Targeted cystatin C testing for large negative eGFR discordance in adults with diabetes: development and external validation of a machine-learning model

Background Large negative discordance between cystatin C- and creatinine-based estimated glomerular filtration rate (eGFR) may reveal clinically relevant renal-function differences that are not apparent from creatinine alone. When universal cystatin C testing is impractical, a method to prioritize patients for additional testing may improve testing efficiency. Methods We conducted a retrospective two-center study of adults with diabetes. Large negative eGFR discordance (D2) was defined as eGFRcys/eGFRcr <0.70. Seven machine-learning algorithms were evaluated in a development cohort of 1,170 participants (214 D2-positive), followed by SHAP-guided feature reduction. The final model was applied unchanged to an independent external validation cohort of 1,619 participants (112 D2-positive). Performance was compared with a prespecified clinical core (age, sex, and eGFRcr); additional analyses evaluated logistic regression using eGFRcr alone or with blood urea nitrogen and albumin. Prespecified cystatin C testing allocations of 10%-50% were evaluated using model-based risk ranking. Results The final nine-predictor XGBoost model achieved an AUROC of 0.863 (95% CI, 0.833-0.889) and an AUPRC of 0.600 (95% CI, 0.532-0.668) in development. In independent external validation, AUROC was 0.806 (95% CI, 0.769-0.840) and AUPRC was 0.258 (95% CI, 0.193-0.337), compared with 0.661 and 0.126, respectively, for the clinical core. At the 20% testing allocation, risk-guided testing identified 61.2% of D2 cases in development and 55.4% externally, with 3.06-fold and 2.77-fold enrichment over random testing. At this allocation in external validation, XGBoost identified 62 D2 cases versus 43 with the three-variable model (recall difference, 17.0 percentage points; 95% CI, 8.6-27.2). However, raw external probabilities overestimated D2 risk (mean predicted probability, 29.9% vs. observed prevalence, 6.9%), and the Brier score was higher than that of the clinical core (0.144 vs. 0.130). Conclusions In two cohorts of adults with diabetes who underwent paired testing, routine clinical laboratory data supported prioritization for large negative eGFR discordance. Model-based ranking may help target cystatin C testing in similar settings, but raw probabilities require local calibration before threshold-based decisions and prospective evaluation is needed before clinical implementation.