Qual Life Res DOI 10.1007/s11136-015-0923-9
BRIEF COMMUNICATION
The STOP-BANG questionnaire: reliability and validity of the Persian version in sleep clinic population Khosro Sadeghniiat-Haghighi • Ali Montazeri • Ahmad Khajeh-Mehrizi Mahsa Ghajarzadeh • Zahra Banafsheh Alemohammad • Omid Aminian • Mojtaba Sedaghat
•
Accepted: 14 January 2015 Ó Springer International Publishing Switzerland 2015
Abstract Purpose The snoring, tiredness, observed apnea, blood pressure, body mass index, age, neck circumference, gender (STOP-BANG) is a concise and effective obstructive sleep apnea (OSA) screening tool, part questionnaire (STOP), and part demographic or anthropometric measurements (BANG). The main purpose of this study was to translate this well-liked questionnaire into Persian and assess its reliability and validity in sleep clinic population. Methods Standard forward–backward method was used for translation. A sample of 603 patients, who admitted to the sleep clinic, completely answered to the STOP questionnaire
K. Sadeghniiat-Haghighi A. Khajeh-Mehrizi Z. B. Alemohammad Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran K. Sadeghniiat-Haghighi Z. B. Alemohammad O. Aminian Center for Research on Occupational Diseases, Tehran University of Medical Sciences, Tehran, Iran A. Montazeri Mental Health Research Group, Health Metrics Research Centre, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran A. Khajeh-Mehrizi (&) Department of Internal Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran e-mail: [email protected]
and underwent in-laboratory polysomnography, included in this study. Height, weight, and neck circumference were measured by technicians for calculating BANG score. The apnea–hypopnea index (AHI) on the polysomnography was used as gold standard for OSA diagnosis: none (AHI \ 5), mild (5 B AHI \ 15), moderate (15 B AHI \ 30), and severe (AHI C 30). One hundred and forty one patients were answered to the STOP questionnaire twice at a time interval of 2–4 weeks for test–retest analysis. Results In reliability analysis, 124 (87.9 %) patients had same STOP score and 130 (92.2 %) patients were classified in same risk of OSA. Based on the polysomnography, 438 patients (72.6 %) had mild (n = 124, 20.4 %), moderate (n = 114, 18.9 %), and severe (n = 201, 33.3 %) OSA, whereas according to the STOP-BANG, 502 patients (83.3 %) were at high risk of OSA. The sensitivity and specificity of the STOP-BANG were found to be 91.6 and 45.2 %, respectively, at AHI C 5, 97.1 and 35.2 %, respectively, at AHI C 15, and 98 and 29.4 %, respectively, at AHI C 30. The area under the curve of the STOPBANG for identifying mild, moderate, and severe OSA was 0.805, 0.779, and 0.755, respectively. Conclusions Persian version of the STOP-BANG performs similar to its original version and is an easy-to-use questionnaire which could be considered as a reliable and valid tool for OSA screening. Keywords STOP-BANG Obstructive sleep apnea Reliability Validity Polysomnography Persian
M. Ghajarzadeh Brain and Spinal Injury Repair (BASIR) Research Center, Tehran University of Medical Sciences, Tehran, Iran
Introduction
M. Sedaghat Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
Obstructive sleep apnea (OSA) is a disorder characterized by repetitive periods of apnea and hypopnea during
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sleep [1]. The prevalence of OSA was reported up to 26 % with more prevalence among obese males [2, 3]. Epidemiological studies demonstrated that 4.9–27.3 % of Iranian populations are at high risk of OSA [4, 5]. OSA raises the risk of refractory hypertension, cardiovascular events, and cerebrovascular diseases [6]. The Sleep Heart Health Study and the Wisconsin Sleep Cohort Study both established an association between OSA severity and decreased quality of life in general population [7, 8]. Polysomnography is the gold standard diagnostic test for OSA [9], although it is expensive and time-consuming. Increasing the public awareness of OSA and inadequate number of sleep laboratories had reasoned to the long waiting list in sleep laboratories. Several questionnaires were developed to discriminate high risk patients for OSA who should undergo polysomnography [10]. It had shown that the Persian version of Berlin questionnaire, a well-known questionnaire for assessing OSA, is not an appropriate tool for identifying OSA in sleep clinic population [11, 12]. The snoring, tiredness, observed apnea, blood pressure, body mass index, age, neck circumference, gender (STOP-BANG) [13] was first developed in 2008 for OSA screening in preoperative surgical clinics. It consists of both subjective and objective items and could classify patients as being at high risk of OSA or not. There is no study exists to develop the Persian version of the STOP-BANG questionnaire. This study aimed to translate, validate, and test the Persian version of this widely used questionnaire in sleep clinic patients.
Materials and methods STOP-BANG questionnaire The STOP-BANG questionnaire consists of two parts: (1) STOP questionnaire including four yes/no questions evaluating snoring, tiredness during daytime, breathing stop observed by another individual during sleep and having or be treated for high blood pressure; and (2) BANG including demographic and anthropometric items measuring body mass index (BMI), age, neck circumference, and gender. BMI more than 35 kg\m2, age more than 50 years, neck circumference more than 40 cm (directly below the laryngeal prominence), and gender of male refer to positive scores. Patients with answering ‘‘yes’’ to two or more of four items of the STOP questionnaire were identified to be high risk of OSA, whereas patients with answering ‘‘yes’’ to three or more of eight items of the STOP-BANG questionnaire were identified to be high risk of OSA. Permission for translation and use of the STOP-BANG questionnaire were obtained from the copyright holder.
