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Titolo A Simple Algorithm to Predict Incident Kidney Disease
Autore Abhijit V. Kshirsagar, MD, MPH; Heejung Bang, PhD; Andrew S. Bomback, MD; Suma Vupputuri, PhD; David A. Shoham, PhD; Lisa M. Kern, MD, MPH; Philip J. Klemmer, MD; Madhu Mazumdar, PhD; Phyllis A. August, MD, MPH
Referenza Archives of Internal Medicine 2008; 168 (22): 2466-2473
Contenuto Abstract (abbreviated) Background Despite the growing burden of chronic kidney disease (CKD), there are no algorithms (to our knowledge) to quantify the effect of concurrent risk factors on the development of incident disease. Results The 3 prediction algorithms were continuous and categorical best-fitting models with 10 predictors and a simplified categorical model with 8 predictors. All showed discrimination with area under the receiver operating characteristic curve in a range of 0.69 to 0.70. In the simplified model, age, anemia, female sex, hypertension, diabetes mellitus, peripheral vascular disease, and history of congestive heart failure or cardiovascular disease were associated with the development of a GFR less than 60 mL/min/1.73 m2. A numeric score of at least 3 using the simplified algorithm captured approximately 70% of incident cases (sensitivity) and accurately predicted a 17% risk of developing CKD (positive predictive value). Conclusions An algorithm containing commonly understood variables helps to stratify middle-aged and older individuals at high risk for future CKD. The model can be used to guide population-level prevention efforts and to initiate discussions between practitioners and patients about risk for kidney disease.
Data 12.01.2009
 
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