title: Identifying unrecognized airflow obstruction in cases with lifestyle-related diseases using a data mining system with electronic medical records.
abstract: BACKGROUND
  Airflow obstruction is the most important pathophysiologic factor in chronic obstructive pulmonary disease (COPD). Although the prevalence of airflow obstruction has been increasing worldwide, airflow obstruction is often under-recognized in clinical practice because of insufficient use of spirometry. The aim of the present study was to identify unrecognized airflow obstruction in cases with lifestyle-related diseases using a data mining system that we have developed for use with electronic medical records.
  
  
  METHODS
  Clinical, spirometric data on 27,111 patients aged 40 years or older treated during the period from January 1999 to December 2008 was retrospectively collected from the electronic medical records of the Nihon University School of Medicine clinical data warehouse. Airflow obstruction was defined according to the criteria established by the Global Initiative for Chronic Obstructive Lung Disease (GOLD).
  
  
  RESULTS
  Of the 27,111 patients, 6,098 (22.5%) showed airflow obstruction. Airflow obstruction was significantly more frequent in males, the elderly and smokers. The prevalence of airflow obstruction in patients with atherosclerosis (29.3%) was greater than that in patients with dyslipidemia (24.3%), diabetes mellitus (23.1%) or hypertension (27.1%). Only 14% of patients with airflow obstruction had a previous diagnosis of COPD.
  
  
  CONCLUSIONS
  Latent COPD patients with airflow obstruction are highly prevalent, not only in those over 70 years of age with lifestyle-related diseases, but also in middle-aged patients. Spirometry should be widely used for patients with lifestyle-related diseases and a history of smoking, to effectively detect undiagnosed COPD.
authors.affiliation: Division of Respiratory Medicine, Department of Internal Medicine, Nihon University School of Medicine, Itabashi-ku, Tokyo 173-8610, Japan.
authors.forename: Ken
authors.initials: K
authors.lastname: Matsumoto
authors.forename: Yasuo
authors.initials: Y
authors.lastname: Takahashi
authors.forename: Yasuhiro
authors.initials: Y
authors.lastname: Gon
authors.forename: Toshiki
authors.initials: T
authors.lastname: Akahoshi
authors.forename: Tomohiro
authors.initials: T
authors.lastname: Nakayama
authors.forename: Satoshi
authors.initials: S
authors.lastname: Asai
authors.forename: Shu
authors.initials: S
authors.lastname: Hashimoto
mesh_terms: D000328:Adult; D000368:Aged; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008019:Life Style; D008297:Male; D008875:Middle Aged; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis*; D013147:Spirometry