BACKGROUND AND OBJECTIVES: There is a need for prediction scores that identify individuals at increased risk for developing progressive chronic kidney disease (CKD). Therefore, this study was performed to develop and validate a "renal risk score" for the general population. Design, setting, participants, & measurements For this study we used data from the PREVEND (Prevention of Renal and Vascular ENdstage Disease) study, a prospective population-based cohort study with a median follow-up of 6.4 years. Participants with two or three consecutive estimated GFR (eGFR) measurements during follow-up were included. Participants within the group who had the most renal function decline (top 20% of the total population) and had an eGFR value <60 ml/min per 1.73 m² during follow-up were defined as having progressive CKD. Possible predictors for progressive CKD were selected on the basis of univariable logistic regression analyses. RESULTS: A final prediction model was built using backward logistic regression analysis. Besides baseline eGFR, the model contained age, urinary albumin excretion, systolic BP, C-reactive protein, and known hypertension. The area under the receiver operating characteristic (ROC) curve was 0.84. We performed internal validation by using a bootstrapping procedure. As expected, after the regression coefficients were corrected for optimism, the area under the ROC curve was still 0.84. For clinical use we divided all predictors in meaningful clinical categories to develop a score chart. The area under the ROC curve was 0.83, indicating the high discriminative value of this model. CONCLUSIONS: Given the high internal validity of this renal risk score, this score can be helpful to identify individuals at increased risk for progressive CKD.
BACKGROUND AND OBJECTIVES: There is a need for prediction scores that identify individuals at increased risk for developing progressive chronic kidney disease (CKD). Therefore, this study was performed to develop and validate a "renal risk score" for the general population. Design, setting, participants, & measurements For this study we used data from the PREVEND (Prevention of Renal and Vascular ENdstage Disease) study, a prospective population-based cohort study with a median follow-up of 6.4 years. Participants with two or three consecutive estimated GFR (eGFR) measurements during follow-up were included. Participants within the group who had the most renal function decline (top 20% of the total population) and had an eGFR value <60 ml/min per 1.73 m² during follow-up were defined as having progressive CKD. Possible predictors for progressive CKD were selected on the basis of univariable logistic regression analyses. RESULTS: A final prediction model was built using backward logistic regression analysis. Besides baseline eGFR, the model contained age, urinary albumin excretion, systolic BP, C-reactive protein, and known hypertension. The area under the receiver operating characteristic (ROC) curve was 0.84. We performed internal validation by using a bootstrapping procedure. As expected, after the regression coefficients were corrected for optimism, the area under the ROC curve was still 0.84. For clinical use we divided all predictors in meaningful clinical categories to develop a score chart. The area under the ROC curve was 0.83, indicating the high discriminative value of this model. CONCLUSIONS: Given the high internal validity of this renal risk score, this score can be helpful to identify individuals at increased risk for progressive CKD.
Authors: Yolanda Hagar; David Albers; Rimma Pivovarov; Herbert Chase; Vanja Dukic; Noémie Elhadad Journal: Stat Anal Data Min Date: 2014-08-19 Impact factor: 1.051
Authors: Daniela Dunkler; Peggy Gao; Shun Fu Lee; Georg Heinze; Catherine M Clase; Sheldon Tobe; Koon K Teo; Hertzel Gerstein; Johannes F E Mann; Rainer Oberbauer Journal: Clin J Am Soc Nephrol Date: 2015-07-14 Impact factor: 8.237
Authors: Pankti A Gheewala; Syed Tabish R Zaidi; Matthew D Jose; Luke Bereznicki; Gregory M Peterson; Ronald L Castelino Journal: J Nephrol Date: 2017-02-08 Impact factor: 3.902
Authors: Priya Vart; Ron T Gansevoort; Josef Coresh; Sijmen A Reijneveld; Ute Bültmann Journal: Clin J Am Soc Nephrol Date: 2013-06-27 Impact factor: 8.237