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

Majid Afshar, Valerie G Press, Rachel G Robison, Abel N Kho, Sindhura Bandi, Ashvini Biswas, Pedro C Avila, Harsha Vardhan Madan Kumar, Byung Yu, Edward T Naureckas, Sharmilee M Nyenhuis, Christopher D Codispoti, Majid Afshar, Valerie G Press, Rachel G Robison, Abel N Kho, Sindhura Bandi, Ashvini Biswas, Pedro C Avila, Harsha Vardhan Madan Kumar, Byung Yu, Edward T Naureckas, Sharmilee M Nyenhuis, Christopher D Codispoti

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.

Keywords: Asthma; algorithm; electronic health record.

Conflict of interest statement

Declaration of Interest:

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Figures

Figure 1.
Figure 1.
Computable Asthma Phenotype
Figure 2.
Figure 2.
Medications that were used to identify asthma cases
Figure 3.
Figure 3.
a-c Area under the Receiver Operating Characteristic Curve for the Computable Asthma Phenotype across CAPriCORN institutions
Figure 3.
Figure 3.
a-c Area under the Receiver Operating Characteristic Curve for the Computable Asthma Phenotype across CAPriCORN institutions

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren