AMIA Annu Symp Proc - Development and validation of an electronic phenotyping algorithm for chronic kidney disease.

Tópicos

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Resumo

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.

Resumo Limpo

twentysix million american estim chronic kidney diseas ckd increas risk cardiovascular diseas end stage renal diseas ckd frequent undiagnos patient unawar hamper intervent tool accur time identif ckd electron medic record emr improv healthcar qualiti identifi patient research member emerg electron medic record genom network develop autom phenotyp algorithm can deploy identifi rapid diabet andor hypertens ckd case control health system emr use diagnost code laboratori result medic blood pressur record textual inform cull note valid statist demonstr posit predict valu negat predict valu similar result obtain implement two independ emerg member institut algorithm dramat outperform identif icdcm code posit negat predict valu respect

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