Shuai He1,2,3, Yanru Li1,2,3, Wen Xu1,2,3, Dan Kang1,2,3, Hongguang Li1,2,3, Chunyan Wang1,2,3, Xiu Ding1,2,3, Demin Han1,2,3. 1. Beijing Tongren Hospital, Capital Medical University, Beijing, People's Republic of China. 2. Obstructive Sleep Apnea-Hypopnea Syndrome Clinical Diagnosis and Therapy and Research Centre, Capital Medical University, Beijing, People's Republic of China. 3. Key Laboratory of Otolaryngology Head and Neck Surgery, Ministry of Education, Capital Medical University, Beijing, People's Republic of China.
Abstract
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.
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.
Authors: Willis H Tsai; John E Remmers; Rollin Brant; W Ward Flemons; Jan Davies; Colin Macarthur Journal: Am J Respir Crit Care Med Date: 2003-01-24 Impact factor: 21.405
Authors: Richard J Schwab; Sarah E Leinwand; Cary B Bearn; Greg Maislin; Ramya Bhat Rao; Adithya Nagaraja; Stephen Wang; Brendan T Keenan Journal: Chest Date: 2017-05-17 Impact factor: 9.410
Authors: Stephen H Wang; Brendan T Keenan; Andrew Wiemken; Yinyin Zang; Bethany Staley; David B Sarwer; Drew A Torigian; Noel Williams; Allan I Pack; Richard J Schwab Journal: Am J Respir Crit Care Med Date: 2020-03-15 Impact factor: 21.405
Authors: Francisco Campos-Rodriguez; Carlos Queipo-Corona; Carmen Carmona-Bernal; Bernabe Jurado-Gamez; Jose Cordero-Guevara; Nuria Reyes-Nuñez; Fernanda Troncoso-Acevedo; Araceli Abad-Fernandez; Joaquin Teran-Santos; Julian Caballero-Rodriguez; Mercedes Martin-Romero; Ana Encabo-Motiño; Lirios Sacristan-Bou; Javier Navarro-Esteva; Maria Somoza-Gonzalez; Juan F Masa; Maria A Sanchez-Quiroga; Beatriz Jara-Chinarro; Belen Orosa-Bertol; Miguel A Martinez-Garcia Journal: Am J Respir Crit Care Med Date: 2016-11-15 Impact factor: 21.405