What question did this study set out to answer?

This review evaluates the quality and predictive performance of models for diabetic kidney disease outcomes.

July 2, 2026

Prognostic models for diabetic kidney disease outcomes: A systematic review

Key Points

This review evaluates the quality and predictive performance of models for diabetic kidney disease outcomes.
Systematic search conducted across multiple databases up to April 2026.
Data extracted using the CHARMS checklist and assessed using PROBAST for bias.
Included 27 studies with 80 prediction models focusing on renal-related and cardiovascular outcomes.
Models showed acceptable discrimination with AUC/C-statistics ranging from 0.70 to 0.90.
Only eight studies achieved external validation, raising concerns about model reliability.
High risk of bias identified in all studies, primarily due to restrictive inclusion criteria and single-center designs.

Abstract

Abstract Objective This systematic review aimed to systematically evaluate the methodological quality and predictive performance of existing prognostic models for diabetic kidney disease (DKD) outcomes. Methods A systematic search of PubMed, Web of Science, Cochrane Library, Ovid Embase, China National Knowledge Infrastructure (CNKI), Wanfang, Vip and SinoMed was conducted from database inception to 30 April 2026. Two reviewers independently screened studies, extracted data using the CHARMS checklist and assessed risk of bias with PROBAST. Results A total of 27 studies were included, comprising 80 prediction models. Among them, two were prospective studies and 25 were retrospective studies. The primary outcomes included renal‐related events, all‐cause mortality and cardiovascular endpoints. Twenty‐two studies used traditional regression methods, five applied machine learning algorithms and four combined both approaches for model development. The most frequently used predictors were age, estimated glomerular filtration rate (eGFR), urine–albumin–creatinine ratio (UACR), urinary total protein (UTP), body mass index (BMI), haemoglobin and systolic blood pressure (SBP). Only eight studies performed external validation, and most models showed acceptable discrimination, with area under the curve (AUC)/C‐statistic ranging from 0.70 to 0.90. All studies were judged to have a high risk of bias, and 21 were considered to have high concerns regarding applicability, mainly due to single‐centre design and restrictive inclusion criteria. Conclusions Current DKD prognostic models demonstrate acceptable discrimination but lack adequate calibration and external validation, highlighting the need for more rigorous study designs to enhance reliability and clinical applicability.

Ask AI

Helpful

Bookmark

View Full Paper