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Canadian Respiratory Journal
Volume 22, Issue 4, Pages 215-220
Original Article

Use of Electronic Data and Existing Screening Tools to Identify Clinically Significant Obstructive Sleep Apnea

Carl A Severson,1 Sachin R Pendharkar,2,3 Paul E Ronksley,3 and Willis H Tsai2,3

1Cumming School of Medicine, Department of Medicine, University of Calgary, Calgary, Alberta, Canada
2Department of Community Health Sciences, Department of Medicine, University of Calgary, Calgary, Alberta, Canada
3Division of Respirology, Department of Medicine, University of Calgary, Calgary, Alberta, Canada

Copyright © 2015 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


OBJECTIVES: To assess the ability of electronic health data and existing screening tools to identify clinically significant obstructive sleep apnea (OSA), as defined by symptomatic or severe OSA.

METHODS: The present retrospective cohort study of 1041 patients referred for sleep diagnostic testing was undertaken at a tertiary sleep centre in Calgary, Alberta. A diagnosis of clinically significant OSA or an alternative sleep diagnosis was assigned to each patient through blinded independent chart review by two sleep physicians. Predictive variables were identified from online questionnaire data, and diagnostic algorithms were developed. The performance of electronically derived algorithms for identifying patients with clinically significant OSA was determined. Diagnostic performance of these algorithms was compared with versions of the STOP-Bang questionnaire and adjusted neck circumference score (ANC) derived from electronic data.

RESULTS: Electronic questionnaire data were highly sensitive (>95%) at identifying clinically significant OSA, but not specific. Sleep diagnostic testing-determined respiratory disturbance index was very specific (specificity ≥95%) for clinically relevant disease, but not sensitive (<35%). Derived algorithms had similar accuracy to the STOP-Bang or ANC, but required fewer questions and calculations.

CONCLUSIONS: These data suggest that a two-step process using a small number of clinical variables (maximizing sensitivity) and objective diagnostic testing (maximizing specificity) is required to identify clinically significant OSA. When used in an online setting, simple algorithms can identify clinically relevant OSA with similar performance to existing decision rules such as the STOP-Bang or ANC.