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Management of sleep apnea without high pretest probability or with comorbidities by three nights of portable sleep monitoring.

ABSTRACT: Obstructive sleep apnea (OSA) diagnosis using simplified methods such as portable sleep monitoring (PM) is only recommended in patients with a high pretest probability. The aim is to determine the diagnostic efficacy, consequent therapeutic decision-making, and costs of OSA diagnosis using polysomnography (PSG) versus three consecutive studies of PM in patients with mild to moderate suspicion of sleep apnea or with comorbidity that can mask OSA symptoms.Randomized, blinded, crossover study of 3 nights of PM (3N-PM) versus PSG. The diagnostic efficacy was evaluated with receiver operating characteristic (ROC) curves. Therapeutic decisions to assess concordance between the two different approaches were performed by sleep physicians and respiratory physicians (staff and residents) using agreement level and kappa coefficient. The costs of each diagnostic strategy were considered.Fifty-six patients were selected. Epworth Sleepiness Scale was 10.1 (5.3) points. Bland-Altman plot for apnea-hypopnea index (AHI) showed good agreement. ROC curves showed the best area under the curve in patients with PSG AHI ? 5 [0.955 (confidence interval = 0.862-0.993)]. For a PSG AHI ? 5, a PM AHI of 5 would effectively exclude and confirm OSA diagnosis. For a PSG AHI ? 15, a PM AHI ? 22 would confirm and PM AHI < 7 would exclude OSA. The best agreement of therapeutic decisions was achieved by the sleep medicine specialists (81.8%). The best cost-diagnostic efficacy was obtained by the 3N-PM.Three consecutive nights of portable monitoring at home evaluated by a qualified sleep specialist is useful for the management of patients without high pretest probability of obstructive sleep apnea or with comorbidities.http://www.clinicaltrials.gov, registration number: NCT01820156.Guerrero A, Embid C, Isetta V, Farre R, Duran-Cantolla J, Parra O, Barbé F, Montserrat JM, Masa JF. Management of sleep apnea without high pretest probability or with comorbidities by three nights of portable sleep monitoring.

SUBMITTER: Guerrero A

PROVIDER: S-EPMC4096206 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Management of sleep apnea without high pretest probability or with comorbidities by three nights of portable sleep monitoring.

Guerrero Arnoldo A Embid Cristina C Isetta Valentina V Farre Ramón R Duran-Cantolla Joaquin J Parra Olga O Barbé Ferran F Montserrat Josep M JM Masa Juan F JF

Sleep 20140801 8

<h4>Study objectives</h4>Obstructive sleep apnea (OSA) diagnosis using simplified methods such as portable sleep monitoring (PM) is only recommended in patients with a high pretest probability. The aim is to determine the diagnostic efficacy, consequent therapeutic decision-making, and costs of OSA diagnosis using polysomnography (PSG) versus three consecutive studies of PM in patients with mild to moderate suspicion of sleep apnea or with comorbidity that can mask OSA symptoms.<h4>Design and se ...[more]

PMID: 25083017

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Project description:BackgroundWe aimed to evaluate whether the severity of obstructive sleep apnea syndrome (OSAS) per se affects the prevalence of left ventricular (LV) diastolic dysfunction in patients without comorbidities.MethodsA total of 42 patients with first-diagnosed severe OSAS [apnea-hypopnea index (AHI) > 30] and 25 controls (AHI < 5), having been referred for snoring to the Sleep Laboratory Department of our tertiary Hospital, were enrolled in the study. Inclusion criteria were absence of any cardiovascular or oxidative stress-related comorbidities, and age between 20 and 70 years. Clinical, laboratory, echocardiographic, and polysomnographic data were recorded prospectively. Diastolic dysfunction diagnosis and grading was based on 2016 ASE/EACVI recommendations.ResultsSevere OSAS was associated with significantly increased prevalence and degree of diastolic dysfunction (26/42; 61.9%) compared with controls (7/25; 28%) (p = 0.007). AHI ⩾ 55 (dichotomous value of severe OSAS subset) was also characterized by greater prevalence and degree of diastolic dysfunction compared with 30 < AHI < 55 patients (p = 0.015). In the severe OSAS subset, age >45 years-old, height <1.745 m, body-mass index (BMI) >27.76 kg m-2, OSAS severity (AHI > 57.35), oxidative stress (overnight reduction of reduced to oxidized glutathione ratio < 18.44%), and BMI/height ratio > 16.155 kg m-3 (an index describing 'dense', short-heavy patients) presented significant diagnostic utility in identifying diastolic dysfunction in ROC-curve analysis (0.697 ⩾ AUC ⩾ 0.855, 0.001 ⩽ p ⩽ 0.018). In binary logistic regression model, advanced age (OR 1.23, 95% CI 1.025-1.477; p = 0.026) and AHI (OR 1.123, 95% CI 1.007-1.253; p = 0.036) showed independent association with diastolic dysfunction in severe OSAS.ConclusionsThe present prospective study may suggest that severe OSAS is significantly associated with LV diastolic dysfunction; OSAS clinical severity exerts a positive influence on (and possibly constitutes an independent risk factor of) LV diastolic dysfunction. The reviews of this paper are available via the supplementary material section.

Project description:Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis. Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture. Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference. Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.

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Management of sleep apnea without high pretest probability or with comorbidities by three nights of portable sleep monitoring.

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Management of sleep apnea without high pretest probability or with comorbidities by three nights of portable sleep monitoring.

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