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

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.

Mallampati score and pediatric obstructive sleep apnea.

RATIONALE: Pediatric obstructive sleep apnea (OSA) is common, and a delay in diagnosis can lead to significant morbidity. Polysomnography (PSG) is the gold standard for the diagnosis of OSA. However, difficulty accessing PSG due to the relative shortage of sleep centers with pediatric expertise can lead to a delay in the diagnosis and management of OSA. OBJECTIVES: To assess the utility of Mallampati score (sitting and supine) in predicting the presence and severity of OSA in children. METHODS: A retrospective study of 158 children from a single pediatric sleep center. All patients had a PSG and a physical examination documenting Mallampati score. The Mallampati score, tonsillar size, age, sex, and apnea hypopnea index (AHI) were analyzed. Odds ratio of having pediatric OSA (AHI > 1) with increase in Mallampati score and tonsillar size were calculated. MEASUREMENTS AND MAIN RESULTS: A significant correlation was found between Mallampati score, tonsillar size, and AHI. For every point increase in the Mallampati score, the odds ratio of having OSA increased by more than 6-fold. For every point increase in tonsillar size, the odds ratio of having OSA increased by more than 2-fold. CONCLUSIONS: Mallampati score and tonsillar size are independent predictors of OSA. Oral examination including Mallampati score and tonsillar size should be considered when evaluating a patient for OSA. They can be used to prioritize children who may need PSG.