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Translation Standard forward–backward translation method was used (Fig. 1) [14]. Two physicians translated the English version of the STOP questionnaire into Persian (the official language of Iran). A fellowship of sleep medicine compared these translations, and a single provisional version was provided. This Persian version was back translated into English by two professional translators who were blind to the original questionnaire. Translations were handed to a bilingual physician, and one English version was derived which did not have significant differences with the original version. Eight expert physicians in sleep medicine evaluated content validity. Fifteen patients were asked to fill in the questionnaire, and then, debriefing interview was done for assessing face validity. Finally, the Persian version of the STOP questionnaire was provided for the study. Patients The study had a cross-sectional multicenter design conducted in three sleep clinics (located in north, south, and
English version of the STOP questionnaire
Translator 1
Translator 2
Translation into Persian 1
Translation into Persian 2
A fellowship of sleep medicine
Singe provisional Persian version
Translator 3
Translator 4
Back-translation into English 1
Back-translation into English 2
A bilingual physician
Expert committee – content validity
Pre-test in patient – face validity
Persian version of the STOP questionnaire
Fig. 1 Diagram representing the protocol used for translation of the STOP questionnaire into Persian
Qual Life Res
center of Tehran) between September 2008 and August 2012 in Tehran, Iran. All patients who attended to the sleep clinics were asked to complete a survey including STOPBANG questionnaire. Seven hundred and thirty-two patients who underwent in-laboratory polysomnography and responded to STOP questionnaire were consecutively recruited. One hundred and twenty-nine patients were excluded due to incomplete response to the STOP questionnaire, unsatisfactory polysomnography, or they did not want to participate in present study. Technicians measured the height and weight by a digital measuring station for height and weight (seca 284). Neck circumference was measured by an Ergonomic circumference measuring tape (seca 201). In order to carry out test–retest analysis, 200 patients were asked to answer the questionnaire twice at a time interval of 2–4 weeks: at the first clinic visit and in the next morning after polysomnography but 141 patients completely answered. Informed consents were obtained from all patients. The study had been approved by ethics committee of Tehran University of Medical Sciences. Polysomnography Polysomnography is the gold standard test for OSA diagnosis. Sandman Elite digital sleep software and Sandman SD32? amplifiers were used for performing polysomnography. Electroencephalogram, electrocardiogram, electrooculogram, electromyogram (submental and bilateral anterior tibialis), snoring, arterial oxygen saturation, abdominal and thoracic respiratory efforts, oronasal pressure, and body position were monitored during sleep. Video monitoring recorded patients during sleep by Infrared beams. An apnea was defined as total cessation of airflow for at least 10 s, whereas hypopnea was defined as the reduction of airflow for more than 50 % for at least 10 s with 3 % reduction of arterial oxygen saturation or with arousal. Apnea–hypopnea index (AHI) was calculated by dividing sum of apnea and hypopnea by hours of sleep. AHI C 5 was considered as clinical diagnosis of OSA while 5 B AHI \ 15, 15 B AHI \ 30, and AHI C 30 were determined as mild, moderate, and severe OSA, respectively. All tests were analyzed by first author according to the recommendation criteria by the American Academy of Sleep Medicine [15].
ratios, odds ratio, and area under the curve (AUC) for OSA identification using the AHI cutoff points of 5, 15, and 30. P value less than 0.05 was defined as statistically significant. PASW statistics 18 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis.
Results Characteristics of 603 studied patients are shown in Table 1. In test–retest analysis, 141 patients answered the STOP questionnaire twice at a time interval of 2–4 weeks (median: 23 days), 124 (87.9 %) patients had same STOP score, and 130 (92.2 %) patients were classified in same risk of OSA. The intraclass correlation coefficient was 0.88 (95 % confidence interval 0.83–0.91, p value \0.001). Based on the STOP questionnaire, 468 patients (77.6 %) were at high risk of OSA and 135 patients (22.4 %) were at low risk. The STOP-BANG questionnaire classified 502 patients (83.3 %) as being at high risk of OSA and 101 patients (16.7 %) as being at low risk. Based on the polysomnography, 165 patients (27.4 %) had AHI \ 5, whereas the remaining 438 patients had mild (n = 124, 20.4 %), moderate (n = 114, 18.9 %), and severe (n = 201, 33.3 %) OSA. The percents of classified patients as being at high risk of OSA were increased by rising in AHI score. Also, patients with more severe forms of OSA had higher STOP and STOP-BANG score (Table 2). Using AHI [ 5, 15, and 30 as cutoff values to evaluate the STOP questionnaire, the sensitivities were 86.3, 91.1, and 94.1, the specificities were 46.5, 37.1, and 30.7, and the negative predictive values were 54.8, 79, and 91.1, respectively (Table 3). Results indicate that the STOP questionnaire is more sensitive in detecting the patients with moderate to severe OSA. Table 4 presents the same results for the STOP-BANG questionnaire. The majority of included patients in our study were male; therefore, we calculated the AUC separately in men and women to determine the role of gender in performance of the STOP-BANG. The AUC of the STOP-BANG in men and women was found to be 0.804 and 0.809, respectively, at AHI C 5, 0.772, and 0.787, respectively, at AHI C 15, 0.751, and 0.763, respectively, at AHI C 30. The difference in all cutoff points was not statistically significant.
Statistical analysis Patients’ characteristics are presented as means (±standard deviation) or percentages. Test–retest analysis was performed using intraclass correlation coefficient. We calculated predictive parameters of the STOP and STOP-BANG questionnaire including sensitivity, specificity, positive and negative predictive values, positive and negative likelihood
Discussion More than 85 % of individuals with OSA are never diagnosed and treated [16], therefore applying valid and reliable instruments for screening is essential. In real work setting, it is not applicable to utilize polysomnography for
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Qual Life Res Table 1 Characteristics of studied patients based on AHI categories AHI \ 5 N = 164 Mean ± SD
5 B AHI \ 15 N = 124 Mean ± SD
15 B AHI \ 30 N = 114 Mean ± SD
