title: Clinical patterns of obstructive sleep apnea and its comorbid conditions: a data mining approach.
abstract: OBJECTIVE
  Obstructive sleep apnea often results in a wide range of comorbid conditions. Although some conditions have been clearly identified as comorbid, a full clinical pattern of associated diseases has not been systematically documented. This research aimed to reveal the full pattern of comorbid conditions associated with OSA by employing a data mining technique.
  
  
  METHODS
  A large data repository (the New South Wales inpatient Data Collection) collected between 1999 and 2004 was mined, and all clinical diagnoses were coded with ICD-10-AM codes.
  
  
  RESULTS
  A total of 60,197 cases (4% of total records) were identified as related to OSA (72.2% males, 27.8% females). OSA occurrence showed 2 peaks at 0-4 years and 55-59 years. A strikingly low occurrence was observed for the adolescent years. Conditions comorbid with OSA in adults by descending frequency were essential hypertension, obesity, hypercholesterolemia, type 2 diabetes, past or current tobacco use, and ischemic heart conditions. Obesity and OSA showed a similar time course of onset, with a latent period of 5 years for hypertension and type 2 diabetes and 15 years for chronic ischemic heart conditions. Comorbid conditions were predominantly of the cardiovascular, endocrine/metabolic and respiratory systems. The data also indicated OSA patients are high users of health services.
  
  
  CONCLUSIONS
  The data mining technique confirms the prevalence of the disease, describes the age distribution patterns and time courses of disease development from obesity and OSAto comorbid conditions, and implicates possible interrelationships among these conditions and high cost of treating OSA patients.
authors.affiliation: Discipline of Health Informatics, Faculty of Health Sciences, The University of Sydney, Sydney, NSW Australia.
authors.forename: Qi Rong
authors.initials: QR
authors.lastname: Huang
authors.forename: Zhenxing
authors.initials: Z
authors.lastname: Qin
authors.forename: Shichao
authors.initials: S
authors.lastname: Zhang
authors.forename: Chin Moi
authors.initials: CM
authors.lastname: Chow
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D015992:Body Mass Index; D002648:Child; D002675:Child, Preschool; D002908:Chronic Disease / Q000453:epidemiology*; D015897:Comorbidity; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology*; D008875:Middle Aged; D009517:New South Wales / Q000453:epidemiology; D017286:Polysomnography; D015995:Prevalence; D012307:Risk Factors; D020181:Sleep Apnea, Obstructive / Q000453:epidemiology*; D055815:Young Adult