Living Donor Clinic in the Renal Unit at CMJAH over a 32-year period between 01 January 1983 and 31 July 2015. The closing date for sampling reflects the period of protocol submission for this study. The cohort comprised of 1208 potential living donors, of which: • 910 are failed living donors, assessed between 01 January 1990 and 31 July 2015 • 298 are accepted living donors, assessed between 01 January 1983 and 31 July 2015 5.3 Data collection 5.2.1 Data collection for failed living donors Data collection for failed living donors comprised the following parameters: • Demographic data – age at assessment, gender and ethnicity • Family history of the donor • Relation to the intended recipient – whether related, unrelated or altruistic • The outcome of eligibility evaluation • If excluded from living donation, reasons for non-donation will be documented, which were categorised as: - donor-recipient related, - donor-related, - recipient-related, or - miscellaneous. • The indications and findings of any renal biopsy undertaken on a donor was recorded 5.2.2 Data collection for accepted living donors Data collection for accepted living donors comprised the following parameters: • Demographic information – gender, ethnicity, age at donation (as well as age at each follow-up point) • Family history of the accepted donor • Details pertaining to the donation, specifically: - relation to the recipient, as well as cause of renal failure in the recipient - the date of donation - the graft outcome (if known) • The last follow-up date at the Living Donor Clinic and the approximate number of post-donation follow-up visits • Domicile in relation to the Living Donor Clinic (in kilometres from transplant centre) • The reason for lost to follow-up (if known) • Baseline characteristics at donation, including: - Body mass index - Urine albumin: creatinine ratio - Systolic blood pressure - Diastolic blood pressure - Baseline serum creatinine - eGFR as defined by an isotope study, the chromium-51-ethylene-diamine-tetra-aceticacid scan (51Cr EDTA scan) as well as the CKD-EPI formula - Habits, including smoking status and history of alcohol consumption - History of pre-existing medical condition(s) • Characteristics at follow-up (correlated with time after donation), including: - Body mass index - Urine albumin: creatinine ratio - Systolic blood pressure - Diastolic blood pressure - Serum creatinine - eGFR as defined by the CKD-EPI formula - Habits, including smoking status and alcohol consumption - Development of co-morbid disease - History of nephrotoxic drug intake The above variables were retrospectively collected from data recorded at the patients’ first follow-up visit post-donation, one-year post-donation visit, and at the most recent follow-up visit. • Mortality data was collected in accepted living donors that demised during the study period, and will include: - age at death - the time from donation to mortality - cause of death, whether related to renal disease, a cardiovascular event or other cause 5.3 Definition of variables 5.3.1 Classification of donors • Potential living donors (PLDs) – refer to all donors assessed at the CMJAH Living Donor Clinic • Failed living donors (FLDs) – refer to the sub-group of PLDs excluded from living kidney donation • Accepted living donors (ALDs) – refer to the subgroup of PLDs that ultimately donated a kidney 5.3.2 Hypertension Defined as per the Eighth Joint National Committee (JNC8) guidelines for blood pressure targets: • For donors with a current age of more than sixty years: - a systolic blood pressure of more than 150mmHg, with - a diastolic blood pressure of more than 90mmHg • For donors with a current age of less than sixty years: - a systolic blood pressure of more than 140mmHg, with - a diastolic blood pressure of more than 90mmHg 5.3.2 Albuminuria Quantified as per the revised Kidney Disease Improving Global Outcomes (KDIGO) chronic kidney disease classification into three stages of albuminuria based on the albumin excretion rate (AER) in milligrams per day (mg/day): • A1: Normal or mildly increased (AER <30 mg/day) • A2: Moderately increased (AER between 30 - 300 mg/day) • A3: Severely increased (AER >300 mg/day, with nephrotic range proteinuria defined as >3500 mg/day) 5.3.3 Glomerular filtration rate • Pre-donation GFR will be defined: - as per isotope study: 51Cr EDTA scan - as calculated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula, expressed as: GFR = 141 × min (Scr /κ, 1) α × max (Scr /κ, 1)-1.209 × 0.993Age × 1.018 [if female] × 1.159 [if black] where: GFR = glomerular filtration rate in ml/min/1,73m2 Scr = serum creatinine in mg/dL κ = 0.7 for females and 0.9 for males α = -0.329 for females and -0.411 for males min indicates the minimum of Scr /κ or 1, and max indicates the maximum of Scr /κ or 1. • Post-donation GFR will be calculated by the CKD-EPI formula, as expressed above. 