Identification of insomnia in a sleep center population using electronic health data sources and the insomnia severity index.

STUDY
OBJECTIVES: To assess the validity and efficacy of using electronic health data to identify a physician diagnosis of insomnia in a population of patients referred for testing at a tertiary sleep center.
METHODS: Retrospective cohort study in a tertiary sleep center in Calgary, Alberta, Canada. Cohort consisted of 1,207 patients referred for sleep diagnostic testing and/or assessment by a sleep physician. Two sleep physicians independently assigned each patient a primary sleep diagnosis. Univariate logistic regression was used to identify variables that were predictive for insomnia from online questionnaire and diagnostic testing data. Diagnostic algorithms derived from these predictors and from the Insomnia Severity Index were evaluated against physician diagnosis as a reference standard.
RESULTS: The combination of self-reported sleep latency > 20 minutes, total sleep time < 6.5 hours per night, the inability to fall asleep after waking, BMI < 27 kg/m(2), and Epworth Sleepiness Scale score < 9 had very high specificity (99.3%) for diagnosing insomnia; however, sensitivity was poor (11.8%). Other algorithms derived from these data had either high sensitivity or high specificity. No combination of variables yielded simultaneous high sensitivity and specificity. Likewise, the Insomnia Severity Index can be highly sensitive or highly specific at identifying insomnia, but not both.
CONCLUSIONS: Diagnostic algorithms derived from electronic data can provide high specificity or high sensitivity for identifying insomnia.