Clinical consequences and economic costs of untreated obstructive sleep apnea syndrome

Knauert, Melissa P.; Naik, Sreelatha; Gillespie, M. Boyd; Kryger, Meir H.

doi:10.1016/j.wjorl.2015.08.001

Cited by 213 publications

(138 citation statements)

References 105 publications

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“…In the United States, sleeping disorders are common, affecting about 10% of middle aged men and 3% women [1], with 80% of cases of at-least moderate OSA severity still undiagnosed [2]. Undiagnosed and untreated OSA results in severe ancillary medical outcomes, and is a massive economic burden; treating OSA patients after early diagnosis has been reported to save billions of dollars annually [3], and a better understanding and effective treatment of OSA can significantly improve society’s health.…”

Section: Introductionmentioning

confidence: 99%

Correlations Between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

et al. 2018

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PURPOSE The body mass index (BMI), an estimate of body fat, provides a rather imprecise indication of risk for obstructive sleep apnea (OSA). We examined whether other measures, including waist and neck circumference, provide improved indicators of risk in treatment-naïve OSA subjects. METHODS We studied 59 OSA subjects [age, 48.8±10.0 years; BMI, 31.9±6.6 kg/m2; apnea-hypopnea-index (AHI), 38.5±23.0 events/hour; sleep efficiency index (SEI, n=52), 78.6±14.4%; lowest oxygen saturation (SaO2 nadir), 79.5±8.0%; systolic blood pressure (BP), 127.4±15.7 mmHg; diastolic BP, 80.1±9.1 mmHg; 43 male), and determined waist and neck circumferences (waist, 107.4±15.3 cm; neck, 41.8±4.7 cm), daytime sleepiness [Epworth sleepiness scale (ESS), 8.7±4.6], sleep quality [Pittsburgh sleep quality index (PSQI), 8.5±4.1], depression levels [Beck depression inventory II (BDI-II), 6.6±5.7), and anxiety levels [Beck anxiety inventory (BAI), 6.2±7.2]. We used partial correlation procedures (covariates, age and gender) to examine associations between BMI, waist, and neck circumferences vs. AHI, sleep, and neuropsychological variables. RESULTS BMI, waist, and neck circumferences were significantly correlated with SaO2 nadir (BMI; r=−0.423, p=0.001; waist; r=−0.457, p<0.001; neck; r=−0.263, p=0.048), AHI (BMI; r=0.349, p=0.008; waist; r=0.459, p<0.001; neck; r=0.276, p=0.038), and systolic BP (BMI; r=0.354, p=0.007; waist; r=0.321, p=0.015; neck; r=0.388, p=0.003). SEI was significantly correlated with waist circumference (r=0.28, p=0.049), but higher with BMI (r=0.291, p=0.04). CONCLUSIONS No other significant waist or neck correlations emerged. This study suggests that waist and neck measures correlate better than BMI with select disease severity (SaO2 nadir and AHI) in OSA subjects. The findings offer an easily-measured, ancillary means to assess OSA risk.

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Section: Introductionmentioning

confidence: 99%

Correlations Between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

et al. 2018

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“…However, people in modern societies are suffering from inadequate sleep and its consequences6. Sleep loss is associated with impairment in cognitive performance, motor vehicle accidents, and poor quality of life7,8, and contributes to heightened socioeconomic burden9, 10. Beyond the quantity of sleep (sleep duration), quality of sleep includes sleep onset latency (i.e., time between going to bed and falling asleep), sleep efficiency (i.e., the percentage of time in bed, during which someone is asleep), sleep disturbances, use of sleeping medication, and daytime dysfunction, all interacting with individual health and well-being11,12.…”

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confidence: 99%

Phenotypic and Genetic Correlation Between Sleep, Behavior, and Macroscale Cortical Grey Matter

Tahmasian

Samea

Khazaie

et al. 2019

Preprint

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Humans need about 7 to 9 hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental and physical health.This raises the question whether associations between sleep, mental and physical health can be attributed to a shared macroscale neurobiology.Combining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (n=1887) we demonstrate phenotypic and genetic correspondence between sleep, intelligence, and BMI. Sleep was associated with local thickness variation in frontal, temporal, and occipital cortices. Using a comprehensive multivariate approach, we identified two robust latent components highlighting the interdigitation of sleep, intelligence, BMI, and depression and their shared relation to regions in unimodal and heteromodal association cortices. Latent relationships were heritable and driven by shared additive genetic factors. These observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.

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“…Our finding that ODI 3% identified over 90% of those classified as having moderate–severe OSA by AHI flow50% and that AHI flow50% failed to identify more than half of those classified as moderate–severe OSA by ODI 3% has significant clinical implications. Given the direct and indirect health and economic costs of OSA (Leger, Bayon, Laaban, & Philip, ; Wittmann & Rodenstein, ), including nearly a twofold increase in medical expenditure associated with undiagnosed OSA (Wittmann & Rodenstein, ) and billions of dollars of additional healthcare costs resulting from the consequences of undiagnosed OSA (Knauert, Naik, Gillespie, & Kryger, ), missing those who may benefit from treatment for OSA (Adult Obstructive Sleep Apnea Task Force of the American Academy of Sleep Medicine, ) also has important public health implications.…”

Section: Discussionmentioning

confidence: 99%

“…| 5 of 8 Wittmann & Rodenstein, 2004), including nearly a twofold increase in medical expenditure associated with undiagnosed OSA (Wittmann & Rodenstein, 2004) and billions of dollars of additional healthcare costs resulting from the consequences of undiagnosed OSA (Knauert, Naik, Gillespie, & Kryger, 2015), missing those who may benefit from treatment for OSA (Adult Obstructive Sleep Apnea Task Force of the American Academy of Sleep Medicine, 2009) also has important public health implications.…”

Section: Discussionmentioning

confidence: 99%

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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Clinical consequences and economic costs of untreated obstructive sleep apnea syndrome

Cited by 213 publications

References 105 publications

Correlations Between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

Correlations Between Waist and Neck Circumferences and Obstructive Sleep Apnea Characteristics

Phenotypic and Genetic Correlation Between Sleep, Behavior, and Macroscale Cortical Grey Matter

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

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