Key result
Five out of six clinical prediction models for unknown chronic kidney disease demonstrated fair discrimination in a German general population, with c-values ranging from 0.71 to 0.73.
Why the study?
Screening populations at higher risk for CKD is effective to initiate earlier treatment, but clinical prediction models using non-invasive parameters required external validation.
Do non-invasive clinical prediction models accurately identify unknown chronic kidney disease in a general German population?
Observational (n=4,185)
Do non-invasive clinical prediction models accurately identify unknown chronic kidney disease in a general German population?
Effect estimate: AUC 0.67 to 0.73
Non-invasive clinical prediction models show fair discrimination for identifying unknown CKD in a general population and may be useful for targeted screening.
Supports external validation of CKD models in European cohorts; leaves open calibration, outcome impact, and screening utility.
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 unknown 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.
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Stolpe et al. (2021) conducted an observational in Unknown chronic kidney disease (CKD) (n=4,185). Six clinical prediction models (SCORED, modified SCORED, Kearns, Kshirsagar, Kwon, Thakkinstian) was evaluated on Model discrimination (c-statistic/AUC) for predicting unknown CKD (AUC 0.67 to 0.73). Five out of six clinical prediction models for unknown chronic kidney disease demonstrated fair discrimination in a German general population, with c-values ranging from 0.71 to 0.73.
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