Performance of prediction models for nephropathy in people with type 2 diabetes: systematic review and external validation study

Abstract

Objectives To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. Design Systematic review and external validation. Data sources PubMed and Embase. Eligibility criteria Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. Methods Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell’s C statistic). Results 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. Conclusions This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. Systematic review registration PROSPERO CRD42020192831.

A novel kidney disease index reflecting both the albumin-to-creatinine ratio and estimated glomerular filtration rate, predicted cardiovascular and kidney outcomes in type 2 diabetes

“…Many epidemiologic analyses evaluated multivariable prediction models for diabetes-related kidney outcomes and reported excellent performance for models that include many variables in addition to eGFR and/or ACR [11]. Some have identified both the eGFR and ACR as important indices for kidney outcomes [12][13][14][15], cardiovascular outcomes [16][17][18], and death [14][15][16].…”

Bibliometric Study of Trends in the Diabetic Nephropathy Research Space from 2016 to 2020

“…Through analyses of multiple patient populations and databases, researchers can better clarify DN patient mortality rates, ESRD incidence in an effort to identify risk factors associated with these outcomes [ 32 ]. Further epidemiological analyses have the potential to guide the development and validation of prognostic models capable of gauging DN patient outcomes in a clinical setting to guide treatment efforts [ 33 ]. There have been many studies of the pathophysiology of DN at the biopsy, cellular, molecular, and single-cell molecular levels [ 34 ], with a variety of research techniques having been employed to clarify the drivers of this condition in an effort to develop more reliable preventative, diagnostic, and treatment strategies for affected patients.…”

Development of minimal resource pre-screening tools for chronic kidney disease in people with type 2 diabetes

“…17 Many models to predict CKD in people with T2DM already exist. 18 However, the focus in the literature remains on prognostic modelling (ie predicting a future diagnosis of CKD), whereas we have modelled a patient's risk of currently experiencing renal impairment. Similar tools have been proposed for the general population but our models are specific to patients with diabetes and highlight that diabetes is likely to interact with risk factors.…”