TY - JOUR
T1 - Accuracy of ECG-based screening for sleep-disordered breathing
T2 - A survey of all male workers in a transport company
AU - Hayano, Junichiro
AU - Tsukahara, Teruomi
AU - Watanabe, Eiichi
AU - Sasaki, Fumihiko
AU - Kawai, Kiyohiro
AU - Sakakibara, Hiroki
AU - Kodama, Itsuo
AU - Nomiyama, Tetsuo
AU - Fujimoto, Keisaku
N1 - Funding Information:
Fig. 4 Algorithm of autocorrelated wave detection with adaptive threshold (ACAT). The algorithm detects the temporal positions of cyclic variation of heart rate (CVHR) in the interbeat interval time series as the cyclic and autocorrelated dips that meet four specific criteria (modified from Fig. 1 in Ref. [19]) Acknowledgments This work was supported by the Japan Society for the Promotion of Science, Japan [Grant-in-Aid for Scientific Research (C) 18590552 (to Dr. Nomiyama), 20590832, 23591055 (to Dr. Hayano)] and by the Ministry of Health, Labour and Welfare, Japan [Grant for the Respiratory Failure Research Group (to Dr. Fujimoto), Research Grant for Nervous and Mental Disorders 20B-7, 23-2 (to Dr. Hayano)].
PY - 2013/3
Y1 - 2013/3
N2 - Purpose: 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.
AB - Purpose: 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.
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U2 - 10.1007/s11325-012-0681-7
DO - 10.1007/s11325-012-0681-7
M3 - Review article
C2 - 22430527
AN - SCOPUS:84874190760
SN - 1520-9512
VL - 17
SP - 243
EP - 251
JO - Sleep and Breathing
JF - Sleep and Breathing
IS - 1
ER -
