Risk factors and clinical prediction formula for the evaluation of obstructive sleep apnea in Asian adults

Abstract

Obstructive sleep apnea is a highly prevalent cyclic repetitive hypoxia-normoxia respiratory sleep disorder characterized by intermittent upper-airway collapse. It is mainly diagnosed using in-laboratory polysomnography. However, the time-spatial constraints of this procedure limit its application. To overcome these limitations, there have been studies aiming to develop clinical prediction formulas for screening of obstructive sleep apnea using the risk factors for this disorder. However, the applicability of the formula is restricted by the group specific factors included in it. Therefore, we aimed to assess the risk factors for obstructive sleep apnea and develop clinical prediction formulas, which can be used in different situations, for screening and assessing this disorder. We enrolled 3,432 Asian adult participants with suspected obstructive sleep apnea who had successfully undergone in-laboratory polysomnography. All parameters were evaluated using correlation analysis and logistic regression. Among them, age, sex, hypertension, diabetes mellitus, anthropometric factors, Berlin questionnaire and Epworth Sleepiness Scale scores, and anatomical tonsil and tongue position were significantly associated with obstructive sleep apnea. To develop the clinical formulas for obstructive sleep apnea, the participants were divided into the development (n = 2,516) and validation cohorts (n = 916) based on the sleep laboratory visiting date. We developed and selected 13 formulas and divided them into those with and without physical examination based on the ease of application; subsequently, we selected suitable formulas based on the statistical analysis and clinical applicability (formula including physical exam: sensitivity, 0.776; specificity, 0.757; and AUC, 0.835; formula without physical exam: sensitivity, 0.749; specificity, 0.770; and AUC, 0.839). Analysis of the validation cohort with developed formulas showed that these models and formula had sufficient performance and goodness of fit of model. These tools can effectively utilize medical resources for obstructive sleep apnea screening in various situations.

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review

“…The most recent study by Park et al [ 66 ], performed in 2021 with a validation data set of 916 participants (largest sample), only reached values of 78% and 76% for sensitivity and specificity, respectively, when compared with the 2 previous best models. This was also a logistic regression, electing BMI, age, sex, Berlin questionnaire score, and tonsil grade as the clinical factors for an OSA definition of AHI≥5.…”

Obstructive Sleep Apnea in a Clinical Population: Prevalence, Predictive Factors, and Clinical Characteristics of Patients Referred to a Sleep Center in Mongolia

“…OSA has been associated with numerous negative outcomes, including sleep disruption, daytime sleepiness, cognitive dysfunction, and cardiovascular disease [1]. There has been increasing concern regarding lapses in attention due to EDS, a major OSA symptom, leading to increased risk of traffic accidents and errors by workers that can cause serious industrial accidents [2][3][4]. Generally, OSA is diagnosed based on the apnea-hypopnea index (AHI), which indicates the number of apnea/hypopnea events per hour of sleep, wherein a pause in breathing lasting 10 s or longer with an associated decrease in blood oxygenation is considered an event.…”

Therapeutic effect of laparoscopic sleeve gastrectomy on obstructive sleep apnea and relationship of type 2 diabetes in Japanese patients with severe obesity

“…In addition, we showed that BCF significantly improved depending on the degree of OSA in type 2 diabetes patients with moderate/severe IMS. There are some influencing factors of inducing and progressing OSA, such as micrognathia, nasal septum deviation, tonsil swelling, adenoid hypertrophy and severe obesity [18][19][20][21] . Among them, severe obesity is known to be the most influential factor of OSA, because respiratory mechanical impairment leads to upper airway collapse during sleep 22 .…”