AHI C 30 N = 201 Mean ± SD
All patients N = 603 Mean ± SD
Female, n (%)
57 (34.7)
32 (25.8)
22 (19.3)
41 (20.4)
152 (25.2)
Age (years)
42.3 ± 14
46.1 ± 10.7
48.4 ± 13.3
47.1 ± 11.8
45.8 ± 12.7
BMI (kg/m2) Systolic blood pressure (mmHg)
26.3 ± 5 112.9 ± 14.3
28.8 ± 5.5 119.9 ± 12.1
30 ± 4.7 123.8 ± 14.5
31.1 ± 6.4 123.5 ± 14.6
29.18 ± 5.9 119.7 ± 14.7
Diastolic blood pressure (mmHg)
74.9 ± 10.7
78 ± 10.6
80.4 ± 14.8
81.5 ± 11.7
78.7 ± 12
Neck circumference (cm)
38 ± 3.2
39.6 ± 3.5
40.3 ± 3.44
41.2 ± 3.4
39.7 ± 3.6
Mean SaO2 (%)
94 ± 2.3
93.2 ± 1.8
91.8 ± 4.8
88.9 ± 8.7
91.7 ± 6
Lowest SaO2 (%)
77.5 ± 28.9
74.4 ± 26.2
70 ± 25.6
59.7 ± 27.2
69.5 ± 28.1
AHI apnea–hypopnea index, BMI body mass index
Table 2 Mean (standard deviation) of the STOP and STOP-BANG questionnaires and the classification of patients in various groups of AHI AHI \ 5 (n = 164)
5 B AHI \ 15 (n = 124)
15 B AHI \ 30 (n = 114)
AHI C 30 (n = 201)
All patients (n = 603)
1.6 (0.9)
2.2 (1.02)
2.5 (0.9)
2.8 (0.7)
2.3 (1.03)
Low risk, n (%)
74 (46.6)
32 (25.2)
17 (14.9)
12 (5.9)
135 (22.3)
High risk, n (%)
91 (53.4)
92 (74.8)
97 (85.1)
189 (94.1)
468 (77.7)
Score, mean (SD)
2.8 (1.3)
3.8 (1.3)
4.4 (1.1)
4.7 (1.08)
3.9 (1.4)
Low risk, n (%)
81 (44.7)
28 (21.9)
6 (5.3)
4 (1.9)
101 (20.1)
High risk, n (%)
111 (55.3)
96 (78.1)
108 (94.7)
197 (98.1)
503 (79.9)
STOP Score, mean (SD)
STOP-BANG
AHI apnea–hypopnea index
Table 3 Predictive parameters of the STOP questionnaire for OSA identification in AHI cutoff points 5, 15, and 30
OSA obstructive sleep apnea, AHI apnea–hypopnea index
AHI cutoff point
AHI C 15
AHI C 30
Sensitivity
86.3 (82.6–89.3)
91.1 (87.2–93.8)
Specificity
46.5 (38.6–54.6)
37.1 (31.5–43)
30.7 (26.2–35.6)
Positive predictive value
81.9 (78–85.2)
61.5 (56.9–66)
40.2 (36.6–45.9)
Negative predictive value
54.8 (46–63.3)
79 (71–85.2)
91.1 (84.8–94.9)
Positive likelihood ratio
1.61 (1.38–1.88)
1.44 (1.31–1.6)
1.36 (1.26–1.47)
Negative likelihood ratio
0.29 (0.21–0.39)
0.24 (0.16–0.35)
0.18 (0.1–0.34)
Odds ratio
5.51 (3.58–8.47)
6.04 (3.76–9.7)
Area under the curve
all patients at risk of OSA. In this way, a simple easy-use instrument for OSA identification should be considered. One of the most common questionnaires in this field is the Berlin questionnaire which has three categories and 10 questions with a complex scoring system. It had wide range of sensitivity from 54 to 86 % and the specificity from 43 to 87 % among patients in different settings [17–20].
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AHI C 5
0.745 (0.698–0.792)
0.721(0.678–0.763)
94.1 (89.8–96.7)
7.19 (3.76–13.76) 0.704 (0.660–747)
In recent years, the STOP-BANG questionnaire was used broadly for screening OSA. In this study, we translated the STOP-BANG questionnaire into Persian and assessed its reliability and validity. In test–retest analysis, we found that 87.9 % of patients had the same STOP score in 2–4 weeks interval. Chung et al. [13] assessed the test– retest agreement of the STOP questionnaire which 55
Qual Life Res Table 4 Predictive parameters of the STOP-BANG questionnaire for OSA identification in AHI cutoff points 5, 15, and 30
OSA obstructive sleep apnea, AHI apnea–hypopnea index
AHI cutoff point
AHI C 5
AHI C 15
AHI C 30
Sensitivity
91.6 (87–94.7)
97.1 (92.8–98.8)
98 (93.1–99.4)
Specificity
45.2 (35.6–55.2)
35.2 (28.2–42.9)
29.4 (23.5–36.1)
Positive predictive value
78.2 (72.5–83)
56.9 (50.5–63)
41.8 (35.7–48.1)
Negative predictive value
71.6 (59.2–81.4)
93.3 (84–97.3)
96.6 (88.6–99)
Positive likelihood ratio
1.67 (1.38–2.02)
1.49 (1.33–1.68)
1.38 (1.26–1.52)
Negative likelihood ratio
0.18 (0.11–0.20)
0.08 (0.03–0.22)
0.06 (0.01–0.36)
Odds ratio
9.09 (4.79–17.25)
18.35 (6.44–52.24)
20.86 (4.97–87.42)
0.805 (0.753–0.858)
0.779 (0.727–0.831)
0.755 (0.7–0.809)
Area under the curve
patients answered to questionnaire twice at a time interval of 1–27 days (median = 8 days). They found that 96.4 % of patients had the same STOP score upon retesting. This difference might be caused by larger sample and wider time interval in our study. We found sensitivity, specificity, positive, and negative predictive values of the STOP questionnaire for AHI C 5 as 86, 46, 81, and 54 %, respectively. Also, we calculated area under the curve as 0.74 at this point. Chung et al. [13] in a study on surgical patients found sensitivity, specificity, positive and negative predictive values, and area under the curve of the STOP questionnaire for AHI C 5 as 65.6, 60, 78.4, 44 %, and 0.703, respectively. Pecotic et al. [21] evaluated Croatian version of STOP questionnaire in sleep clinic patients and reported sensitivity, specificity, positive and negative predictive values, and area under the curve for AHI C 5 as 96, 83, 61, 95 %, and 0.84, respectively. A recent study of the STOP questionnaire in patients referred to sleep clinics and reported sensitivity, specificity, positive and negative predictive values, and area under the curve for AHI C 5 as 74.6, 34, 79.2, 28.3 %, and 0.607, respectively [22]. Our results are compatible with previous studies that showed the STOP-BANG questionnaire in a population with high prevalence of OSA-like patients referred to sleep clinic, performs with high sensitivity and negative predictive value and low specificity and positive predictive value specially for diagnosing moderate and severe forms of OSA [22–25]. Boynton et al. [22] reported sensitivity, specificity, positive and negative predictive values, and area under the curve of the STOP-BANG for AHI C 15 as 93.2, 40.5, 58.2, 87 %, and 0.746, respectively, and for AHI C 30 as 96.8, 33.1, 36.4, 96.3 %, and 0.762, respectively. The major limitation of our study is that we failed to generalize our findings to all sleep clinics. Sleep clinic populations have many diversities, and variety of these patients is different from one clinic to another depending on their coherence to various medical sections. The patients in present study were collected from three sleep clinics which most of them had been referred to undergo
PSG due to suspected OSA (72.8 % of included patients had AHI [ 5). We tried to overcome this limitation with including large number of patients and also referred patients not suspicious to OSA, but we failed to include patients from sleep clinics dependent to neurology and psychiatry centers which prevalence of OSA among their patients is low. In conclusion, Persian version of the STOP-BANG is an easy-to-use questionnaire which could be considered as a reliable and valid tool for OSA screening in sleep clinic population. Further investigations are needed to assess the performance of Persian version of the STOP-BANG for measuring OSA in other clinical settings and community people. Acknowledgments This research has been supported by a grant from Tehran University of Medical Sciences and health Services.
