Validity of a New Prediction Model to Identify Patients at Risk for Obstructive Sleep Apnea Hypopnea Syndrome.

Autor:	Tawaranurak K; Department of Otolaryngology Head and Neck Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand., Kamolphiwong S; Computer Engineering Department, Faculty of Engineer, Prince of Songkla University, Hat Yai, Songkhla, Thailand., Sae-Wong S; Computer Engineering Department, Faculty of Engineer, Prince of Songkla University, Hat Yai, Songkhla, Thailand., Vasupongayya S; Computer Engineering Department, Faculty of Engineer, Prince of Songkla University, Hat Yai, Songkhla, Thailand., Kamolphiwong T; Computer Engineering Department, Faculty of Engineer, Prince of Songkla University, Hat Yai, Songkhla, Thailand., Bumrungsena C; Nursing Department, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand., Chaiyarukjirakun V; Department of Otolaryngology Head and Neck Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand.
Jazyk:	angličtina
Zdroj:	Ear, nose, & throat journal [Ear Nose Throat J] 2023 Jan; Vol. 102 (1), pp. 52-57. Date of Electronic Publication: 2021 Jan 04.
DOI:	10.1177/0145561320986045
Abstrakt:	Objectives: To develop and validate a new clinical prediction model for screening patients at risk for obstructive sleep apnea-hypopnea syndrome (OSAHS). Methods: This study used 2 data sets to develop and validate the model. To build the model, the first data set comprised 892 patients who had diagnostic polysomnography (PSG); data were assessed by multivariate logistic regression analysis. To validate the new model, the second data set comprised 374 patients who were enrolled to undergo overnight PSG. Receiver operating characteristic analysis and all predictive parameters were validated. Results: In the model development phase, univariate analysis showed 6 parameters were significant for prediction apnea-hypopnea index ≥15 events/hour: male sex, choking or apnea, high blood pressure, neck circumference >16 inches (female) or 17 inches (male), waist circumference ≥80 (female) or 90 cm (male), and body mass index >25 kg/m 2 . Estimated coefficients showed an area under the curve of 0.753. In the model validation phase, the sensitivity and specificity were approximately 93% and 26%, respectively, for identifying OSAHS. Comparison with the Epworth Sleepiness Scale score of ≥10 and STOP-Bang score ≥3 showed sensitivity of 42.26% and 56.23%, respectively, for detecting patients at risk. Conclusions: This new prediction model gives a better result on identifying patients at risk for OSAHS than Epworth Sleepiness Scale and STOP-Bang in terms of sensitivity. Moreover, this model may play a role in clinical decision-making for a comprehensive sleep evaluation to prioritize patients for PSG.
Databáze:	MEDLINE
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