Performance of clinical prediction models for chronic kidney disease among people with diabetes: External validation using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)

Background Several clinical prediction models that predict the risk of chronic kidney disease (CKD) in people with diabetes have been developed; however, these models lack external validation demonstrating accurate predictions in Canadian primary care. We externally validated existing clinical prediction models for CKD in Canadian primary care data, overall and across subgroups defined by sex/gender, age, comorbidities, and neighbourhood-level deprivation. Methods We conducted a retrospective cohort study using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) electronic medical record database (2014–2019). We identified models that use demographic, health behaviour, clinical and diabetes-related characteristics to predict incident CKD based on two recent systematic reviews and included models with sufficient predictors in CPCSSN (≤1 unavailable) and eGFR-based CKD definitions. We included adult patients (18+) with diabetes without an existing diagnosis of CKD. We identified incident cases of CKD within 5 years based on ≥2 laboratory values corresponding to eGFR < 60 mL/min/1.73 m ² separated by ≥90 days and ≤1 year. For each model, we estimated the discrimination, precision, recall, and calibration within CPCSSN. Results Among 37,604 patients with diabetes, 14.6% met diagnostic criteria for CKD within 5 years. Overall performance of the 13 included CKD prediction models in CPCCSN was mixed: three models displayed moderate to strong discrimination (areas under the receiver-operating characteristic curves [AUROCs] > 0.70), whereas other AUROCs were as low as 0.508. After model updating, calibrations were heterogeneous with most models displaying some miscalibration. Some subgroups displayed considerable differences in performance: discriminative performance (AUROC) declined with increasing age and number of comorbidities, whereas the precision and recall improved with increasing age and number of comorbidities. We observed no difference in performance according to sex/gender or deprivation quintile. Conclusions Three models displayed moderate to strong performance predicting CKD among CPCSSN patients. Next, these models should be evaluated for their impact on practitioner and patient outcomes when implemented in clinical practice. If successful, these models hold promise in achieving widespread adoption to help identify those at highest risk of CKD and guide therapies that may prevent or delay CKD and related sequelae (e.g., end-stage renal disease) among people with diabetes.