External validation of six clinical models for prediction of chronic kidney disease in a German population

Abstract

Abstract Background Chronic kidney disease (CKD) is responsible for large personal health and societal burdens. Screening populations at higher risk for CKD is effective to initiate earlier treatment and decelerate disease progress. We externally validated clinical prediction models for unknown CKD that might be used in population screening. Methods We validated six risk models for prediction of CKD using only non-invasive parameters. Validation data came from 4,185 participants of the German Heinz-Nixdorf-Recall study (HNR), drawn in 2000 from a general population aged 45–75 years. We estimated discrimination and calibration using the full model information, and calculated the diagnostic properties applying the published scoring algorithms of the models using various thresholds for the sum of scores. Results The risk models used four to nine parameters. Age and hypertension were included in all models. Five out of six c-values ranged from 0.71 to 0.73, indicating fair discrimination. Positive predictive values ranged from 15 to 19%, negative predictive values were > 93% using score thresholds that resulted in values for sensitivity and specificity above 60%. Conclusions Most of the selected CKD prediction models show fair discrimination in a German general population. The estimated diagnostic properties indicate that the models are suitable for identifying persons at higher risk for unknown CKD without invasive procedures.

Keywords

• Chronic Kidney Disease
• Prediction Model Screening
• External Validation Sensitivity
• Specificity ROC Curve