Home Sleep Tests for Obstructive Sleep Apnea (OSA)

Kapoor, Mukesh; Greenough, Glen

doi:10.3122/jabfm.2015.04.140266

Cited by 39 publications

(30 citation statements)

References 44 publications

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“…61 Alternatively, patients can be "flagged" as having a high probability for undiagnosed OSA and proceed to surgery if delaying the surgery is deemed unacceptable.…”

Section: Pre-and Postoperative Managementmentioning

confidence: 99%

Obstructive Sleep Apnea: Preoperative Screening and Postoperative Care

Wolfe

Pomerantz

Miller

et al. 2016

The Journal of the American Board of Family Medicine

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The incidence of obstructive sleep apnea (OSA) has reached epidemic proportions, and it is an often unrecognized cause of perioperative morbidity and mortality. Profound hypoxic injury from apnea during the postoperative period is often misdiagnosed as cardiac arrest due to other causes. Almost a quarter of patients entering a hospital for elective surgery have OSA, and >80% of these cases are undiagnosed at the time of surgery. The perioperative period puts patients at high risk of apneic episodes because of drug effects from sedatives, narcotics, and general anesthesia, as well as from the effects of postoperative rapid eye movement sleep changes and postoperative positioning in the hospital bed.

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“…61 Alternatively, patients can be "flagged" as having a high probability for undiagnosed OSA and proceed to surgery if delaying the surgery is deemed unacceptable.…”

Section: Pre-and Postoperative Managementmentioning

confidence: 99%

Obstructive Sleep Apnea: Preoperative Screening and Postoperative Care

Wolfe

Pomerantz

Miller

et al. 2016

The Journal of the American Board of Family Medicine

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“…Although this is relatively higher than the rate of data loss that has commonly been reported (Kapoor & Greenough, 2015), data losses of up to 33% have been previously reported (Lux, Boehlecke, & Lohr, 2004). Although the prevalence of OSA when ODI 3% ≥ 5 events per hr was used seems high (72%), similar estimates have been previously reported in population-based studies (Senaratna et al, 2017;Tan et al, 2016) that used AASM 2012 scoring criteria (Berry et al, 2012), which uses the same desaturation levels.…”

Section: Discussionmentioning

confidence: 58%

Comparison of apnoea–hypopnoea index and oxygen desaturation index when identifying obstructive sleep apnoea using type‐4 sleep studies

Senaratna

Lowe

Perret

et al. 2018

Journal of Sleep Research

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Summary The concordance of different indices from type‐4 sleep studies in diagnosing and categorising the severity of obstructive sleep apnoea is not known. This is a critical gap as type‐4 sleep studies are used to diagnose obstructive sleep apnoea in some settings. Therefore, we aimed to determine the concordance between flow‐based apnoea–hypopnoea index (AHIflow50%) and oxygen desaturation index (ODI3%) by measuring them concurrently. Using a random sub‐sample of 296 from a population‐based cohort who underwent two‐channel type‐4 sleep studies, we assessed the concordance between AHIflow50% and ODI3%. We compared the prevalence of obstructive sleep apnoea of various severities as identified by the two methods, and determined their concordance using coefficient Kappa(κ). Participants were aged (mean ± SD) 53 ± 0.9 years (48% male). The body mass index was 28.8 ± 5.2 kg m−2 and neck circumference was 37.4 ± 3.9 cm. The median AHIflow50% was 5 (inter‐quartile range 2, 10) and median ODI3% was 9 (inter‐quartile range 4, 15). The obstructive sleep apnoea prevalence reported using AHIflow50% was significantly lower than that reported using ODI3% at all severity thresholds. Although 90% of those with moderate–severe obstructive sleep apnoea classified using AHIflow50% were identified by using ODI3%, only 46% of those with moderate–severe obstructive sleep apnoea classified using ODI3% were identified by AHIflow50%. The overall concordance between AHIflow50% and ODI3% in diagnosing and classifying the severity of obstructive sleep apnoea was only fair (κ = 0.32), better for males (κ = 0.42 [95% confidence interval 0.32–0.57] versus 0.22 [95% confidence interval 0.09–0.31]), and lowest for those with a body mass index ≥ 35 (κ = 0.11). In conclusion, ODI3% and AHIflow50% from type‐4 sleep studies are at least moderately discordant. Until further evidence is available, the use of ODI3% as the measure of choice for type‐4 sleep studies is recommended cautiously.

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“…The HST [26,27] is a kind of limited PSG that is made at home (i.e., portable equipment is transported to the patient's home). The number of channels used is often reduced to three: airflow, respiratory effort, and oximetry.…”

Section: Home Sleep Test (Hst)mentioning

confidence: 99%

Peer Review #1 of "A survey on sleep assessment methods (v0.2)"

2018

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Purpose.A literature review is presented that aims to summarize and compare current methods to evaluate sleep.Methods. Current sleep assessment methods have been classified according to different criteria; e.g., objective (polysomnography, actigraphy…) vs. subjective (sleep questionnaires, diaries…), contact vs. contactless devices, and need for medical assistance vs. self-assessment. A comparison of validation studies is carried out for each method, identifying their sensitivity and specificity reported in the literature. Finally, the state of the market has also been reviewed with respect to customers' opinions about current sleep apps. Results.A taxonomy that classifies the sleep detection methods. A description of each method that includes the tendencies of their underlying technologies analyzed in accordance with the literature. A comparison in terms of precision of existing validation studies and reports.Discussion. In order of accuracy, sleep detection methods may be arranged as follows: Questionnaire < Sleep diary < Contactless devices < Contact devices < PolysomnographyA literature review suggests that current subjective methods present a sensitivity between 73% and 97.7%, while their specificity ranges in the interval 50%-96%. Objective methods such as actigraphy present a sensibility higher than 90%. However, their specificity is low compared to their sensitivity, being one of the limitations of such technology. Moreover, there are other factors, such as the patient's perception of her or his sleep, that can be provided only by subjective methods. Therefore, sleep detection methods should be combined to produce a synergy between objective and subjective methods. The review of the market indicates the most valued sleep apps, but it also identifies problems and gaps, e.g., many hardware devices have not been validated and (especially software apps) should be studied before their clinical use.PeerJ reviewing PDF |

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Home Sleep Tests for Obstructive Sleep Apnea (OSA)

Cited by 39 publications

References 44 publications

Obstructive Sleep Apnea: Preoperative Screening and Postoperative Care

Obstructive Sleep Apnea: Preoperative Screening and Postoperative Care

Comparison of apnoea–hypopnoea index and oxygen desaturation index when identifying obstructive sleep apnoea using type‐4 sleep studies

Peer Review #1 of "A survey on sleep assessment methods (v0.2)"

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