(Circulation. 1999;100:1411-1415.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
Screening of Obstructive Sleep Apnea Syndrome by Heart Rate Variability Analysis
From Service d'Exploration Fonctionnelle CardioRespiratoire, Laboratoire de Physiologie (F.R., V.P., D.D., F.C., J.-C.B.) and Service de Pneumologie et d'Oncologie Thoracique (I.C.-F.), CHU Nord, Faculté de Médecine Jacques Lisfranc, Université Jean Monnet, Saint-Etienne, France; Clinique de Médecine II et Division de Cardiologie, Département de Médecine Interne, Hôpitaux Universitaires, Geneva, Switzerland (J.-M.G.); Ecole Nationale de Statistiques et d'Analyse de l'Information, Rennes, France (P.M.); and Laboratoire de Physiologie, Faculté de Médecine Lyon Sud, Université Lyon I, Oullins, France (J.-R.L.).
Background—Enhanced nocturnal heart rate variability (HRV) has been evoked in sleep-related breathing disorders. However, its capacity to detect obstructive sleep apnea syndrome (OSAS) has not been systematically determined. Thus, we evaluated the discriminant power of HRV parameters in a first group of patients (G1) and validated their discriminant capacity in a second group (G2).
Methods and Results—In G1, 39 of 91 patients (42.8%) were
identified as diseased by polysomnography, as were 24 of 52 patients
(46%) in G2. Time-domain HRV variables (SD of NN intervals
[SDNN], mean of the standard deviations of all NN intervals for all
consecutive 5-minute segments of the recording [SDNN index], square
root of the mean of the sum of the squares of differences between
adjacent normal RR intervals [r-MSSD], and SD of the averages of NN
intervals in all 5-minute segments of the recording [SDANN])
were calculated for daytime and nighttime periods, as well as the
differences between daytime and nighttime values (
[D/N]).
Correlations between HRV variables and OSAS status were
analyzed in G1 by use of receiver-operating characteristic
(ROC) curves and logistic regression analysis. By ROC curve
analysis, 7 variables were significantly associated with
OSAS. After adjustment for other variables through multiple
logistic regression analysis, [D/N]SDNN index and
[D/N] r-MSSD remained significant independent predictors of OSAS,
with ORs of 8.22 (95% CI, 3.16 to 21.4) and 2.86 (95% CI, 1.21 to
6.75), respectively. The classification and regression tree methodology
demonstrated a sensitivity reaching 89.7% (95% CI, 73.7 to 97.7) with
[D/N] SDNN index and a specificity of 98.1% (95% CI, 86.4 to
100) with [D/N] SDNN using appropriate thresholds. These
thresholds, applied to G2, yielded a sensitivity of 83% using
[D/N] SDNN index and a specificity of 96.5% using [D/N]
SDNN.
Conclusions—Time-domain HRV analysis may represent an accurate and inexpensive screening tool in clinically suspected OSAS patients and may help focus resources on those at the highest risk.
Key Words: sleep • heart rate • nervous system, autonomic
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