Validation of a risk prediction model for early chronic kidney disease in patients with type 2 diabetes: Data from the German/Austrian Diabetes Prospective Follow‐up registry
Authors' conclusion
The predictive model achieved moderate discrimination but good calibration in a German/Austrian T2D population, suggesting that the model may be relevant for determining CKD risk.
Study context
- Population
- Adults aged 39–75 years with T2D, normoalbuminuria and eGFR ≥60 ml/min/1.73m2, DPV registry; mean age 61 years, diabetes duration 6 years, body mass index (BMI) 31.7 kg/m², HbA1c 6.9%; 9.1% diabetic retinopathy, 16.3% smokers
- Participants
- 10,922 adults
- Comparison
- Risk categories: low vs moderate vs high vs very high risk
- Outcome
- CKD occurrence (model discrimination; prognostic factors: age, BMI, smoking status, HbA1c)
- Follow-up
- ≈59 months
Methodological detailsregistry cohort, external validation, German/Austrian DPV registry
- Registry cohort
- External validation
- German/Austrian DPV registry
Performance
As reported in the abstract
Developed CKD by risk group · after follow-up
- Developed chronic kidney disease (CKD), overall
- 37.4%
- AUC, unadjusted base model
- 0.58 (95% CI 0.57-0.59)
- AUC adjusted for diabetes and follow-up duration
- 0.69 (95% CI 0.68-0.70)
- Increasing risk score correlated with increasing cumulative risk of incident CKD over median 4.5 years
- P < 0.0001
Original publication
Validation of a risk prediction model for early chronic kidney disease in patients with type 2 diabetes: Data from the German/Austrian Diabetes Prospective Follow‐up registry. Diabetes Obes Metab. Published 20 December 2022. doi:10.
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