Helena Marti-Soler1, Camila Hirotsu2, Pedro Marques-Vidal3, Peter Vollenweider3, Gérard Waeber3, Martin Preisig4, Mehdi Tafti5, Sergio Brasil Tufik6, Lia Bittencourt2, Sergio Tufik2, José Haba-Rubio7, Raphael Heinzer8. 1. Institute of Social and Preventive Medicine, University of Lausanne, Lausanne, Switzerland. 2. Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, Brazil. 3. Department of Internal Medicine, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland. 4. Psychiatry Department, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland. 5. Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland; Center for Investigation and Research in Sleep, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland. 6. Medical School, Universidade de São Paulo, São Paulo, Brazil. 7. Center for Investigation and Research in Sleep, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland. Electronic address: jose.haba-rubio@chuv.ch. 8. Center for Investigation and Research in Sleep, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland; Pulmonary Department, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland. Electronic address: raphael.heinzer@chuv.ch.
Abstract
BACKGROUND: Diagnosis of sleep-disordered breathing requires overnight recordings, such as polygraphy or polysomnography. Considering the cost and low availability of these procedures, preselection of patients at high risk is recommended. We aimed to develop a screening tool allowing identification of individuals at risk of sleep-disordered breathing. METHODS: We used the participants from the population-based HypnoLaus cohort in Lausanne, Switzerland, who had a clinical assessment and polysomnography at home, to build a clinical score (the NoSAS score) using multiple factor analysis and logistic regression to identify people likely to have clinically significant sleep-disordered breathing. The NoSAS score was externally validated in an independent sleep cohort (EPISONO). We compared its performance to existing screening scores (STOP-Bang and Berlin scores). FINDINGS: We used the 2121 participants from the HypnoLaus cohort who were assessed between Sept 1, 2009, and June 30, 2013. The NoSAS score, which ranges from 0 to 17, allocates 4 points for having a neck circumference of more than 40 cm, 3 points for having a body-mass index of 25 kg/m(2) to less than 30 kg/m(2) or 5 points for having a body-mass index of 30 kg/m(2) or more, 2 points for snoring, 4 points for being older than 55 years of age, and 2 points for being male. Using a threshold of 8 points or more, the NoSAS score identified individuals at risk of clinically significant sleep-disordered breathing, with an area under the curve (AUC) of 0·74 (95% CI 0·72-0·76). It showed an even higher performance in the EPISONO cohort, with an AUC of 0·81 (0·77-0·85). The NoSAS score performed significantly better than did the STOP-Bang (AUC 0·67 [95% CI 0·65-0·69]; p<0·0001) and Berlin (0·63 [0·61-0·66]; p<0·0001) scores. INTERPRETATION: The NoSAS score is a simple, efficient, and easy to implement score enabling identification of individuals at risk of sleep-disordered breathing. Because of its high discrimination power, the NoSAS score can help clinicians to decide which patients to further investigate with a nocturnal recording. FUNDING: Faculty of Biology and Medicine of the University of Lausanne, Lausanne University Hospital, Swiss National Science Foundation, Leenaards Foundation, GlaxoSmithKline, and Vaud Pulmonary League.
BACKGROUND: Diagnosis of sleep-disordered breathing requires overnight recordings, such as polygraphy or polysomnography. Considering the cost and low availability of these procedures, preselection of patients at high risk is recommended. We aimed to develop a screening tool allowing identification of individuals at risk of sleep-disordered breathing. METHODS: We used the participants from the population-based HypnoLaus cohort in Lausanne, Switzerland, who had a clinical assessment and polysomnography at home, to build a clinical score (the NoSAS score) using multiple factor analysis and logistic regression to identify people likely to have clinically significant sleep-disordered breathing. The NoSAS score was externally validated in an independent sleep cohort (EPISONO). We compared its performance to existing screening scores (STOP-Bang and Berlin scores). FINDINGS: We used the 2121 participants from the HypnoLaus cohort who were assessed between Sept 1, 2009, and June 30, 2013. The NoSAS score, which ranges from 0 to 17, allocates 4 points for having a neck circumference of more than 40 cm, 3 points for having a body-mass index of 25 kg/m(2) to less than 30 kg/m(2) or 5 points for having a body-mass index of 30 kg/m(2) or more, 2 points for snoring, 4 points for being older than 55 years of age, and 2 points for being male. Using a threshold of 8 points or more, the NoSAS score identified individuals at risk of clinically significant sleep-disordered breathing, with an area under the curve (AUC) of 0·74 (95% CI 0·72-0·76). It showed an even higher performance in the EPISONO cohort, with an AUC of 0·81 (0·77-0·85). The NoSAS score performed significantly better than did the STOP-Bang (AUC 0·67 [95% CI 0·65-0·69]; p<0·0001) and Berlin (0·63 [0·61-0·66]; p<0·0001) scores. INTERPRETATION: The NoSAS score is a simple, efficient, and easy to implement score enabling identification of individuals at risk of sleep-disordered breathing. Because of its high discrimination power, the NoSAS score can help clinicians to decide which patients to further investigate with a nocturnal recording. FUNDING: Faculty of Biology and Medicine of the University of Lausanne, Lausanne University Hospital, Swiss National Science Foundation, Leenaards Foundation, GlaxoSmithKline, and Vaud Pulmonary League.
Authors: Lien Desteghe; Jeroen M L Hendriks; R Doug McEvoy; Ching Li Chai-Coetzer; Paul Dendale; Prashanthan Sanders; Hein Heidbuchel; Dominik Linz Journal: Clin Res Cardiol Date: 2018-04-12 Impact factor: 5.460
Authors: Adeline Tan; Yueheng Hong; Linda W L Tan; Rob M van Dam; Yan Yi Cheung; Chi-Hang Lee Journal: Sleep Breath Date: 2017-01-07 Impact factor: 2.816
Authors: Sara Q C Giampá; Rodrigo P Pedrosa; Carolina C Gonzaga; Adriana Bertolami; Celso Amodeo; Sofia F Furlan; Luiz A Bortolotto; Geraldo Lorenzi-Filho; Luciano F Drager Journal: J Hum Hypertens Date: 2018-05-23 Impact factor: 3.012
Authors: Ricardo L M Duarte; Marcelo F Rabahi; Tiago S Oliveira-E-Sá; Flavio J Magalhães-da-Silveira; Fernanda C Q Mello; David Gozal Journal: Lung Date: 2019-01-02 Impact factor: 2.584