Majid Afshar1, Valerie G Press2, Rachel G Robison3, Abel N Kho4, Sindhura Bandi5, Ashvini Biswas5, Pedro C Avila6, Harsha Vardhan Madan Kumar7, Byung Yu8, Edward T Naureckas2, Sharmilee M Nyenhuis9, Christopher D Codispoti5. 1. a Division of Pulmonary and Critical Care, Department of Medicine , Loyola University Chicago Stritch School of Medicine , Maywood , IL , USA. 2. b Section of General Internal Medicine, Departments of Medicine and Pediatrics , University of Chicago Medicine , Chicago , IL , USA. 3. c Division of Allergy/Immunology, Department of Pediatrics , Northwestern University Feinberg School of Medicine , Chicago , IL , USA. 4. d Department of Medicine, Center for Health Information Partnerships , Northwestern University Feinberg School of Medicine , Chicago , IL , USA. 5. e Department of Immunology and Microbiology, Allergy/Immunology Section , Rush University Medical Center , Chicago , IL , USA. 6. f Department of Medicine , Northwestern University , Chicago , IL , USA. 7. g Department of Pediatrics , University of Illinois at Chicago , Chicago , IL , USA. 8. h Department of Pediatrics , Division of Allergy/Immunology, Cook County Health & Hospital System , Chicago , IL , USA. 9. i Department of Medicine , University of Illinois at Chicago , Chicago , IL , USA.
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.
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.
Entities:
Keywords:
Asthma; algorithm; electronic health record
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