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

Background. Automated extraction of data from electronic health records has allowed high-quality retrospective analyses of large cohorts. 

Objectives. To derive and validate an au-tomated electronic search algorithm to identify surgical patients with a diagnosis of or at high risk for obstructive sleep ap-nea (OSA).

Methods. From 558 adult patients who underwent surgery from January 1, 2011, through December 31, 2015, we construct-ed a derivation cohort of 100 subjects se-lected 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 interro-gated results of a preoperative checklist that specifically queried patients regarding OSA history and screened for OSA risk us-ing 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 sensi-tivity and specificity were compared again with manual chart review of the respective group.

Results. In the derivation cohort, the auto-mated electronic algorithm achieved a sen-sitivity 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 compari-son.

Conclusion. An automated electronic search algorithm was developed that interrogates electronic health records to identi-fy, with a high degree of accuracy, surgical patients with a diagnosis of or at high risk for OSA. 

Flemons criteria, obstructive sleep apnea, search algorithm

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