Simplified end stage renal failure risk prediction model for the low-risk general population with chronic kidney disease

Abstract

Background Chronic kidney disease (CKD) contributes significant morbidity and mortality among Asians; hence interventions should focus on those most at-risk of progression. However, current end stage renal failure (ESRF) risk stratification tools are complex and not validated in multi-ethnic Asians. We hence aimed to develop an ESRF risk prediction model by taking into account ethnic differences within a fairly homogenous socioeconomic setting and using parameters readily accessible to primary care clinicians managing the vast majority of patients with CKD. Methods We performed a prospective cohort study of 1970 adults with CKD estimated glomerular filtration rate <60 ml/min/1.73m 2 or albuminuria >30 mg/g from the population-based Singapore Epidemiology of Eye Diseases study (n = 10,033). Outcome was incident ESRF, ascertained by linkage to the Singapore Renal Registry until 2015. Results Mean follow up was 8.5 ± 1.8 years and ESRF occurred in 32 individuals (1.6%). ESRF incidence rates were 2.8, 0.8 and 2.6 per 1000 patient years in Malays, Indians and Chinese respectively. The best ESRF prediction model included age, gender, eGFR and albuminuria (calibration χ2 = 0.45, P = 0.93; C-statistic 0.933, 95% confidence interval (CI) 0.889–0.978, p = 0.01; AIC 356). Addition of ethnicity improved discrimination marginally (C statistic 0.942, 95% CI 0.903–0.981, p = 0.21). Addition of clinical variables such as diabetes and hyperlipidemia did not improve model performance significantly. Conclusion We affirmed the utility of commonly available clinical information (age, gender, eGFR and UACR) in prognosticating ESRF for multi-ethnic Asians with CKD.

Changes in metabolic parameters and adverse kidney and cardiovascular events during glomerulonephritis and renal vasculitis treatment in patients with and without diabetes mellitus

“…While DM is an established risk factor for kidney failure and cardiovascular disease in the general population and in individuals with chronic kidney disease [ 13 , 22 – 24 ], there has been little focus on the additive risks of DM in glomerulonephritis and renal vasculitis, which may in themselves result in inherently increased risks of ESKD and cardiovascular disease [ 25 , 26 ]. We confirmed that DM doubled the risk of cardiovascular-related hospitalization independent of other cardiovascular risk factors and underlying glomerulonephritis diagnosis.…”

Application of machine learning techniques to understand ethnic differences and risk factors for incident chronic kidney disease in Asians

“…An interviewer-administered questionnaire was used to collect participants’ sociodemographic (age, gender), socioeconomic (highest education attained), lifestyle (current smoking, alcohol consumption) and medical history as previously described. 23 Physical examination included height, weight and blood pressure (BP) measurements. We calculated BMI as weight in kilograms divided by height in meters squared.…”

Prediction of individuals at high risk of chronic kidney disease during treatment with lithium for bipolar disorder

“…In the general population, established risk factors for CKD include age, sex (increased in women), ethnicity (increased in Black, Asian and Minority Ethnic (BAME) populations), family history of kidney disease, smoking, obesity, hypertension, diabetes mellitus, excessive alcohol consumption and acute kidney injury [ 9 ]. Prediction models have been developed for end-stage kidney failure in groups with a range of underlying risk [ 10 – 14 ]. These tend to include a small number of core features including age, gender, ethnicity, eGFR and albuminuria.…”