References 1. The Report of an American Academy of Sleep Medicine Task Force. (1999). Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. Sleep, 22, 667–689. 2. Peppard, P. E., Young, T., Barnet, J. H., Palta, M., Hagen, E. W., & Hia, K. M. (2013). Increased prevalence of sleep-disordered breathing in adults. American Journal of Epidemiology, 177, 1006–1014. 3. Young, T., Peppard, P. E., & Gottlieb, D. J. (2002). Epidemiology of obstructive sleep apnea: A population health perspective. American Journal of Respiratory and Critical Care Medicine, 165, 1217–1239. 4. Amra, B., Farajzadegan, Z., Golshan, M., Fietze, I., & Penzel, T. (2011). Prevalence of sleep apnea-related symptoms in a Persian population. Sleep and Breathing, 15, 425–429. 5. Khazaie, H., Najafi, F., Rezaie, L., Tahmasian, M., Sepehry, A. A., & Herth, F. J. F. (2011). Prevalence of symptoms and risk of obstructive sleep apnea syndrome in the general population. Archives of Iranian Medicine, 14, 335–338. 6. Kryger, M. H. (2000). Diagnosis and management of sleep apnea syndrome. Clinical Cornerstone, 2, 39–47. 7. Baldwin, C. M., Griffitch, K. A., Nieto, F. J., O’Connor, G. T., Walsleben, J. A., & Redline, S. (2001). The association of sleepdisordered breathing and sleep symptoms with quality of life in the Sleep Heart Health Study. Sleep, 24, 96–105.
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Qual Life Res 8. Finn, L., Young, T. B., Palta, M., & Fryback, D. G. (1998). Sleepdisordered breathing and self-reported general health status in the Wisconsin Sleep Cohort Study. Sleep, 21, 701–706. 9. Bloch, K. E. (1997). Polysomnography: A systematic review. Technology and Health Care, 5, 285–305. 10. Abrishami, A., Khajehdehi, A., & Chung, F. (2010). A systematic review of screening questionnaires for obstructive sleep apnea. Canadian Journal of Anaesthesia, 57, 423–438. 11. Sadeghniiat-Haghighi, K., Montazeri, A., Khajeh-Mehrizi, A., Aminian, O., Rahimi-Golkhandan, A., et al. (2014). The Berlin questionnaire: Performance of the Persian version for measuring obstructive sleep apnea in sleep clinic population. Journal of Sleep Disorders: Treatment and Care, 3, 4. 12. Amra, B., Nouranian, E., Golshan, M., Fietze, I., & Penzel, T. (2013). Validation of the Persian version of Berlin sleep questionnaire for diagnosing obstructive sleep apnea. International Journal of Preventive Medicine, 4, 334–339. 13. Chung, F., Yegneswaran, B., Liao, P., Chung, S. A., Vairavanathan, S., Islam, I., et al. (2008). STOP questionnaire: A tool to screen patients for obstructive sleep apnea. Anesthesiology, 108, 812–821. 14. Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25, 3186–3191. 15. Iber, C. A.-I. S., Chesson, A. L, Jr, & Quan, S. F. (2007). The AASM manual for the scoring of sleep and associated events, rules, terminology and technical specifications (1st ed.). Westchester: American Academy of Sleep Medicine. 16. Young, T., Evans, L., Finn, L., & Palta, M. (1997). Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep, 20, 705–706. 17. Netzer, N. C., Stoohs, R. A., Netzer, C. M., Clark, K., & Strohl, K. P. (1999). Using the Berlin questionnaire to identify patients at risk for the sleep apnea syndrome. Annals of Internal Medicine, 131, 485–491.
123
18. Martinez, D., da Silva, R. P., Klein, C., Fiori, C. Z., Massierer, D., Cassol, C. M., et al. (2012). High risk for sleep apnea in the Berlin questionnaire and coronary artery disease. Sleep and Breathing, 16, 89–94. 19. Ahmadi, N., Chung, S. A., Gibbs, A., & Shapiro, C. M. (2008). The Berlin questionnaire for sleep apnea in a sleep clinic population: Relationship to polysomnographic measurement of respiratory disturbance. Sleep and Breathing, 12, 39–45. 20. Chung, F., Yegneswaran, B., Liao, P., Chung, S. A., Vairavanathan, S., Islam, I., et al. (2008). Validation of the Berlin Questionnaire and American Society of Anesthesiologists Checklist as screening tools for obstructive sleep apnea in surgical patients. Anesthesiology, 108, 822–830. 21. Pecotic, R., Dodig, I. P., Valic, M., Ivkovic, N., & Dogas, Z. (2011). The evaluation of the Croatian version of the Epworth sleepiness scale and STOP questionnaire as screening tools for obstructive sleep apnea syndrome. Sleep and Breathing, 16, 793–802. 22. Boynton, G., Vahabzadeh, A., Hammoud, S., Ruzicka, D. L., & Chervin, R. D. (2013). Validation of the STOP-BANG Questionnaire among patients referred for suspected obstructive sleep apnea. Journal of Sleep Disorders: Treatment and Care, 2, 4. 23. Ong, T. H., Raudha, S., Fook-Chong, S., Lew, N., & Hsu, A. A. (2010). Simplifying STOP-BANG: Use of a simple questionnaire to screen for OSA in an Asian population. Sleep and Breathing, 14, 371–376. 24. Farney, R. J., Walker, B. S., Farney, R. M., Snow, G. L., & Walker, J. M. (2011). The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: Relation to polysomnographic measurements of the apnea/hypopnea index. Journal of Clinical Sleep Medicine, 7, 459–465. 25. Vana, K. D., Silva, G. E., & Goldberg, R. (2013). Predictive abilities of the STOP-Bang and Epworth Sleepiness Scale in identifying sleep clinic patients at high risk for obstructive sleep apnea. Research in Nursing & Health, 36, 84–94.
