A computable phenotype for asthma case identification in adult and pediatric patients: External validation in the Chicago Area Patient-Outcomes Research Network (CAPriCORN). (2nd September 2018)

• Record Type: Journal Article
• Authors: Afshar, Majid; Press, Valerie G.; Robison, Rachel G.; Kho, Abel N.; Bandi, Sindhura; Biswas, Ashvini; Avila, Pedro C.; Kumar, Harsha Vardhan Madan; Yu, Byung; Naureckas, Edward T.; Nyenhuis, Sharmilee M.; Codispoti, Christopher D.
• Is Part Of: Journal of asthma. Volume 55:Number 9(2018)
• Page Start: 1035
• Page End: 1042
• Publication Date: 2018-09-02
• Subjects: Asthma -- electronic health record -- algorithm; Asthma -- Periodicals; 616.238005
• DOI: 10.1080/02770903.2017.1389952
• Languages: English
• ISSNs: 0277-0903

Abstract

ABSTRACT: Objective : Comprehensive, rapid, and accurate identification of patients with asthma for clinical care and engagement in research efforts is needed. The original development and validation of a computable phenotype for asthma case identification occurred at a single institution in Chicago and demonstrated excellent test characteristics. However, its application in a diverse payer mix, across different health systems and multiple electronic health record vendors, and in both children and adults was not examined. The objective of this study is to externally validate the computable phenotype across diverse Chicago institutions to accurately identify pediatric and adult patients with asthma. Methods : A cohort of 900 asthma and control patients was identified from the electronic health record between January 1, 2012 and November 30, 2014. Two physicians at each site independently reviewed the patient chart to annotate cases. Results : The inter-observer reliability between the physician reviewers had a κ-coefficient of 0.95 (95% CI 0.93–0.97). The accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the computable phenotype were all above 94% in the full cohort. Conclusions : The excellent positive and negative predictive values in this multi-center external validation study establish a useful tool to identify asthma cases in in the electronic health record for research and care. This computable phenotype could be used in large-scale comparative-effectiveness trials.