Derivation and validation of an automated electronic search algorithm to identify patients at risk for obstructive sleep apnea

Abstract

Background. Automated extraction of data from electronic health records has allowed high-quality retrospective analyses of large cohorts. Objectives. To derive and validate an automated electronic search algorithm to identify surgical patients with a diagnosis of or at high risk for obstructive sleep apnea (OSA). Methods. From 558 adult patients who underwent surgery from January 1, 2011, through December 31, 2015, we constructed a derivation cohort of 100 subjects selected using the initial search algorithm to have equal numbers of patients with high and low likelihood of having OSA. This algorithm conducted a free-text electronic search of patient diagnoses and interrogated results of a preoperative checklist that specifically queried patients regarding OSA history and screened for OSA risk using Flemons criteria. The derivation cohort was then manually reviewed to identify patients with OSA risk and results were used to refine the algorithm. Second, the algorithm was validated with the other 458 patients (the validation cohort). The sensitivity and specificity were compared again with manual chart review of the respective group. Results. In the derivation cohort, the automated electronic algorithm achieved a sensitivity of 98.2% and a specificity of 100.0% compared with the manual review. In the validation cohort, sensitivity was 100.0% and specificity was 98.4% in this comparison.