BRIEF COMMUNICATION
The STOP-BANG questionnaire: reliability and validity of the Persian version in sleep clinic population Khosro Sadeghniiat-Haghighi • Ali Montazeri • Ahmad Khajeh-Mehrizi Mahsa Ghajarzadeh • Zahra Banafsheh Alemohammad • Omid Aminian • Mojtaba Sedaghat
•
Accepted: 14 January 2015 Ó Springer International Publishing Switzerland 2015
Abstract Purpose The snoring, tiredness, observed apnea, blood pressure, body mass index, age, neck circumference, gender (STOP-BANG) is a concise and effective obstructive sleep apnea (OSA) screening tool, part questionnaire (STOP), and part demographic or anthropometric measurements (BANG). The main purpose of this study was to translate this well-liked questionnaire into Persian and assess its reliability and validity in sleep clinic population. Methods Standard forward–backward method was used for translation. A sample of 603 patients, who admitted to the sleep clinic, completely answered to the STOP questionnaire
K. Sadeghniiat-Haghighi A. Khajeh-Mehrizi Z. B. Alemohammad Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran K. Sadeghniiat-Haghighi Z. B. Alemohammad O. Aminian Center for Research on Occupational Diseases, Tehran University of Medical Sciences, Tehran, Iran A. Montazeri Mental Health Research Group, Health Metrics Research Centre, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran A. Khajeh-Mehrizi (&) Department of Internal Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran e-mail: [email protected]
and underwent in-laboratory polysomnography, included in this study. Height, weight, and neck circumference were measured by technicians for calculating BANG score. The apnea–hypopnea index (AHI) on the polysomnography was used as gold standard for OSA diagnosis: none (AHI \ 5), mild (5 B AHI \ 15), moderate (15 B AHI \ 30), and severe (AHI C 30). One hundred and forty one patients were answered to the STOP questionnaire twice at a time interval of 2–4 weeks for test–retest analysis. Results In reliability analysis, 124 (87.9 %) patients had same STOP score and 130 (92.2 %) patients were classified in same risk of OSA. Based on the polysomnography, 438 patients (72.6 %) had mild (n = 124, 20.4 %), moderate (n = 114, 18.9 %), and severe (n = 201, 33.3 %) OSA, whereas according to the STOP-BANG, 502 patients (83.3 %) were at high risk of OSA. The sensitivity and specificity of the STOP-BANG were found to be 91.6 and 45.2 %, respectively, at AHI C 5, 97.1 and 35.2 %, respectively, at AHI C 15, and 98 and 29.4 %, respectively, at AHI C 30. The area under the curve of the STOPBANG for identifying mild, moderate, and severe OSA was 0.805, 0.779, and 0.755, respectively. Conclusions Persian version of the STOP-BANG performs similar to its original version and is an easy-to-use questionnaire which could be considered as a reliable and valid tool for OSA screening. Keywords STOP-BANG Obstructive sleep apnea Reliability Validity Polysomnography Persian
M. Ghajarzadeh Brain and Spinal Injury Repair (BASIR) Research Center, Tehran University of Medical Sciences, Tehran, Iran
Introduction
M. Sedaghat Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
Obstructive sleep apnea (OSA) is a disorder characterized by repetitive periods of apnea and hypopnea during
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sleep [1]. The prevalence of OSA was reported up to 26 % with more prevalence among obese males [2, 3]. Epidemiological studies demonstrated that 4.9–27.3 % of Iranian populations are at high risk of OSA [4, 5]. OSA raises the risk of refractory hypertension, cardiovascular events, and cerebrovascular diseases [6]. The Sleep Heart Health Study and the Wisconsin Sleep Cohort Study both established an association between OSA severity and decreased quality of life in general population [7, 8]. Polysomnography is the gold standard diagnostic test for OSA [9], although it is expensive and time-consuming. Increasing the public awareness of OSA and inadequate number of sleep laboratories had reasoned to the long waiting list in sleep laboratories. Several questionnaires were developed to discriminate high risk patients for OSA who should undergo polysomnography [10]. It had shown that the Persian version of Berlin questionnaire, a well-known questionnaire for assessing OSA, is not an appropriate tool for identifying OSA in sleep clinic population [11, 12]. The snoring, tiredness, observed apnea, blood pressure, body mass index, age, neck circumference, gender (STOP-BANG) [13] was first developed in 2008 for OSA screening in preoperative surgical clinics. It consists of both subjective and objective items and could classify patients as being at high risk of OSA or not. There is no study exists to develop the Persian version of the STOP-BANG questionnaire. This study aimed to translate, validate, and test the Persian version of this widely used questionnaire in sleep clinic patients.
Materials and methods STOP-BANG questionnaire The STOP-BANG questionnaire consists of two parts: (1) STOP questionnaire including four yes/no questions evaluating snoring, tiredness during daytime, breathing stop observed by another individual during sleep and having or be treated for high blood pressure; and (2) BANG including demographic and anthropometric items measuring body mass index (BMI), age, neck circumference, and gender. BMI more than 35 kg\m2, age more than 50 years, neck circumference more than 40 cm (directly below the laryngeal prominence), and gender of male refer to positive scores. Patients with answering ‘‘yes’’ to two or more of four items of the STOP questionnaire were identified to be high risk of OSA, whereas patients with answering ‘‘yes’’ to three or more of eight items of the STOP-BANG questionnaire were identified to be high risk of OSA. Permission for translation and use of the STOP-BANG questionnaire were obtained from the copyright holder.
