Targeted cystatin C testing for large negative eGFR discordance in adults with diabetes: development and external validation of a machine-learning model
- 1Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University
- 2Department of Clinical Laboratory, Wanbei Coal Electric Group General Hospital
- 3Department of Hematology, The First Affiliated Hospital of Anhui Medical University
Abstract
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.
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BibTeX
@article{wang2026targeted,
title = {{Targeted cystatin C testing for large negative eGFR discordance in adults with diabetes: development and external validation of a machine-learning model}},
author = {Xuanxuan Wang and Mengyu Zhang and Ke Zhang and Yuanhong Xu and Yingying Chen and Yuanyuan Hu},
journal = {Frontiers in Endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.2002540},
url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.2002540/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Targeted cystatin C testing for large negative eGFR discordance in adults with diabetes: development and external validation of a machine-learning model
AU - Xuanxuan Wang
AU - Mengyu Zhang
AU - Ke Zhang
AU - Yuanhong Xu
AU - Yingying Chen
AU - Yuanyuan Hu
PY - 2026
DA - 2026/10/29
JO - Frontiers in Endocrinology
DO - 10.3389/fendo.2026.2002540
UR - https://www.frontiersin.org/articles/10.3389/fendo.2026.2002540/full
AB - 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.
SN - 1664-2392
ER -