Hospital-acquired acute kidney injury prevalence in in adults at a South African tertiary hospital

Background: Hospital Acquired Acute Kidney Injury (HA-AKI) prevalence has not been analysed in a South African setting. We investigated HA-AKI prevalence, using the KDIGO definition, with clinical characteristics and outcomes. The aim was to provide evidence for earlier treatment interventions to improve outcomes, such as recent UK NHS initiatives of automated electronic alerts in the laboratory information system.Methods: Retrospective laboratory and clinical data was analysed for a 6-month period at Tygerberg Hospital, Cape Town. Serum creatinine results and clinical records were analysed and collated into gender and age group specific results.Results: HA-AKI occurred in 6.2% of hospitalised patients for the period of analysis. The highest incident occurred in females aged 18-39 and males aged 40-59. The most common AKI stage reached was stage 1. HA-AKI increased length of stay by an average of 4.6 days and 20% of patients were readmitted at a later date with renal dysfunction.Conclusion: AKI prevalence is significant and associated with adverse patient outcomes. Initiatives that allow front-line healthcare professionals to treat and manage AKI, such as introduction of automated electronic alerts, should be considered. Similar initiatives have been implemented in UK NHS hospitals with positive impacts.Keywords: Acute kidney, injury prevalence, South African

Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

BACKGROUND: Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans. METHODS: Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment. RESULTS: Seven hundred thirty-seven participants (27% male), mean age, 52.2years, were included, among whom 130 (17.6%) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95% confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment. CONCLUSIONS: The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings

Proliferator-activated receptor gamma Pro12Ala interacts with the insulin receptor substrate 1 Gly972Arg and increase the risk of insulin resistance and diabetes in the mixed ancestry population from South Africa

BACKGROUND: The peroxisome proliferator-activated receptor gamma (PPARG), Pro12Ala and the insulin receptor substrate (IRS1), Gly972Arg confer opposite effects on insulin resistance and type 2 diabetes mellitus (T2DM). We investigated the independent and joint effects of PPARG Pro12Ala and IRS1 Gly972Arg on markers of insulin resistance and T2DM in an African population with elevated risk of T2DM. In all 787 (176 men) mixed-ancestry adults from the Bellville-South community in Cape Town were genotyped for PPARG Pro12Ala and IRS1 Gly972Arg by two independent laboratories. Glucose tolerance status and insulin resistance/sensitivity were assessed. RESULTS: Genotype frequencies were 10.4% (PPARG Pro12Ala) and 7.7% (IRS1 Gly972Arg). Alone, none of the polymorphisms predicted prevalent T2DM, but in regression models containing both alleles and their interaction term, PPARG Pro12 conferred a 64% higher risk of T2DM. Furthermore PPARG Pro12 was positively associated in adjusted linear regressions with increased 2-hour post-load insulin in non-diabetic but not in diabetic participants. CONCLUSION: The PPARG Pro12 is associated with insulin resistance and this polymorphism interacts with IRS1 Gly972Arg, to increase the risk of T2DM in the mixed-ancestry population of South Africa. Our findings require replication in a larger study before any generalisation and possible application for risk stratification

Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans

BACKGROUND: Chronic kidney disease (CKD) is a global challenge. Risk models to predict prevalent undiagnosed CKD have been published. However, none was developed or validated in an African population. We validated the Korean and Thai CKD prediction model in mixed-ancestry South Africans. METHODS: Discrimination and calibration were assessed overall and by major subgroups. CKD was defined as 'estimated glomerular filtration rate (eGFR) <60ml/min/1.73m 2 ' or 'any nephropathy'. eGFR was based on the 4-variable Modification of Diet in Renal Disease (MDRD) formula. RESULTS: In all 902 participants (mean age 55years) included, 259 (28.7%) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73-0.79) for 'eGFR <60ml/min/1.73m 2 ' and 0.81 (0.78-0.84) for 'any nephropathy' for the Korean model; corresponding values for the Thai model were 0.80 (0.77-0.83) and 0.77 (0.74-0.81). Discrimination was better in men, older and normal weight individuals. The model underestimated CKD risk by 10% to 13% for the Thai and 9% to 93% for the Korean model. Intercept adjustment significantly improved the calibration with an expected/observed risk of 'eGFR <60ml/min/1.73m 2 ' and 'any nephropathy' respectively of 0.98 (0.87-1.10) and 0.97 (0.86-1.09) for the Thai model; but resulted in an underestimation by 24% with the Korean model. Results were broadly similar for CKD derived from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CONCLUSION: Asian prevalent CKD risk models had acceptable performances in mixed-ancestry South Africans. This highlights the potential importance of using existing models for risk CKD screening in developing countries

