ArticlePloS one2018
SAS score: Targeting high-specificity for efficient population-wide monitoring of obstructive sleep apnea.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Knowledge, Beliefs and Preventive Practices Regarding Osteoporosis: A Cross-Sectional Study in Community Pharmacies in Tunis.Nutrients · 2025Article
- Inclusion of blood pressure parameter increases predictive capability of severe obstructive sleep apnea: A decision tree approach.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
- A New Screening Tool (BAMSA) for Sleep Apnea in Male Professional Truck Drivers.Journal of clinical medicine · 2024Article
- The Predictive Role of Subcutaneous Adipose Tissue in the Pathogenesis of Obstructive Sleep Apnoea.Life (Basel, Switzerland) · 2022Article
- Effects of age and sex on the performance of the NoSAS score as a screening tool for obstructive sleep apnea: a hospital-based retrospective study in China.Sleep & breathing = Schlaf & Atmung · 2021Article
- Analyzing Neck Circumference as an Indicator of CPAP Treatment Response in Obstructive Sleep Apnea with Network Medicine.Diagnostics (Basel, Switzerland) · 2021Article
- Immunization using a heterogeneous geo-spatial population model: A qualitative perspective on COVID-19 vaccination strategies.Procedia computer science · 2021Article
- Gender Phenotyping of Patients with Obstructive Sleep Apnea Syndrome Using a Network Science Approach.Journal of clinical medicine · 2020Article
- Particularities of Older Patients with Obstructive Sleep Apnea and Heart Failure with Mid-Range Ejection Fraction.Medicina (Kaunas, Lithuania) · 2019Article
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Authors and funding
7 authors.
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Abstract
proposalThis paper investigates a novel screening tool for Obstructive Sleep Apnea Syndrome (OSAS), which aims at efficient population-wide monitoring. To this end, we introduce SASscore which provides better OSAS prediction specificity while maintaining a high sensitivity.
methodsWe process a cohort of 2595 patients from 4 sleep laboratories in Western Romania, by recording over 100 sleep, breathing, and anthropometric measurements per patient; using this data, we compare our SASscore with state of the art scores STOP-Bang and NoSAS through area under curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We also evaluate the performance of SASscore by considering different Apnea-Hypopnea Index (AHI) diagnosis cut-off points and show that custom refinements are possible by changing the score's threshold.
resultsSASscore takes decimal values within the interval (2, 7) and varies linearly with AHI; it is based on standardized measures for BMI, neck circumference, systolic blood pressure and Epworth score. By applying the STOP-Bang and NoSAS questionnaires, as well as the SASscore on the patient cohort, we respectively obtain the AUC values of 0.69 (95% CI 0.66-0.73, p < 0.001), 0.66 (95% CI 0.63-0.68, p < 0.001), and 0.73 (95% CI 0.71-0.75, p < 0.001), with sensitivities values of 0.968, 0.901, 0.829, and specificity values of 0.149, 0.294, 0.359, respectively. Additionally, we cross-validate our score with a second independent cohort of 231 patients confirming the high specificity and good sensitivity of our score. When raising SASscore's diagnosis cut-off point from 3 to 3.7, both sensitivity and specificity become roughly 0.6.
conclusionsIn comparison with the existing scores, SASscore is a more appropriate screening tool for monitoring large populations, due to its improved specificity. Our score can be tailored to increase either sensitivity or specificity, while balancing the AUC value.
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