title: Development and validation of an electronic phenotyping algorithm for chronic kidney disease.
abstract: 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.
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Girish N
authors.initials: GN
authors.lastname: Nadkarni
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Omri
authors.initials: O
authors.lastname: Gottesman
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, WI.
authors.forename: James G
authors.initials: JG
authors.lastname: Linneman
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, WI.
authors.forename: Herbert
authors.initials: H
authors.lastname: Chase
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, WI.
authors.forename: Richard L
authors.initials: RL
authors.lastname: Berg
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Samira
authors.initials: S
authors.lastname: Farouk
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Rajiv
authors.initials: R
authors.lastname: Nadukuru
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Vaneet
authors.initials: V
authors.lastname: Lotay
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Steve
authors.initials: S
authors.lastname: Ellis
authors.affiliation: Columbia University Medical Center, New York, NY.
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.affiliation: Marshfield Clinic Research Foundation, Marshfield, WI.
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.affiliation: Columbia University Medical Center, New York, NY.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
authors.affiliation: Icahn School Of Medicine at Mount Sinai, New York, NY.
authors.forename: Erwin P
authors.initials: EP
authors.lastname: Bottinger
mesh_terms: D000465:Algorithms; D048909:Diabetes Complications; D057286:Electronic Health Records; D006801:Humans; D006973:Hypertension / Q000150:complications; D010641:Phenotype; D011237:Predictive Value of Tests; D051436:Renal Insufficiency, Chronic / Q000150:complications / Q000175:diagnosis*