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VOLUME 17 , ISSUE 3 ( July-September, 2022 ) > List of Articles
Sucheta Sunil Bhalerao, Dhiraj Bhatkar, Ketaki Utpat, Unnati Desai
Keywords : Apnea–hypopnea index, Continuous positive airway pressure, Daytime sleepiness, Epworth sleepiness scale, Metabolic syndrome, Obesity, Obesity hypoventilation syndrome, Overlap Syndrome, Sleep-disordered breathing, Syndrome Z
Citation Information : Bhalerao SS, Bhatkar D, Utpat K, Desai U. Comparison of Various Pretest Probability Scores in Obstructive Sleep Apnea Syndrome. Indian Sleep Med 2022; 17 (3):77-82.
License: CC BY-NC 4.0
Published Online: 19-10-2022
Copyright Statement: Copyright © 2022; The Author(s).
Background: In Obstructive sleep apnea (OSA), there is upper airway collapsibility, and obstruction to the flow of air thereby leading to arousal. In view of the rising prevalence of OSA, there is a need for precise tools which are less complicated to identify patients with sleep disorder for early diagnosis and to prevent serious complications. Therefore, it is of utmost importance to classify patients depending on their clinical symptoms and examination and risk factors to identify patients at higher risk and the need for polysomnography (PSG) and further management. Materials and methods: This was a prospective study conducted with institutional ethics committee (IEC) permission, which included 100 patients referred to the Department of Pulmonary Medicine of our tertiary care center, Topiwala National Medical College and Bai Yamunabai Laxman Nair Charitable Hospital, Mumbai, Maharashtra, India. Specificity, sensitivity, and negative and positive predictive values of each pretest probability score [sleep apnea clinical score (SACS), snoring, tiredness, observed apnea, blood pressure, body mass index (BMI), age, neck size, gender (STOP-BANG) questionnaire, Berlin questionnaire (BQ), and four-variable tool (4-VT) questionnaire] were calculated. These scores were plotted against apnea–hypopnea Index (AHI) and the correlation coefficient of these pretest probability scores were compared with each other. Results: Among the 100 patients screened, 93 patients had OSA. Epworth sleepiness scale (ESS) classified that 87% of patients were at risk of OSA. Also, 70, 93, 93, and 49% were at higher risk by SACS, STOP-BANG, BQ, and 4-VT questionnaires, respectively. Conclusion: There is a need for screening patients with sleep-disordered breathing (SDB) to find out such patients and to refer such patients to higher centers where diagnostics facilities are available. Our study showed that various pretest probability questionnaires can be used to screen patients in resource-limited settings, such as primary and non-primary healthcare settings, where there was a limited number of sleep laboratories available.
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