Accuracy of ECG-based screening for sleep-disordered breathing: a survey of all male workers in a transport company.

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.

Interactive associations of depression and sleep apnea with adverse clinical outcomes after acute myocardial infarction.

OBJECTIVE: Depression and sleep apnea (SA) are common among patients with a recent acute myocardial infarction (AMI), and both are associated with increased risk for adverse outcomes. We tested the hypothesis that there is an interaction between them in relation to post-AMI prognosis. METHODS: Participants were patients with a recent AMI, 337 of them were depressed and 379 were nondepressed, who participated in a substudy of the Enhancing Recovery in Coronary Heart Disease (ENRICHD) clinical trial. SA was identified from Holter electrocardiogram by an algorithm that detects cyclic variation of heart rate. RESULTS: During a median follow-up of 25 months, 83 (11.6%) patients either died or experienced a recurrent AMI and 43 (6.0%) patients died. Among 94 patients with both depression and SA, these end points occurred in 25 (26.6%) and 20 (21.3%) at 3.9- and 6.9-times higher prevalence than predicted probabilities by ENRICHD clinical risk scores (p <.001 for both). In the patients with depression alone, SA alone, or neither, the prevalence was similar to the predicted probability. Depression and SA showed significant interactions in prediction of these end points (p = .02 and p = .03). SA independently predicted these end points in patients with depression (p = .001 and p <.001) but not in those without depression (p = .84 and p = .73). Similarly, depression independently predicted these end points in patients with SA (p <.001 for both) but not in those without SA (p = .12 and p = .61). CONCLUSIONS: Depression and SA are interactively associated with adverse clinical outcomes after AMI. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00313573.

Screening for obstructive sleep apnea by cyclic variation of heart rate.

BACKGROUND: Despite the adverse cardiovascular consequences of obstructive sleep apnea, the majority of patients remain undiagnosed. To explore an efficient ECG-based screening tool for obstructive sleep apnea, we examined the usefulness of automated detection of cyclic variation of heart rate (CVHR) in a large-scale controlled clinical setting. METHODS AND RESULTS: We developed an algorithm of autocorrelated wave detection with adaptive threshold (ACAT). The algorithm was optimized with 63 sleep studies in a training cohort, and its performance was confirmed with 70 sleep studies of the Physionet Apnea-ECG database. We then applied the algorithm to ECGs extracted from all-night polysomnograms in 862 consecutive subjects referred for diagnostic sleep study. The number of CVHR per hour (the CVHR index) closely correlated (r=0.84) with the apnea-hypopnea index, although the absolute agreement with the apnea-hypopnea index was modest (the upper and lower limits of agreement, 21 per hour and -19 per hour) with periodic leg movement causing most of the disagreement (P<0.001). The CVHR index showed a good performance in identifying the patients with an apnea-hypopnea index ≥15 per hour (area under the receiver-operating characteristic curve, 0.913; 83% sensitivity and 88% specificity, with the predetermined cutoff threshold of CVHR index ≥15 per hour). The classification performance was unaffected by older age (≥65 years) or cardiac autonomic dysfunction (SD of normal-to-normal R-R intervals over the entire length of recording <65 ms; area under the receiver-operating characteristic curve, 0.915 and 0.911, respectively). CONCLUSIONS: The automated detection of CVHR with the ACAT algorithm provides a powerful ECG-based screening tool for moderate-to-severe obstructive sleep apnea, even in older subjects and in those with cardiac autonomic dysfunction.

Diagnosis of sleep apnea by the analysis of heart rate variation: a mini review.

Cyclic variation of heart rate (CVHR) associated with sleep apnea/hypopnea episodes has been suggested as a marker of sleep disordered breathing (SDB). This study examined the utility of ECG-based CVHR detection for diagnosing SDB using simultaneous polysomnography as the reference standard. We used a previously developed automated CVHR detection algorithm (autocorrelated wave detection with adaptive threshold, ACAT) that provides the number of CVHR per hour (CVHR index). The ACAT was refined using a polysomnographic database of 194 subjects with various severities of SDB and then, applied to a single channel ECG obtained during standard overnight polysomnography in 862 consecutive subjects referred for SDB diagnosis. Using multiple thresholds of CVHR index ≥ 38 and <27, positive and negative predictive values of 95.6% and 95.1%, respectively, were achieved for detecting and excluding subjects with apnea-hypopnea index (AHI) ≥ 30, leaving 58 (6.7%) unclassified subjects. Positive and negative likelihood ratios (LRs) were 97.3 and 0.23, respectively. Also, thresholds of CVHR index ≥ 29 and <7 provided 96.1% and 95.1% of positive and negative predictive values, respectively, for subjects with AHI ≥ 15 (LRs, 50.6 and 0.11), leaving 426 (49.4%) unclassified subjects. The CVHR correlated with the AHI (r = 0.86) and showed the limits of agreement with the AHI of 19.6 and -18.6. Automated detection of CVHR by the ACAT algorithm provides useful screening tool for both increasing and decreasing probability of moderate and sever SDB with adequate thresholds.