Defining common outcome metrics used in obstructive sleep apnea

Department of Otolaryngology and Communication Sciences, Division of Sleep Medicine, Medical College of Wisconsin, 9200 West Wisconsin Avenue, Milwaukee, WI 53226, USA.
Sleep Medicine Reviews (Impact Factor: 8.51). 01/2009; 12(6):449-61. DOI: 10.1016/j.smrv.2008.07.008
Source: PubMed


Sleep-disordered breathing a spectrum that ranges from snoring through disorder of increased airway resistance, to overt sleep apnea affects many clinical disease outcomes. Traditionally, disease outcomes have been measured by polysomnography, with the most common metric being the apnea hypopnea index (AHI). Multiple other clinical metrics are commonly used to assess the severity and impact of disease on important outcomes of obstructive sleep apnea (OSA). These allow assessment of sleepiness, quality of life, performance, and medical, especially cardiovascular outcomes. Currently the available metrics only partially explain the associated disease outcomes in different patients. This review highlights the available clinical, physiological and biomarker metrics in measuring OSA and associated co-morbidities and defines treatment goals.

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    • "Clinicians often face difficulty in identifying which patients are at risk of accidents because of the disparity between daytime symptoms and conventional metrics of disease severity [e.g. apnea hypopnea index (AHI)] (Beebe, 2005; Al-Shawwa et al., 2008; Quan et al., 2011). Moreover, the heterogeneity of impairment in the patient population adds to this problem -one patient may be relatively asymptomatic whereas another may be greatly compromised even though both have the same degree of disease measured by sleep study (Beebe, 2005). "
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    ABSTRACT: OBJECTIVE: To explore the use of detrended fluctuation analysis (DFA) scaling exponent of the awake electroencephalogram (EEG) as a new alternative biomarker of neurobehavioural impairment and sleepiness in obstructive sleep apnea (OSA). METHODS: Eight patients with moderate-severe OSA and nine non-OSA controls underwent a 40-h extended wakefulness challenge with resting awake EEG, neurobehavioural performance (driving simulator and psychomotor vigilance task) and subjective sleepiness recorded every 2-h. The DFA scaling exponent and power spectra of the EEG were calculated at each time point and their correlation with sleepiness and performance were quantified. RESULTS: DFA scaling exponent and power spectra biomarkers significantly correlated with simultaneously tested performance and self-rated sleepiness across the testing period in OSA patients and controls. Baseline (8am) DFA scaling exponent but not power spectra were markers of impaired simulated driving after 24-h extended wakefulness in OSA (r=0.738, p=0.037). OSA patients had a higher scaling exponent and delta power during wakefulness than controls. CONCLUSIONS: The DFA scaling exponent of the awake EEG performed as well as conventional power spectra as a marker of impaired performance and sleepiness resulting from sleep loss. SIGNIFICANCE: DFA may potentially identify patients at risk of neurobehavioural impairment and assess treatment effectiveness.
    Full-text · Article · Apr 2013 · Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology
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    • "Many serious complications arise from SAS, such as diminished quality of life brought on by chronic sleep deprivation and cardiovascular problems. The common measure used to describe respiratory disturbances during sleep is the Apnoea Hypopnoea Index (AHI), which is the total number of apnoea and hypopnoea episodes occurring during sleep divided by the hours of sleep time: mild (5-15 events per hour), moderate (15-30 events per hour) and severe (> 30 events per hour) [2]. "
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    ABSTRACT: Sleep apnoea syndrome (SAS) is a disease consisting in the nocturnal cessation of oronasal airflow at least 10 seconds in duration. The standard method for SAS diagnosis is the polysomnographic exam (PSG). However it does not permit a mass screening because it has high cost and requires long term monitoring.This paper presents a preliminary software system prototype for snoring signal analysis, whose main goal is to support the doctor in SAS diagnosis and patient follow-up. The design of the system is modular to allow a future hardware implementation in a portable device for personal snore collection and monitoring.
    Full-text · Article · Dec 2011 · Procedia Computer Science
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    ABSTRACT: This doctoral project seeks to answer the question about the essence of functioning, disability and health in the lived experience of persons with any kind of primary sleep disorder. Its overall objective is the development of a first version of Core Sets of categories of the International Classification of Functioning, Disability and Health (ICF) in an evidence- and consensus-based process. To this end, four separate studies exploring different perspectives (researcher, clinical, patient, health professional) have been conducted and their results provided the evidence basis for selecting the relevant categories for the ICF Core Sets for Sleep Disorders during an international consensus conference. The doctoral thesis first-authored by the doctoral candidate therefore consists of five separate publications (1 Systematic Review, 2 Patient Studies, 1 Expert Survey, 1 Conference Results) that describe the different steps in the development process.
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