Digital Morphometrics

Schwab, Richard J.; Leinwand, Sarah E; Bearn, Cary B.; Maislin, Greg; Rao, Ramya Bhat; Nagaraja, Adithya; Wang, Stephen; Keenan, Brendan T.

doi:10.1016/j.chest.2017.05.005

Cited by 37 publications

(18 citation statements)

References 33 publications

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“…A significant correlation between the oropharyngeal soft tissue (Mallampati test and tonsillar enlargement, etc) and AHI was reported. 20 21 However, the Mallampati test was not a Open access significant parameter in our study. It is possible that the association between soft tissue and OSA is weak in Asian populations.…”

Section: Discussioncontrasting

confidence: 58%

Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

Zhang

Yan

et al. 2021

BMJ Open

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ObjectivesObstructive sleep apnoea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Faciocervical characteristics may screen OSA for Asians due to smaller upper airways compared with Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate to severe OSA based on faciocervical and anthropometric measurements.DesignA cross-sectional study.SettingData were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.ParticipantsA total of 481 Chinese participants were included in the study.Primary and secondary outcome(1) Identification of moderate to severe OSA with apnoea–hypopnoea index 15 events/hour and (2) Verification of the machine-learning model.ResultsSex-Age-Body mass index (BMI)-maximum Interincisal distance-ratio of Height to thyrosternum distance-neck Circumference-waist Circumference (SABIHC2) model was set up. The SABIHC2 model could screen moderate to severe OSA with an area under the curve (AUC)=0.832, the sensitivity of 0.916 and specificity of 0.749, and performed better than the STOP-BANG (snoring, tiredness, observed apnea, high blood pressure, BMI, age, neck circumference, and male gender) questionnaire, which showed AUC=0.631, the sensitivity of 0.487 and specificity of 0.772. Especially for asymptomatic patients (Epworth Sleepiness Scale <10), the SABIHC2 model demonstrated better predictive ability compared with the STOP-BANG questionnaire, with AUC (0.824 vs 0.530), sensitivity (0.892 vs 0.348) and specificity (0.755 vs 0.809).ConclusionThe SABIHC2 machine-learning model provides a simple and accurate assessment of moderate to severe OSA in the Chinese population, especially for those without significant daytime sleepiness.

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Section: Discussioncontrasting

confidence: 58%

Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

Zhang

Yan

et al. 2021

BMJ Open

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“…Tongue size was larger in the patients with OSA than in the controls in unadjusted models controlled for age, sex, and race, but the difference was not significant when the models were controlled for BMI. 23 , 24 Weight loss leads to a reduction in tongue fat content and pharyngeal length, reinforcing the association between obesity and increased tongue volume through fat infiltration. 7 …”

Section: Discussionmentioning

confidence: 91%

“…Studies using CT and magnetic resonance imaging have shown that tongue fat infiltration and upper airway soft tissue volume are associated with obesity. 6 Schwab et al 23 analyzed digital photographs of 318 controls and 542 patients with OSA. Tongue size was larger in the patients with OSA than in the controls in unadjusted models controlled for age, sex, and race, but the difference was not significant when the models were controlled for BMI.…”

Section: Discussionmentioning

confidence: 99%

Tongue size matters: revisiting the Mallampati classification system in patients with obstructive sleep apnea

et al. 2023

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Objective: The Mallampati classification system has been used to predict obstructive sleep apnea (OSA). Upper airway soft tissue structures are prone to fat deposition, and the tongue is the largest of these structures. Given that a higher Mallampati score is associated with a crowded oropharynx, we hypothesized that the Mallampati score is associated with tongue volume and an imbalance between tongue and mandible volumes. Methods: Adult males underwent clinical evaluation, polysomnography, and upper airway CT scans. Tongue and mandible volumes were calculated and compared by Mallampati class. Results: Eighty patients were included (mean age, 46.8 years). On average, the study participants were overweight (BMI, 29.3 ± 4.0 kg/m2) and had moderate OSA (an apnea-hypopnea index of 26.2 ± 26.7 events/h). Mallampati class IV patients were older than Mallampati class II patients (53 ± 9 years vs. 40 ± 12 years; p < 0.01), had a larger neck circumference (43 ± 3 cm vs. 40 ± 3 cm; p < 0.05), had more severe OSA (51 ± 27 events/h vs. 24 ± 23 events/h; p < 0.01), and had a larger tongue volume (152 ± 19 cm3 vs. 135 ± 18 cm3; p < 0.01). Mallampati class IV patients also had a larger tongue volume than did Mallampati class III patients (152 ± 19 cm3 vs. 135 ± 13 cm3; p < 0.05), as well as having a higher tongue to mandible volume ratio (2.5 ± 0.5 cm3 vs. 2.1 ± 0.4 cm3; p < 0.05). The Mallampati score was associated with the apnea-hypopnea index (r = 0.431, p < 0.001), BMI (r = 0.405, p < 0.001), neck and waist circumference (r = 0.393, p < 0.001), tongue volume (r = 0.283, p < 0.001), and tongue/mandible volume (r = 0.280, p = 0.012). Conclusions: The Mallampati score appears to be influenced by obesity, tongue enlargement, and upper airway crowding.

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“…Otolaryngologists evaluated the scale of adenoid, tonsils, tongue, and oropharynx (see Supplemental Material). [16][17][18] Obstructive Sleep Apnea Questionnaire-18 (OSA-18)…”

Section: Physical Examinationmentioning

confidence: 99%

Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Wang¹,

Xu²,

Zhao³

et al. 2023

NSS

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Purpose Distinguishing obstructive sleep apnea (OSA) in a high-risk population remains challenging. This study aimed to investigate clinical features to identify children with OSA combined with craniofacial photographic analysis. Methods One hundred and forty-five children (30 controls, 62 with primary snoring, and 53 with OSA) were included. Differences in general demographic characteristics and surface facial morphology among the groups were compared. Risk factors and prediction models for determining the presence of OSA (obstructive sleep apnea-hypopnea index>1) were developed using logistic regression analysis. Results The BMI (z-score), tonsil hypertrophy, and lower face width (adjusted age, gender, and BMI z-score) were showed significantly different in children with OSA compared with primary snoring and controls (adjusted p<0.05). The screening model based on clinical features and photography measurements correctly classified 79.3% of the children with 64.2% sensitivity and 89.1% specificity. The area under the curve of the model was 81.0 (95% CI, 73.5–98.4%). Conclusion A screening model based on clinical features and photography measurements would be helpful in clinical decision-making for children with highly suspected OSA if polysomnography remains inaccessible in resource-stretched healthcare systems.

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Digital Morphometrics

Cited by 37 publications

References 33 publications

Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

Tongue size matters: revisiting the Mallampati classification system in patients with obstructive sleep apnea

Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

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