Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. As members of eMERGE (electronic medical records and genomics) Network, we developed an automated phenotyping algorithm that can be deployed to identify rapidly diabetic and/or hypertensive CKD cases and controls in health systems with EMRs It uses diagnostic codes, laboratory results, medication and blood pressure records, and textual information culled from notes. Validation statistics demonstrated positive predictive values of 96% and negative predictive values of 93.3. Similar results were obtained on implementation by two independent eMERGE member institutions. The algorithm dramatically outperformed identification by ICD-9-CM codes with 63% positive and 54% negative predictive values, respectively.
['Nadkarni Gn', 'Omri Gottesman', 'James G. Linneman', 'Herbert S. Chase', 'Berg Rl', 'Farouk S', 'Rajiv Nadukuru', 'Lotay', 'Ellis S', 'George Hripcsak', 'Peggy L. Peissig', 'Chunhua Weng', 'Erwin P. Bottinger']
Development and validation of an electronic phenotyping algorithm for chronic kidney disease