An audit of 24-hour creatinine clearance measurements at Tygerberg Hospital and comparison with prediction equations

ArticleThe original publication is available at http://www.samj.org.zaBibliographyBACKGROUND: Internationally, clinical guidelines recommend the use of creatinine-based equations to estimate glomerular filtration rate (GFR) for assessment and follow-up of kidney disease. The routine use of 24-hour creatinine clearances (CrCl) is no longer advocated. Objectives. To examine the indications for requesting CrCl at Tygerberg Hospital, identify problems associated with the procedure, and evaluate the utility of the Cockcroft-Gault (CG) and Modification of Diet in Renal Disease (MDRD) equations with different levels of renal dysfunction in the ethnic groups of the Western Cape. Methods. A clinical audit of CrCl was performed. The estimated GFR as predicted by the modified CG and MDRD formulae was compared with CrCl in 252 patients, representing three local ethnic groups. MDRD formulae with and without the correction factor for black ethnic group (MDRD-B) were evaluated. Results. Problems with urine collection or data supplied were identified in one-third of CrCl requests, leading to unreliable results. The CG correlated best with CrCl in the group as a whole. The average absolute and percentage differences from CrCl in the different ethnic groups were as follows: coloured (mixed ethnicity) (N = 186) - CG 13.4 ml/min/1.73 m2 (18%), MDRD 16.8 ml/min/1.73 m2 (23%) and MDRD-B 27.9 ml/mim/1.73 m2 (37%); black (N = 21) - CG 14.8 ml/min/1.73 m2 (19%), MDRD 12.9 ml/min/1.73 m2 (17%) and MDRD-B 25.1 ml/min/1.73 m2 (33%); white (N = 45) CG 13.5 ml/min/1.73 m2 (19%), MDRD 15.3 ml/min/1.73 m2 (21%) and MDRD-B 24.8 ml/min/1.73 m2 (35%). Throughout the renal function levels (chronic kidney disease stages 1 - 5) CG correlated better with CrCl than MDRD. Conclusions. Possible reasons for poor correlations include a high prevalence of obesity, underweight and normal GFR in the study population. There is a need for further research, using a gold standard, into the accuracy of these prediction equations in our unique patient populations before firm recommendations can be made regarding their use. Until then CrCl will continue to be widely used. Greater efforts at patient and health care worker education are required to ensure proper collections.Publishers' Versio

No evidence for association of insulin receptor substrate-1 Gly972Arg variant with type 2 diabetes mellitus in a mixed-ancestry population of South Africa