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Translation Standard forward–backward translation method was used (Fig. 1) [14]. Two physicians translated the English version of the STOP questionnaire into Persian (the official language of Iran). A fellowship of sleep medicine compared these translations, and a single provisional version was provided. This Persian version was back translated into English by two professional translators who were blind to the original questionnaire. Translations were handed to a bilingual physician, and one English version was derived which did not have significant differences with the original version. Eight expert physicians in sleep medicine evaluated content validity. Fifteen patients were asked to fill in the questionnaire, and then, debriefing interview was done for assessing face validity. Finally, the Persian version of the STOP questionnaire was provided for the study. Patients The study had a cross-sectional multicenter design conducted in three sleep clinics (located in north, south, and
English version of the STOP questionnaire
Translator 1
Translator 2
Translation into Persian 1
Translation into Persian 2
A fellowship of sleep medicine
Singe provisional Persian version
Translator 3
Translator 4
Back-translation into English 1
Back-translation into English 2
A bilingual physician
Expert committee – content validity
Pre-test in patient – face validity
Persian version of the STOP questionnaire
Fig. 1 Diagram representing the protocol used for translation of the STOP questionnaire into Persian
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center of Tehran) between September 2008 and August 2012 in Tehran, Iran. All patients who attended to the sleep clinics were asked to complete a survey including STOPBANG questionnaire. Seven hundred and thirty-two patients who underwent in-laboratory polysomnography and responded to STOP questionnaire were consecutively recruited. One hundred and twenty-nine patients were excluded due to incomplete response to the STOP questionnaire, unsatisfactory polysomnography, or they did not want to participate in present study. Technicians measured the height and weight by a digital measuring station for height and weight (seca 284). Neck circumference was measured by an Ergonomic circumference measuring tape (seca 201). In order to carry out test–retest analysis, 200 patients were asked to answer the questionnaire twice at a time interval of 2–4 weeks: at the first clinic visit and in the next morning after polysomnography but 141 patients completely answered. Informed consents were obtained from all patients. The study had been approved by ethics committee of Tehran University of Medical Sciences. Polysomnography Polysomnography is the gold standard test for OSA diagnosis. Sandman Elite digital sleep software and Sandman SD32? amplifiers were used for performing polysomnography. Electroencephalogram, electrocardiogram, electrooculogram, electromyogram (submental and bilateral anterior tibialis), snoring, arterial oxygen saturation, abdominal and thoracic respiratory efforts, oronasal pressure, and body position were monitored during sleep. Video monitoring recorded patients during sleep by Infrared beams. An apnea was defined as total cessation of airflow for at least 10 s, whereas hypopnea was defined as the reduction of airflow for more than 50 % for at least 10 s with 3 % reduction of arterial oxygen saturation or with arousal. Apnea–hypopnea index (AHI) was calculated by dividing sum of apnea and hypopnea by hours of sleep. AHI C 5 was considered as clinical diagnosis of OSA while 5 B AHI \ 15, 15 B AHI \ 30, and AHI C 30 were determined as mild, moderate, and severe OSA, respectively. All tests were analyzed by first author according to the recommendation criteria by the American Academy of Sleep Medicine [15].
ratios, odds ratio, and area under the curve (AUC) for OSA identification using the AHI cutoff points of 5, 15, and 30. P value less than 0.05 was defined as statistically significant. PASW statistics 18 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis.
Results Characteristics of 603 studied patients are shown in Table 1. In test–retest analysis, 141 patients answered the STOP questionnaire twice at a time interval of 2–4 weeks (median: 23 days), 124 (87.9 %) patients had same STOP score, and 130 (92.2 %) patients were classified in same risk of OSA. The intraclass correlation coefficient was 0.88 (95 % confidence interval 0.83–0.91, p value \0.001). Based on the STOP questionnaire, 468 patients (77.6 %) were at high risk of OSA and 135 patients (22.4 %) were at low risk. The STOP-BANG questionnaire classified 502 patients (83.3 %) as being at high risk of OSA and 101 patients (16.7 %) as being at low risk. Based on the polysomnography, 165 patients (27.4 %) had AHI \ 5, whereas the remaining 438 patients had mild (n = 124, 20.4 %), moderate (n = 114, 18.9 %), and severe (n = 201, 33.3 %) OSA. The percents of classified patients as being at high risk of OSA were increased by rising in AHI score. Also, patients with more severe forms of OSA had higher STOP and STOP-BANG score (Table 2). Using AHI [ 5, 15, and 30 as cutoff values to evaluate the STOP questionnaire, the sensitivities were 86.3, 91.1, and 94.1, the specificities were 46.5, 37.1, and 30.7, and the negative predictive values were 54.8, 79, and 91.1, respectively (Table 3). Results indicate that the STOP questionnaire is more sensitive in detecting the patients with moderate to severe OSA. Table 4 presents the same results for the STOP-BANG questionnaire. The majority of included patients in our study were male; therefore, we calculated the AUC separately in men and women to determine the role of gender in performance of the STOP-BANG. The AUC of the STOP-BANG in men and women was found to be 0.804 and 0.809, respectively, at AHI C 5, 0.772, and 0.787, respectively, at AHI C 15, 0.751, and 0.763, respectively, at AHI C 30. The difference in all cutoff points was not statistically significant.
Statistical analysis Patients’ characteristics are presented as means (±standard deviation) or percentages. Test–retest analysis was performed using intraclass correlation coefficient. We calculated predictive parameters of the STOP and STOP-BANG questionnaire including sensitivity, specificity, positive and negative predictive values, positive and negative likelihood
Discussion More than 85 % of individuals with OSA are never diagnosed and treated [16], therefore applying valid and reliable instruments for screening is essential. In real work setting, it is not applicable to utilize polysomnography for
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Qual Life Res Table 1 Characteristics of studied patients based on AHI categories AHI \ 5 N = 164 Mean ± SD
5 B AHI \ 15 N = 124 Mean ± SD
