The prediction of obstructive sleep apnea severity based on anthropometric and Mallampati indices.
Anthropometry
body mass index
decision trees
gender
obstructive sleep apnea
polysomnography
Journal
Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences
ISSN: 1735-1995
Titre abrégé: J Res Med Sci
Pays: India
ID NLM: 101235599
Informations de publication
Date de publication:
2019
2019
Historique:
received:
30
08
2018
revised:
26
10
2018
accepted:
02
05
2019
entrez:
17
9
2019
pubmed:
17
9
2019
medline:
17
9
2019
Statut:
epublish
Résumé
Obstructive sleep apnea (OSA) is a common health issue with serious complications. Regarding the high cost of the polysomnography (PSG), sensitive and inexpensive screening tools are necessary. The objective of this study was to evaluate the predictive value of anthropometric and Mallampati indices for OSA severity in both genders. In a cross-sectional study, we evaluated anthropometric data and the Mallampati classification for the patients ( About 54.1% of the patients were male. Mallampati, age, and NCs are important factors in predicting moderate OSA. The likelihood of moderate OSA severity based on Model 1 was 94.16%. In severe OSA, Mallampati, BMI, age, AC, and gender are more predictive. In Model 2, gender had a significant role. The likelihood of severe OSA based on Model 2 in female patients was 89.98% and in male patients was 90.32%. Comparison of the sensitivity and specificity of both models showed a higher sensitivity of Model 1 (93.5%) and a higher specificity of Model 2 (89.66%). For the prediction of moderate and severe OSA, anthropometric and Mallampati indices are important factors.
Sections du résumé
BACKGROUND
BACKGROUND
Obstructive sleep apnea (OSA) is a common health issue with serious complications. Regarding the high cost of the polysomnography (PSG), sensitive and inexpensive screening tools are necessary. The objective of this study was to evaluate the predictive value of anthropometric and Mallampati indices for OSA severity in both genders.
MATERIALS AND METHODS
METHODS
In a cross-sectional study, we evaluated anthropometric data and the Mallampati classification for the patients (
RESULTS
RESULTS
About 54.1% of the patients were male. Mallampati, age, and NCs are important factors in predicting moderate OSA. The likelihood of moderate OSA severity based on Model 1 was 94.16%. In severe OSA, Mallampati, BMI, age, AC, and gender are more predictive. In Model 2, gender had a significant role. The likelihood of severe OSA based on Model 2 in female patients was 89.98% and in male patients was 90.32%. Comparison of the sensitivity and specificity of both models showed a higher sensitivity of Model 1 (93.5%) and a higher specificity of Model 2 (89.66%).
CONCLUSION
CONCLUSIONS
For the prediction of moderate and severe OSA, anthropometric and Mallampati indices are important factors.
Identifiants
pubmed: 31523252
doi: 10.4103/jrms.JRMS_653_18
pii: JRMS-24-66
pmc: PMC6669992
doi:
Types de publication
Journal Article
Langues
eng
Pagination
66Déclaration de conflit d'intérêts
There are no conflicts of interest.
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