Identification and Validation of a Sickle Cell Disease Cohort Within Electronic Health Records. Acad Pediatr 2017 Apr;17(3):283-287
Date
12/17/2016Pubmed ID
27979750DOI
10.1016/j.acap.2016.12.005Scopus ID
2-s2.0-85017030687 (requires institutional sign-in at Scopus site) 21 CitationsAbstract
OBJECTIVE: To develop and validate a computable phenotype algorithm for identifying patient populations with sickle cell disease.
METHODS: In this retrospective study we used electronic health record data from the Children's Hospital of Wisconsin to develop a computable phenotype algorithm for sickle cell disease. The algorithm was on the basis of the International Classification of Diseases, Ninth Revision codes, number of visits, and hospital admissions for sickle cell disease. Using Informatics for Integrating Biology and the Bedside queries, the algorithm was refined in an iterative process. The final algorithm was verified using manual medical records review and by comparison with a gold standard set of confirmed sickle cell cases. The algorithm was then validated at Froedtert Hospital, a neighboring health system for adults.
RESULTS: From the Children's Hospital of Wisconsin, our computable phenotype algorithm identified patients with confirmed sickle cell disease with a positive predictive value of 99.4% and a sensitivity of 99.4%. Additionally, using data from Froedtert, the computable phenotype algorithm identified patients with confirmed sickle cell disease with a positive predictive value of 95.8% and a sensitivity of 98.3%.
CONCLUSIONS: The computable phenotype algorithm developed in this study had a high sensitivity and positive predictive value when identifying patients with sickle cell disease in the electronic health records of the Children's Hospital of Wisconsin and Froedtert, a neighboring health system for adults. Our algorithm allows us to harness data provided by the electronic health record to rapidly and accurately identify patient with sickle cell disease and is a rich resource for future clinical trials.
Author List
Michalik DE, Taylor BW, Panepinto JAAuthor
Bradley W. Taylor Chief Research Informatics Officer in the Clinical and Translational Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AdolescentAlgorithms
Anemia, Sickle Cell
Child
Child, Preschool
Cohort Studies
Electronic Health Records
Female
Hospitalization
Hospitals, Pediatric
Humans
Infant
Infant, Newborn
Male
Phenotype
Predictive Value of Tests
Retrospective Studies
Wisconsin