5.3.4 Chronic kidney disease Defined as per the revised Kidney Disease Outcomes Quality Initiative (KDOQI) as either kidney damage or GFR<60 ml/min/1.73 m² for ≥ 3 months. Kidney damage encompasses pathological abnormalities or markers of damage, including biochemical or radiological abnormalities. GFR is further classified into stages (table 1.1). Table 1.1 | Revised KDOQI classification for chronic kidney disease GFR Stages GFR (ml/min/1.73 m2) Classification 1 >90 Normal 2 60 – 89 Mildly decreased 3a 45 – 59 Mildly to moderately decreased 3b 30 – 44 Moderately to severely decreased 4 15 – 29 Severely decreased 5 <15 ESKD 5.3.5 Body mass index • BMI will be calculated as weight (in kilograms) divided by height (in meters) squared. • It will then be sub-classified as per the World Health Organisation (WHO) international BMI classification (table 1.2). Table 1.2 | WHO international classification of BMI Classification BMI (kg/m2) Underweight < 18.5 Normal Range 18.5 to 24.99 Overweight Pre-obese Obese - Obese Class I - Obese Class II - Obese Class III ≥ 25 25 to 29.99 ≥ 30 30 to 34.99 35 to 39.99 ≥ 40 5.3.6 Isolated medical abnormalities Refers to donors with any of the following characteristics prior to donation: • A borderline 51Cr-EDTA GFR <80 ml/min/1.73 m2 • Pre-existing hypertension well-controlled on a single agent with no end- organ damage • Class I obesity (BMI 30 - 35 kg/m2 )
Dataset From: Forgotten but not gone in rural South Africa: Urinary schistosomiasis and implications for chronic kidney disease screening in endemic countries
(2022-12-11) Craik,Alison; Mayindi,Nokthula; Chipungu,Shingirai; Khoza,Bongekile; Gómez-Olivé, Xavier F; Tomlinson, Laurie Alexandra
Study information The African Research on Kidney Disease (ARK) Study aimed to determine chronic kidney disease (CKD) prevalence and identify associated risk factors in rural South Africa. The study took place from November 2017 to September 2018 and included a population-based sample (N=2759) of adults aged 20-79 years from the Agincourt Health and Socio-Demographic Surveillance System (HDSS) site in rural Bushbuckridge, Mpumalanga Province. Institutional review board approval was obtained from the University of Witwatersrand (clearance number M170583) Written informed consent was obtained from individual participants prior to enrolment. This is a secondary data analysis nested within the ARK study. In this population-based cohort study, we aimed to characterise the burden of urinary schistosomiasis in rural South Africa and evaluate its relationship with markers of kidney dysfunction with implications for CKD screening. We recruited 2021 adults aged 20-79 years in the Mpumalanga Province, South Africa. Sociodemographic and anthropometric data were recorded, urinalysis performed, and serum and urine samples collected. We measured serum creatinine and urine albumin/creatinine. Kidney dysfunction was defined as an estimated glomerular filtration rate (eGFR) <60ml/min/1.73m2 and/or urine albumin-creatinine ratio >3.0mg/mmol. S.haematobium infection was determined by urine microscopy. Multivariable analyses were performed to determine relationships between S.haematobium and kidney dysfunction. The methodology for this sub-study is dependent on the larger ARK study processes. Data quality and ethics processes have previously been validated by the ARK consortium . Institutional review board approval was obtained from the University of Witwatersrand (clearance number M170583) Written informed consent was obtained from individual participants prior to enrolment. Additional approval for this sub-study from the London School of Hygiene and Tropical Medicine (reference number 22152). Kalyesubula R, Fabian J, Nakanga W, Newton R, Ssebunnya B, Prynn J, et al. How to estimate glomerular filtration rate in sub-Saharan Africa: design and methods of the African Research into Kidney Diseases (ARK) study. BMC Nephrol. 2020 Jan 15;21(1):20.
WDGMC Paediatric Liver Transplant Research Database
(REDcap, 2019-12-09) Fabian, June; Botha, Jean; Van der Schyff, Francisca.; Terblanche, Alberta J
Biliary atresia (BA) is a progressive fibrosing cholangiopathy of infancy, the most common cause of cholestatic jaundice in infants and the top indication for liver transplantation in children. Kasai portoenterostomy (KPE) when successful may delay the requirement for liver transplantation, which in the majority offers the only cure. Good outcomes demand early surgical intervention, appropriate management of liver cirrhosis, and in most cases, liver transplantation. These parameters were audited of children with BA treated at the Steve Biko Academic Hospital (SBAH) in Pretoria, South Africa.

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