Derivation and Validation of a Renal Risk Score for People With Type 2 Diabetes

Elley, C Raina; Robinson, Tom; Moyes, Simon; Kenealy, Tim; Collins, John; Robinson, Elizabeth; Orr-Walker, Brandon; Drury, Paul

doi:10.2337/dc13-0190

Cited by 70 publications

(120 citation statements)

References 31 publications

(47 reference statements)

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“…Our enrollment criterion predicts much higher ESRD risk than recent predictive models of kidney failure 27, 28, 29 . Those models were based on traditional renal markers, eGFR and ACR and several clinical characteristics, such as sex, age, blood pressure, ethnicity, diabetes duration or Hba1c.…”

Section: Discussionmentioning

confidence: 78%

Improved clinical trial enrollment criterion to identify patients with diabetes at risk of end-stage renal disease

Yamanouchi

Skupień

Niewczas

et al. 2017

Kidney International

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Design of Phase III trials in diabetic nephropathy currently requires patients at high risk of progression defined as within three years of a hard end-point (end stage renal disease, 40% loss of estimated glomerular filtration rate, or death). To improve design of these trials, we used natural history data from the Joslin Kidney Studies of chronic kidney disease in patients with diabetes to develop an improved criterion to identify such patients. This included a training cohort of 279 patients with type 1 diabetes and 134 end-points within three years, and a validation cohort of 221 patients with type 2 diabetes and 88 end-points. Previous trials selected patients using clinical criteria for baseline urinary albumin to creatinine ratio and estimated glomerular filtration rate. Application of these criteria to our cohort data yielded sensitivities (detection of patients at risk) of 70-80% and prognostic values of only 52-63%. We applied Classification and Regression Trees analysis to select from among all clinical characteristics and markers the optimal prognostic criterion that divided patients with type 1 diabetes according to risk. The optimal criterion was a serum Tumor Necrosis Factor Receptor 1 level over 4.3 ng/ml alone or 2.9-4.3 ng/ml with an albumin to creatinine ratio over 1.9 g/g. Remarkably, this criterion produced similar results in both type 1 and type 2 diabetic patients. Overall, sensitivity and prognostic value were high (72% and 81%, respectively). Thus, application of this criterion to enrollment in future clinical trials could reduce sample size required to achieve adequate statistical power for detection of treatment benefits.

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Section: Discussionmentioning

confidence: 78%

Improved clinical trial enrollment criterion to identify patients with diabetes at risk of end-stage renal disease

Yamanouchi

Skupień

Niewczas

et al. 2017

Kidney International

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“…7–12 However, widespread adoption of the KFREs requires validation in additional populations including non-white ethnicities, patients not under nephrology care, and cohorts outside North America. The accuracy of the KFREs in different geographic regions and patient populations is evaluated here.…”

Section: Introductionmentioning

confidence: 99%

Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure

Tangri

Grams

Levey

et al. 2016

JAMA

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500

563

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“…Elley et al . presented current smoking status, but not past smoking status as a predictor of ESRD in patients with type 2 diabetes mellitus.…”

Section: Discussionmentioning

confidence: 99%

Clinical predictive factors in diabetic kidney disease progression

Radcliffe

Seah

Clarke

et al. 2016

J of Diabetes Invest

149

109

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Diabetic kidney disease (DKD) represents a major component of the health burden associated with type 1 and type 2 diabetes. Recent advances have produced an explosion of ‘novel’ assay‐based risk markers for DKD, though clinical use remains restricted. Although many patients with progressive DKD follow a classical albuminuria‐based pathway, non‐albuminuric DKD progression is now well recognized. In general, the following clinical and biochemical characteristics have been associated with progressive DKD in both type 1 and type 2 diabetes: increased hemoglobin A1c, systolic blood pressure, albuminuria grade, early glomerular filtration rate decline, duration of diabetes, age (including pubertal onset) and serum uric acid; the presence of concomitant microvascular complications; and positive family history. The same is true in type 2 diabetes for male sex category, in patients following an albuminuric pathway to DKD, and also true for the presence of increased pulse wave velocity. The following baseline clinical characteristics have been proposed as risk factors for DKD progression, but with further research required to assess the nature of any relationship: dyslipidemia (including low‐density lipoprotein, total and high‐density lipoprotein cholesterol); elevated body mass index; smoking status; hyperfiltration; decreases in vitamin D, hemoglobin and uric acid excretion (all known consequences of advanced DKD); and patient test result visit‐to‐visit variability (hemoglobin A1c, blood pressure and high‐density lipoprotein cholesterol). The development of multifactorial ‘renal risk equations’ for type 2 diabetes has the potential to simplify the task of DKD prognostication; however, there are currently none for type 1 diabetes‐specific populations. Significant progress has been made in the prediction of DKD progression using readily available clinical data, though further work is required to elicit the role of several variables, and to consolidate data to facilitate clinical implementation.

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Derivation and Validation of a Renal Risk Score for People With Type 2 Diabetes

Cited by 70 publications

References 31 publications

Improved clinical trial enrollment criterion to identify patients with diabetes at risk of end-stage renal disease

Improved clinical trial enrollment criterion to identify patients with diabetes at risk of end-stage renal disease

Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure

Clinical predictive factors in diabetic kidney disease progression

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