BACKGROUND: The most common single-nucleotide polymorphism in the insulin receptor substrate-1 (IRS1) gene is Gly972Arg, which is associated with a 25% increased risk of developing diabetes. The mixed-ancestry population of South Africa (SA) has one of the highest prevalences of type 2 diabetes mellitus (T2DM) in Africa. OBJECTIVE: To report the frequency of IRS1 Gly972Arg and investigate its associations with cardiometabolic traits. METHODS: DNA from 856 mixed-ancestry adults drawn from an urban community of Bellville South, Cape Town, SA, was genotyped by two independent laboratories. Oral glucose tolerance tests were performed and cardiometabolic risk factors measured. RESULTS: A total of 237 (24.7%) participants had T2DM. The IRS1 Gly972Arg variant was present in 7.9% of the individuals studied and only one participant (non-diabetic) carried the homozygous A/A variant. In linear and logistic regression analyses, Gly972Arg was not associated with obesity, insulin resistance/sensitivity or T2DM. CONCLUSIONS: The prevalence of the Gly972Arg variant in the mixed-ancestry population of SA is comparable to that reported in African Americans, but its presence is not associated with cardiometabolic traits. This suggests that the Gly972Arg variant may not aid diabetes risk evaluation in this setting, nor can such information help explain the high prevalence of diabetes previously reported in this population

Effects of different missing data imputation techniques on the performance of undiagnosed diabetes risk prediction models in a mixed-ancestry population of South Africa

BACKGROUND: Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation. METHODS: Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models’ discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment. RESULTS: The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4%) had missing data. Family history had the highest proportion of missing data (25%). Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals). Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods. CONCLUSIONS: Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n=30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals

Clinical staff knowledge and awareness of point of care testing best practices at Tygerberg Hospital, South Africa

Background: Point-of-care testing (POCT) is defined as testing done near or at the site of patient care with the goal of providing rapid information and improving patient outcomes. Point-of-care testing has many advantages and some limitations which affect its use and implementation. Objective: The aim of the audit was to determine the current practices, staff attitudes and training provided to hospital clinical staff. Methods: The audit was conducted with the use of a questionnaire containing 30 questions. One hundred and sixty questionnaires were delivered to 55 sites at Tygerberg Academic Hospital in Cape Town, South Africa, from 21 June 2016 to 15 July 2016. A total of 68 questionnaires were completed and returned (42.5% response rate). Results: Most participants were nursing staff (62/68, 91%), and the rest were medical doctors (6/68, 9%). Most participants (66/68, 97%) performed glucose testing, 16/68 (24%) performed blood gas testing and 17/68 (25%) performed urine dipstick testing. Many participants (35/68, 51%) reported having had some formal training in one or more of the tests and 25/68 (37%) reported having never had any formal training in the respective tests. Many participants (46/68, 68%) reported that they never had formal assessment of competency in performing the respective tests. Conclusion: Participants indicated a lack of adequate training in POCT and, thus, limited knowledge of quality control measures. This audit gives an indication of the current state of the POCT programme at a tertiary hospital and highlights areas where intervention is needed to improve patient care and management

E-Selectin and markers of HIV disease severity, inflammation and coagulation in HIV-infected treatment-naïve individuals

Background: E-selectin has been shown to play a role in atherosclerosis and to be increased in HIV-infected individuals due to chronic immune activation. There is a paucity of studies on E-selectin in HIV-infected treatment-naïve individuals. Objectives: This study aimed to determine whether E-selectin levels were increased in HIV-infected treatment-naïve individuals and whether these correlated with markers of disease severity, inflammation and coagulation to determine if this population is at risk for cardiovascular disease (CVD).Methods: E-selectin levels were determined in 114 HIV-infected treatment-naive and 66 HIV-negative individuals, compared between groups and correlated with markers of disease severity, inflammation and coagulation.Results: There were statistically significant differences (p&lt;0.01) in levels of WCC, CD4+ count, %CD38/8, albumin, IgG, hsCRP and D-dimer between groups and no statistically significant differences in E-selectin (p=0.84) and fibrinogen (p=0.65) levels. E-selectin correlated with age (p=0.02) and gender (p=0.01). Conclusion: E-selectin was a poor marker in this setting. There was no correlation with any of the markers of disease severity, inflammation and coagulation. E-selectin is most likely raised in an acute inflammatory setting, rather than chronic stage of HIV-infection. We recommend that other markers be utilized to identify patients at increased risk of CVD; as these were significantly increased untreated in individuals.Keywords: E-selectin, inflammation and coagulation in HIV-infected treatment-naïve individuals