Identifying Cases of Sleep Disorders through International Classification of Diseases (ICD) Codes in Administrative Data

Abstract

ObjectivesPrevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. MethodsA cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). ResultsFrom a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. ConclusionSleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed.

The prevalence and impact of pre-existing sleep disorder diagnoses and objective sleep parameters in patients hospitalized for COVID-19

“…53 This electronic medical record search was conducted automatically with use of the University of Michigan's Data Direct program. 54 Specifically, we included diagnostic codes used for billing purposes in the ambulatory environment within 2 years prior to COVID-19 admission to best approximate the methodology used by Jolley and colleagues 55 that validated billing codes against sleep clinic diagnoses.…”

“…However, an investigation of sleep disorders codes (compared to the gold standard of sleep disorder confirmed by a sleep specialist) demonstrated that ICD-9 sleep disorder codes had a positive predictive value of 89% for confirmed sleep disorders. 55 The negative predictive value of the presence of an ICD-9 sleep disorders code was 16%; therefore, of greater concern is that use of diagnostic codes may not capture all patients with sleep disorders. 55 This limitation takes place on the backdrop of a decreased reporting of sleep related symptoms in racial/ ethnic minorities and the socioeconomically disadvantaged, 68 groups that are disproportionately affected by COVID-19.…”

“…55 The negative predictive value of the presence of an ICD-9 sleep disorders code was 16%; therefore, of greater concern is that use of diagnostic codes may not capture all patients with sleep disorders. 55 This limitation takes place on the backdrop of a decreased reporting of sleep related symptoms in racial/ ethnic minorities and the socioeconomically disadvantaged, 68 groups that are disproportionately affected by COVID-19. 14,69,70 Additionally, the time elapsed since the diagnosis was coded in the electronic health record was not appraised and could vary between individuals, although we restricted the use of the diagnosis codes to the 2 years prior to COVID-19 hospitalization.…”

Patient Reports of Night Noise in Hospitals Are Associated With Unplanned Readmissions Among Older Adults

“…Index visits included those discharged to home, either with or without support services (discharge disposition code of “4,” or “5”). To eliminate any potential confounding effects of sleep disorders upon our results, we eliminated any index cases where a sleep disorder was coded in any of the 25 DAD diagnosis positions ( 17 ). Unplanned readmissions were identified as subsequent visits with an admission category of “U” (urgent) in the DAD data set.…”

Development and Validation of a Clinical Prediction Model for Sleep Disorders in the ICU: A Retrospective Cohort Study

“…The sleep of ICU patients is generally disturbed, and the pattern of sleep disorders is apparently complicated (Kamdar et al, 2012;Boyko et al, 2017;Jolley et al, 2018). Specifically, non-specific sleep disorders, including their subtypes (e.g., insomnia, unspecified sleep apnea, sleep-wake circadian rhythm disorder, parasomnias, and other nonspecific sleep problems) are common problems that affect ICU Abbreviations: AUC, area under curve; CI, confidence Interval; DCA, decision curve analysis; GCS, Glasgow coma scale; ICD-9, International Classification of Diseases, Ninth Revision; ICU, intensive care unit; MIMIC-III, Medical Information Mart for Intensive Care III; OR, odds ratio; ROC, receiver operating characteristic curve; SAPS II, simplified acute physiology score II; SOFA, sequential organ failure assessment.…”