title: Accuracy of ECG-based screening for sleep-disordered breathing: a survey of all male workers in a transport company.
abstract: OBJECTIVE
  Sleep-disordered breathing (SDB) is associated with increased risk for cardiovascular morbidity and mortality and for sleepiness-related accidents, but >75 % of the patients remain undiagnosed. We sought to determine the diagnostic accuracy of ECG-based detection of SDB when used for population-based screening.
  
  
  METHODS
  All male workers, mostly truck drivers, of a transport company (n = 165; age, 43 ± 12 years) underwent standard attended overnight polysomnography. Cyclic variation of heart rate (CVHR), a characteristic pattern of heart rate associated with SDB, was detected from single-lead ECG signals during the polysomnography by a newly developed automated algorithm of autocorrelated wave detection with adaptive threshold (ACAT).
  
  
  RESULTS
  Among 165 subjects, the apnea-hypopnea index (AHI) was ≥5 in 62 (38 %), ≥15 in 26 (16 %), and ≥30 in 16 (10 %). The number of CVHR per hour (CVHR index) closely correlated with AHI [r = 0.868 (95 % CI, 0.825-0.901)]. The areas under the receiver operating characteristic curves for detecting subjects with AHI ≥5, ≥15, and ≥30 were 0.796 (95 % CI, 0.727-0.855), 0.974 (0.937-0.993), and 0.997 (0.971-0.999), respectively. With a predetermined criterion of CVHR index ≥15, subjects with AHI ≥15 were identified with 88 % sensitivity and 97 % specificity (likelihood ratios for positive and negative test, 30.7 and 0.12). The classification performance was retained in subgroups of subjects with obesity, hypertension, diabetes mellitus, dyslipidemia, and decreased autonomic function.
  
  
  CONCLUSIONS
  The CVHR obtained by the ACAT algorithm may provide a useful marker for screening for moderate-to-severe SDB among apparently healthy male workers.
authors.affiliation: Nagoya City University Graduate School of Medical Sciences, Aichi, Japan. hayano@med.nagoya-cu.ac.jp
authors.forename: Junichiro
authors.initials: J
authors.lastname: Hayano
authors.forename: Teruomi
authors.initials: T
authors.lastname: Tsukahara
authors.forename: Eiichi
authors.initials: E
authors.lastname: Watanabe
authors.forename: Fumihiko
authors.initials: F
authors.lastname: Sasaki
authors.forename: Kiyohiro
authors.initials: K
authors.lastname: Kawai
authors.forename: Hiroki
authors.initials: H
authors.lastname: Sakakibara
authors.forename: Itsuo
authors.initials: I
authors.lastname: Kodama
authors.forename: Tetsuo
authors.initials: T
authors.lastname: Nomiyama
authors.forename: Keisaku
authors.initials: K
authors.lastname: Fujimoto
mesh_terms: D000062:Accidents, Occupational / Q000517:prevention & control*; D000063:Accidents, Traffic / Q000517:prevention & control*; D000328:Adult; D000465:Algorithms; D015992:Body Mass Index; D015897:Comorbidity; D006970:Disorders of Excessive Somnolence / Q000175:diagnosis / Q000453:epidemiology; D004562:Electrocardiography; D006306:Health Surveys; D006801:Humans; D008297:Male; D008403:Mass Screening; D008875:Middle Aged; D018986:Motor Vehicles; D009784:Occupational Diseases / Q000175:diagnosis* / Q000453:epidemiology*; D017286:Polysomnography; D011237:Predictive Value of Tests; D012372:ROC Curve; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000453:epidemiology*