15 B AHI \ 30 N = 114 Mean ± SD
AHI C 30 N = 201 Mean ± SD
All patients N = 603 Mean ± SD
Female, n (%)
57 (34.7)
32 (25.8)
22 (19.3)
41 (20.4)
152 (25.2)
Age (years)
42.3 ± 14
46.1 ± 10.7
48.4 ± 13.3
47.1 ± 11.8
45.8 ± 12.7
BMI (kg/m2) Systolic blood pressure (mmHg)
26.3 ± 5 112.9 ± 14.3
28.8 ± 5.5 119.9 ± 12.1
30 ± 4.7 123.8 ± 14.5
31.1 ± 6.4 123.5 ± 14.6
29.18 ± 5.9 119.7 ± 14.7
Diastolic blood pressure (mmHg)
74.9 ± 10.7
78 ± 10.6
80.4 ± 14.8
81.5 ± 11.7
78.7 ± 12
Neck circumference (cm)
38 ± 3.2
39.6 ± 3.5
40.3 ± 3.44
41.2 ± 3.4
39.7 ± 3.6
Mean SaO2 (%)
94 ± 2.3
93.2 ± 1.8
91.8 ± 4.8
88.9 ± 8.7
91.7 ± 6
Lowest SaO2 (%)
77.5 ± 28.9
74.4 ± 26.2
70 ± 25.6
59.7 ± 27.2
69.5 ± 28.1
AHI apnea–hypopnea index, BMI body mass index
Table 2 Mean (standard deviation) of the STOP and STOP-BANG questionnaires and the classification of patients in various groups of AHI AHI \ 5 (n = 164)
5 B AHI \ 15 (n = 124)
15 B AHI \ 30 (n = 114)
AHI C 30 (n = 201)
All patients (n = 603)
1.6 (0.9)
2.2 (1.02)
2.5 (0.9)
2.8 (0.7)
2.3 (1.03)
Low risk, n (%)
74 (46.6)
32 (25.2)
17 (14.9)
12 (5.9)
135 (22.3)
High risk, n (%)
91 (53.4)
92 (74.8)
97 (85.1)
189 (94.1)
468 (77.7)
Score, mean (SD)
2.8 (1.3)
3.8 (1.3)
4.4 (1.1)
4.7 (1.08)
3.9 (1.4)
Low risk, n (%)
81 (44.7)
28 (21.9)
6 (5.3)
4 (1.9)
101 (20.1)
High risk, n (%)
111 (55.3)
96 (78.1)
108 (94.7)
197 (98.1)
503 (79.9)
STOP Score, mean (SD)
STOP-BANG
AHI apnea–hypopnea index
Table 3 Predictive parameters of the STOP questionnaire for OSA identification in AHI cutoff points 5, 15, and 30
OSA obstructive sleep apnea, AHI apnea–hypopnea index
AHI cutoff point
AHI C 15
AHI C 30
Sensitivity
86.3 (82.6–89.3)
91.1 (87.2–93.8)
Specificity
46.5 (38.6–54.6)
37.1 (31.5–43)
30.7 (26.2–35.6)
Positive predictive value
81.9 (78–85.2)
61.5 (56.9–66)
40.2 (36.6–45.9)
Negative predictive value
54.8 (46–63.3)
79 (71–85.2)
91.1 (84.8–94.9)
Positive likelihood ratio
1.61 (1.38–1.88)
1.44 (1.31–1.6)
1.36 (1.26–1.47)
Negative likelihood ratio
0.29 (0.21–0.39)
0.24 (0.16–0.35)
0.18 (0.1–0.34)
Odds ratio
5.51 (3.58–8.47)
6.04 (3.76–9.7)
Area under the curve
all patients at risk of OSA. In this way, a simple easy-use instrument for OSA identification should be considered. One of the most common questionnaires in this field is the Berlin questionnaire which has three categories and 10 questions with a complex scoring system. It had wide range of sensitivity from 54 to 86 % and the specificity from 43 to 87 % among patients in different settings [17–20].
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AHI C 5
0.745 (0.698–0.792)
0.721(0.678–0.763)
94.1 (89.8–96.7)
7.19 (3.76–13.76) 0.704 (0.660–747)
In recent years, the STOP-BANG questionnaire was used broadly for screening OSA. In this study, we translated the STOP-BANG questionnaire into Persian and assessed its reliability and validity. In test–retest analysis, we found that 87.9 % of patients had the same STOP score in 2–4 weeks interval. Chung et al. [13] assessed the test– retest agreement of the STOP questionnaire which 55
Qual Life Res Table 4 Predictive parameters of the STOP-BANG questionnaire for OSA identification in AHI cutoff points 5, 15, and 30
OSA obstructive sleep apnea, AHI apnea–hypopnea index
AHI cutoff point
AHI C 5
AHI C 15
AHI C 30
Sensitivity
91.6 (87–94.7)
97.1 (92.8–98.8)
98 (93.1–99.4)
Specificity
45.2 (35.6–55.2)
35.2 (28.2–42.9)
29.4 (23.5–36.1)
Positive predictive value
78.2 (72.5–83)
56.9 (50.5–63)
41.8 (35.7–48.1)
Negative predictive value
71.6 (59.2–81.4)
93.3 (84–97.3)
96.6 (88.6–99)
Positive likelihood ratio
1.67 (1.38–2.02)
1.49 (1.33–1.68)
1.38 (1.26–1.52)
Negative likelihood ratio
0.18 (0.11–0.20)
0.08 (0.03–0.22)
0.06 (0.01–0.36)
Odds ratio
9.09 (4.79–17.25)
18.35 (6.44–52.24)
20.86 (4.97–87.42)
0.805 (0.753–0.858)
0.779 (0.727–0.831)
0.755 (0.7–0.809)
Area under the curve
patients answered to questionnaire twice at a time interval of 1–27 days (median = 8 days). They found that 96.4 % of patients had the same STOP score upon retesting. This difference might be caused by larger sample and wider time interval in our study. We found sensitivity, specificity, positive, and negative predictive values of the STOP questionnaire for AHI C 5 as 86, 46, 81, and 54 %, respectively. Also, we calculated area under the curve as 0.74 at this point. Chung et al. [13] in a study on surgical patients found sensitivity, specificity, positive and negative predictive values, and area under the curve of the STOP questionnaire for AHI C 5 as 65.6, 60, 78.4, 44 %, and 0.703, respectively. Pecotic et al. [21] evaluated Croatian version of STOP questionnaire in sleep clinic patients and reported sensitivity, specificity, positive and negative predictive values, and area under the curve for AHI C 5 as 96, 83, 61, 95 %, and 0.84, respectively. A recent study of the STOP questionnaire in patients referred to sleep clinics and reported sensitivity, specificity, positive and negative predictive values, and area under the curve for AHI C 5 as 74.6, 34, 79.2, 28.3 %, and 0.607, respectively [22]. Our results are compatible with previous studies that showed the STOP-BANG questionnaire in a population with high prevalence of OSA-like patients referred to sleep clinic, performs with high sensitivity and negative predictive value and low specificity and positive predictive value specially for diagnosing moderate and severe forms of OSA [22–25]. Boynton et al. [22] reported sensitivity, specificity, positive and negative predictive values, and area under the curve of the STOP-BANG for AHI C 15 as 93.2, 40.5, 58.2, 87 %, and 0.746, respectively, and for AHI C 30 as 96.8, 33.1, 36.4, 96.3 %, and 0.762, respectively. The major limitation of our study is that we failed to generalize our findings to all sleep clinics. Sleep clinic populations have many diversities, and variety of these patients is different from one clinic to another depending on their coherence to various medical sections. The patients in present study were collected from three sleep clinics which most of them had been referred to undergo
PSG due to suspected OSA (72.8 % of included patients had AHI [ 5). We tried to overcome this limitation with including large number of patients and also referred patients not suspicious to OSA, but we failed to include patients from sleep clinics dependent to neurology and psychiatry centers which prevalence of OSA among their patients is low. In conclusion, Persian version of the STOP-BANG is an easy-to-use questionnaire which could be considered as a reliable and valid tool for OSA screening in sleep clinic population. Further investigations are needed to assess the performance of Persian version of the STOP-BANG for measuring OSA in other clinical settings and community people. Acknowledgments This research has been supported by a grant from Tehran University of Medical Sciences and health Services.
