Skip to main content
SpringerLink
Log in

Weighted STOP-Bang and screening for sleep-disordered breathing

  • Sleep Breathing Physiology and Disorders • Original Article
  • Published:
Sleep and Breathing Aims and scope Submit manuscript

Abstract

Background

STOP-Bang is a tool for predicting the likelihood for sleep-disordered breathing (SDB). In the conventional score, all variables are dichotomous. Our aim was to identify whether modifying the STOP-Bang scoring tool by weighting the variables could improve test characteristics.

Methods

Subjects who participated in the Sleep Heart Health Study (SHHS) were included in this analysis using a derivation dataset (n = 1667) and a validation dataset (n = 4774). In the derivation dataset, each STOP-Bang variable was evaluated using linear regression against the presence of SDB (AHI > 15/h) in order to determine the coefficients that would allow variable weighting. In other models, BMI, age, and neck circumference were entered as continuous variables. The sum of the weighted dichotomous variables yielded a weighted STOP-Bang (wSTOP-Bang). The sum of the weighted-continuous variables yielded a continuous STOP-Bang (cSTOP-Bang). The wSTOP-Bang, cSTOP-Bang, and the conventional STOP-Bang scores were then applied to the validation dataset, and receiver operating characteristic (ROC) curves were constructed.

Results

The area under the curve (AUC) for cSTOP-Bang (0.738) was greater than the AUC for conventional STOP-Bang (0.706) and wSTOP-Bang (0.69). The sensitivities for cSTOP-Bang, STOP-Bang, and wSTOP-Bang were similar at 93.2, 93.2, and 93.3 %, respectively. The cSTOP-Bang had a higher specificity (31.8 %) than both STOP-Bang (23.2 %) and wSTOP-Bang (23.6 %). The cSTOP-Bang had a higher likelihood ratio of a positive test (1.36) than both STOP-Bang (1.21) and wSTOP-Bang (1.22).

Conclusions

Modifying the STOP-Bang score by weighting the variables and using continuous variables for BMI, age, and neck circumference can maintain sensitivity while improving specificity, positive likelihood ratio, and area under the receiver operating characteristic curve.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Lee W, Nagubadi S, Kryger MH, Mokhlesi B (2008) Epidemiology of obstructive sleep apnea: a population-based perspective. Expert Rev Respir Med 2(3):349–364

    Article  PubMed  PubMed Central  Google Scholar 

  2. Chung F, Yegneswaran B, Liao P, Chung SA, Vairavanathan S, Islam S, Khajehdehi A, Shapiro CM (2008) STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology 108(5):812–821

    Article  PubMed  Google Scholar 

  3. Silva GE, Vana KD, Goodwin JL, Sherrill DL, Quan SF (2011) Identification of patients with sleep disordered breathing: comparing the Four-Variable screening tool, STOP, STOP-Bang, and Epworth Sleepiness Scales. J Clin Sleep Med 7(5):467–472

    PubMed  PubMed Central  Google Scholar 

  4. Farney RJ, Walker BS, Farney RM, Snow GL, Walker JM (2011) The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: relation to polysomnographic measurements of the apnea/hypopnea index. J Clin Sleep Med 7:459–465

    PubMed  PubMed Central  Google Scholar 

  5. Quan SF, Howard BV, Iber C, et al (1997) The Sleep Heart Health Study: design, rationale, and methods. Sleep 20:1077–1085

    CAS  PubMed  Google Scholar 

  6. Ong TH, Raudha S, Fook-Chong S, Lew N, Hsu AAL (2010) Simplifying STOP-BANG: use of a simple questionnaire to screen for OSA in an Asian population. Sleep Breath 14:371–376

    Article  PubMed  Google Scholar 

  7. Vana KD, Silva GE, 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. Res Nursing Health 36:84–94

    Article  Google Scholar 

  8. Redline S, Sanders MH, Lind LK, et al (1998) Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep Heart Health Research Group Sleep 21:759–767

    CAS  PubMed  Google Scholar 

  9. Rechtschaffen A, Kales A (1968) Manual of standardized techniques and scoring system for sleep stages of human subjects. UCLA Brain Information Services and Brain Research Institute, Los Angeles

    Google Scholar 

  10. Iber C, Ancoli-Israel S, Chesson Jr AL, Quan SF (2007) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications. American Academy of Sleep Medicine, Westchester

  11. Johns MW (1991) A new method for measuring daytime sleepiness: the Epworth Sleepiness Scale. Sleep 14(6):540–545

    CAS  PubMed  Google Scholar 

  12. Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP (1999) Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med 131(7):485–491

    Article  CAS  PubMed  Google Scholar 

  13. Takegami M, Hashino Y, Chin K, Sokejima S, Kodtani H (2009) Simple four-variable screening tool for identification of patients with sleep-disordered breathing. Sleep 32:939–948

    PubMed  PubMed Central  Google Scholar 

  14. Abrishami A, Khajehdehi A, Chung F (2010) A systematic review of screening questionnaires for obstructive sleep apnea. Can J Anesth 57:423–438

    Article  PubMed  Google Scholar 

  15. Chung F, Chau E, Yang Y, Liao P, Hall R, Mokhlesi B (2013) Serum bicarbonate level improves specificity of STOP-Bang screening for obstructive sleep apnea. Chest 143(5):1284–1293

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the Sleep Heart Health Study (SHHS) cohort implemented by the National Heart, Lung, and Blood Institute and supported by grants (U01HL53916, U01HL53931, U01HL53934, U01HL53937, U01HL53938, U01HL53940, U01HL53941, U01HL64360) from the National Institutes of Health.

Conflict of interest

The authors declare that they have no competing interest.

Author information

Authors and Affiliations

  1. Arizona Respiratory Center and Department of Medicine, Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, University of Arizona, 1501 North Campbell Avenue, Tucson, AZ, 85724, USA

    Ryan Nahapetian, Sairam Parthasarathy & Stuart F. Quan

  2. College of Nursing, University of Arizona, Tucson, AZ, USA

    Graciela E. Silva

  3. College of Nursing and Health Innovation, Arizona State University, Phoenix, AZ, USA

    Kimberly D. Vana

  4. Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA

    Stuart F. Quan

Authors
  1. Ryan Nahapetian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Graciela E. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kimberly D. Vana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sairam Parthasarathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stuart F. Quan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ryan Nahapetian.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nahapetian, R., Silva, G.E., Vana, K.D. et al. Weighted STOP-Bang and screening for sleep-disordered breathing. Sleep Breath 20, 597–603 (2016). https://doi.org/10.1007/s11325-015-1255-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11325-015-1255-2

Keywords

Search

Navigation