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• W1972239968 abstract "Obstructive sleep apnea–hypopnea (OSAH) increases the risk for metabolic syndrome, heart failure, myocardial infarction, stroke, sudden cardiac death and vehicular accidents. In spite of the wide prevalence of OSAH, physicians often miss the signs and symptoms of OSAH, thus a large number of undiagnosed patients remain at great risks. Furthermore, most effective methods of diagnosing OSAH or identifying its symptoms require the observation of a sleep technician, an overnight in-lab polysomnogram, and/or a portable home sleep-monitor which can be both costly and time consuming. As a result, we devised a method of predicting OSAH by assigning weighted numerical values that considers a patient’s age, history of snoring, Epworth Sleepiness Scale (ESS), body mass index (BMI), and upper airway structure (Mallampati classification). Purpose: To predict the presence and severity of OSAH (in the form of an AHI category) with a multilateral bedside scoring system wherein physicians with no training in sleep medicine may be able to use in order to enhance the awareness of OSAH and expedite referrals to sleep medicine specialists and sleep laboratories. Given known correlations of snoring, body mass index (BMI), Epworth Sleepiness Scale (ESS), Mallampati classification, and age with OSAH, we assigned simplified weighted values to the listed variable factors. The total Chan score is the sum of the weighted values that corresponds to each variable. Analyses of 315 patients (Male:Female – 175:140), picked at random with scored in-lab polysomnograms, were retrospectively correlated to individual factors and used to optimize the weighting of the Chan score. Ordinal regression analyses were carefully executed using AHI-categories (1 – AHI <5, 2 – AHI 5–14.99, 3 – AHI 15–29.99, 4 – AHI 30–49.99, 5 – AHI ⩾50) to obtain values for interpretation. We found that Age, ESS, Mallampati classification, BMI, and Snoring when analyzed individually proved to have less statistical significance in ordinal regression using AHI-categories (Age: p-value < 0.00001, ESS: p-value = 0.10, Mallampati: p-value = 0.007, Snoring: p-value = 0.02, BMI: p-value < 0.00001) than the multilateral Chan score (p-value < 0.0000000001) which accurately predicted over 50% of the population’s AHI-category (1–5) exactly, and diagnosed over 80% of the population’s OSAH. Our scoring system, Chan score, predicts the presence and severity of OSAH at the bedside and correlated remarkably well with polysomnogram results. It is a simple, valuable clinical tool for physicians, who may have no training in Sleep Medicine, to quickly identify patients who may have OSAH and predict its severity. ROSALIA CABE, RPSGT for scoring the sleep tests." @default.
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• W1972239968 date "2013-12-01" @default.
• W1972239968 modified "2023-09-27" @default.
• W1972239968 title "Chan score predicts presence and severity of sleep apnea at the bedside before polysomnogram sleep test" @default.
• W1972239968 doi "https://doi.org/10.1016/j.sleep.2013.11.203" @default.
• W1972239968 hasPublicationYear "2013" @default.
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