References 1. The Report of an American Academy of Sleep Medicine Task Force. (1999). Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. Sleep, 22, 667–689. 2. Peppard, P. E., Young, T., Barnet, J. H., Palta, M., Hagen, E. W., & Hia, K. M. (2013). Increased prevalence of sleep-disordered breathing in adults. American Journal of Epidemiology, 177, 1006–1014. 3. Young, T., Peppard, P. E., & Gottlieb, D. J. (2002). Epidemiology of obstructive sleep apnea: A population health perspective. American Journal of Respiratory and Critical Care Medicine, 165, 1217–1239. 4. Amra, B., Farajzadegan, Z., Golshan, M., Fietze, I., & Penzel, T. (2011). Prevalence of sleep apnea-related symptoms in a Persian population. Sleep and Breathing, 15, 425–429. 5. Khazaie, H., Najafi, F., Rezaie, L., Tahmasian, M., Sepehry, A. A., & Herth, F. J. F. (2011). Prevalence of symptoms and risk of obstructive sleep apnea syndrome in the general population. Archives of Iranian Medicine, 14, 335–338. 6. Kryger, M. H. (2000). Diagnosis and management of sleep apnea syndrome. Clinical Cornerstone, 2, 39–47. 7. Baldwin, C. M., Griffitch, K. A., Nieto, F. J., O’Connor, G. T., Walsleben, J. A., & Redline, S. (2001). The association of sleepdisordered breathing and sleep symptoms with quality of life in the Sleep Heart Health Study. Sleep, 24, 96–105.
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Qual Life Res 8. Finn, L., Young, T. B., Palta, M., & Fryback, D. G. (1998). Sleepdisordered breathing and self-reported general health status in the Wisconsin Sleep Cohort Study. Sleep, 21, 701–706. 9. Bloch, K. E. (1997). Polysomnography: A systematic review. Technology and Health Care, 5, 285–305. 10. Abrishami, A., Khajehdehi, A., & Chung, F. (2010). A systematic review of screening questionnaires for obstructive sleep apnea. Canadian Journal of Anaesthesia, 57, 423–438. 11. Sadeghniiat-Haghighi, K., Montazeri, A., Khajeh-Mehrizi, A., Aminian, O., Rahimi-Golkhandan, A., et al. (2014). The Berlin questionnaire: Performance of the Persian version for measuring obstructive sleep apnea in sleep clinic population. Journal of Sleep Disorders: Treatment and Care, 3, 4. 12. Amra, B., Nouranian, E., Golshan, M., Fietze, I., & Penzel, T. (2013). Validation of the Persian version of Berlin sleep questionnaire for diagnosing obstructive sleep apnea. International Journal of Preventive Medicine, 4, 334–339. 13. Chung, F., Yegneswaran, B., Liao, P., Chung, S. A., Vairavanathan, S., Islam, I., et al. (2008). STOP questionnaire: A tool to screen patients for obstructive sleep apnea. Anesthesiology, 108, 812–821. 14. Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25, 3186–3191. 15. Iber, C. A.-I. S., Chesson, A. L, Jr, & Quan, S. F. (2007). The AASM manual for the scoring of sleep and associated events, rules, terminology and technical specifications (1st ed.). Westchester: American Academy of Sleep Medicine. 16. Young, T., Evans, L., Finn, L., & Palta, M. (1997). Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep, 20, 705–706. 17. Netzer, N. C., Stoohs, R. A., Netzer, C. M., Clark, K., & Strohl, K. P. (1999). Using the Berlin questionnaire to identify patients at risk for the sleep apnea syndrome. Annals of Internal Medicine, 131, 485–491.
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18. Martinez, D., da Silva, R. P., Klein, C., Fiori, C. Z., Massierer, D., Cassol, C. M., et al. (2012). High risk for sleep apnea in the Berlin questionnaire and coronary artery disease. Sleep and Breathing, 16, 89–94. 19. Ahmadi, N., Chung, S. A., Gibbs, A., & Shapiro, C. M. (2008). The Berlin questionnaire for sleep apnea in a sleep clinic population: Relationship to polysomnographic measurement of respiratory disturbance. Sleep and Breathing, 12, 39–45. 20. Chung, F., Yegneswaran, B., Liao, P., Chung, S. A., Vairavanathan, S., Islam, I., et al. (2008). Validation of the Berlin Questionnaire and American Society of Anesthesiologists Checklist as screening tools for obstructive sleep apnea in surgical patients. Anesthesiology, 108, 822–830. 21. Pecotic, R., Dodig, I. P., Valic, M., Ivkovic, N., & Dogas, Z. (2011). The evaluation of the Croatian version of the Epworth sleepiness scale and STOP questionnaire as screening tools for obstructive sleep apnea syndrome. Sleep and Breathing, 16, 793–802. 22. Boynton, G., Vahabzadeh, A., Hammoud, S., Ruzicka, D. L., & Chervin, R. D. (2013). Validation of the STOP-BANG Questionnaire among patients referred for suspected obstructive sleep apnea. Journal of Sleep Disorders: Treatment and Care, 2, 4. 23. Ong, T. H., Raudha, S., Fook-Chong, S., Lew, N., & Hsu, A. A. (2010). Simplifying STOP-BANG: Use of a simple questionnaire to screen for OSA in an Asian population. Sleep and Breathing, 14, 371–376. 24. Farney, R. J., Walker, B. S., Farney, R. M., Snow, G. L., & Walker, J. M. (2011). The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: Relation to polysomnographic measurements of the apnea/hypopnea index. Journal of Clinical Sleep Medicine, 7, 459–465. 25. Vana, K. D., Silva, G. E., & Goldberg, R. (2013). Predictive abilities of the STOP-Bang and Epworth Sleepiness Scale in identifying sleep clinic patients at high risk for obstructive sleep apnea. Research in Nursing & Health, 36, 84–94.
