Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2021
The predictive value of photogrammetry for obstructive sleep apnea.
СтатьяНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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He Shuai, Li Yanru, Xu Wen, Kang Dan, Li Hongguang, Wang Chunyan, Ding Xiu, Han Demin
Аннотация
STUDY OBJECTIVES: The aim of this study was to develop a prediction model for obstructive sleep apnea (OSA) based on photographic measurements of upper airway structures and to compare this to the model based on general physical examination.
METHODS: Participants with suspected OSA were recruited consecutively. General physical examination and photography of the oropharyngeal structures were performed prior to polysomnography. Logistic regression analysis was used to establish the prediction models.
RESULTS: A total of 197 eligible participants were included, and 74% were confirmed with OSA. The logistic regression model consisted of 4 photographic measurements (tongue area, uvula area, frenulum length, and retroposition distance) that classified 82.7% of the participants correctly and had 85.6% (95% confidence interval, 78.9-90.9%) sensitivity and 84.3% (95% confidence interval, 71.4-93.0%) specificity at the best cutoff point (0.71). The area under the receiver operating characteristics curve of the model was 0.90, which was higher than that of the model based on general physical measurements alone (area under the curve 0.80). The prediction performance further improved when photographic and general physical measurements were combined (area under the curve 0.93).
CONCLUSIONS: Detailed abnormality data of upper airway structures in OSA could be provided by photogrammetry. Prediction models comprising photographic measurements could be useful in the prediction of OSA.
CLINICAL TRIAL REGISTRATION: Registry: Chinese Clinical Trial Registry; Name: Mechanisms of cessation of respiratory events in patients with different phenotypes of obstructive sleep apnea; URL: http://www.chictr.org.cn/historyversionpuben.aspx?regno=ChiCTR2000031748; Identifier: ChiCTR2000031748.
METHODS: Participants with suspected OSA were recruited consecutively. General physical examination and photography of the oropharyngeal structures were performed prior to polysomnography. Logistic regression analysis was used to establish the prediction models.
RESULTS: A total of 197 eligible participants were included, and 74% were confirmed with OSA. The logistic regression model consisted of 4 photographic measurements (tongue area, uvula area, frenulum length, and retroposition distance) that classified 82.7% of the participants correctly and had 85.6% (95% confidence interval, 78.9-90.9%) sensitivity and 84.3% (95% confidence interval, 71.4-93.0%) specificity at the best cutoff point (0.71). The area under the receiver operating characteristics curve of the model was 0.90, which was higher than that of the model based on general physical measurements alone (area under the curve 0.80). The prediction performance further improved when photographic and general physical measurements were combined (area under the curve 0.93).
CONCLUSIONS: Detailed abnormality data of upper airway structures in OSA could be provided by photogrammetry. Prediction models comprising photographic measurements could be useful in the prediction of OSA.
CLINICAL TRIAL REGISTRATION: Registry: Chinese Clinical Trial Registry; Name: Mechanisms of cessation of respiratory events in patients with different phenotypes of obstructive sleep apnea; URL: http://www.chictr.org.cn/historyversionpuben.aspx?regno=ChiCTR2000031748; Identifier: ChiCTR2000031748.