Association of STOP-Bang Questionnaire as a Screening Tool for Sleep Apnea and Postoperative Complications: A Systematic Review and Bayesian Meta-analysis of Prospective and Retrospective Cohort Studies : Anesthesia & Analgesia

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Respiration and Sleep Medicine

Association of STOP-Bang Questionnaire as a Screening Tool for Sleep Apnea and Postoperative Complications: A Systematic Review and Bayesian Meta-analysis of Prospective and Retrospective Cohort Studies

Nagappa, Mahesh MD*; Patra, Jayadeep PhD; Wong, Jean FRCPC; Subramani, Yamini MD*; Singh, Mandeep FRCPC; Ho, George BSc; Wong, David T. FRCPC; Chung, Frances FRCPC

Author Information
Anesthesia & Analgesia 125(4):p 1301-1308, October 2017. | DOI: 10.1213/ANE.0000000000002344



The risk of postoperative complications increases with undiagnosed obstructive sleep apnea (OSA). The high-risk OSA (HR-OSA) patients can be easily identified using the STOP-Bang screening tool. The aim of this systematic review and meta-analysis is to determine the association of postoperative complications in patients screened as HR-OSA versus low-risk OSA (LR-OSA).


The following data bases were searched from January 1, 2008, to October 31, 2016, to identify the eligible articles: Cochrane Central Register of Controlled Trials, MEDLINE, PubMed, Cochrane Databases of Systematic Reviews, Medline-in-Process & other nonindexed citations, Google Scholar, Embase, Web of Sciences and Scopus. The search included studies with adult surgical patients screened for OSA with STOP-Bang questionnaire that reported at least 1 cardiopulmonary or any other complication requiring intensive care unit admission as diagnosis of outcome. We used a Bayesian random-effects analysis to evaluate the existing evidence of STOP-Bang in relation to OSA and to assess the association of postoperative complications with the identified HR-OSA patients by study design and methodologies.


This systematic review and meta-analysis was conducted using 10 cohort studies: 23,609 patients (HR-OSA, 7877; LR-OSA, 15,732). The pooled odds of perioperative complications were higher in the HR-OSA versus LR-OSA patients (odds ratio 3.93, 95% credible interval, 1.85–7.77, P= .003; 6.86% vs 4.62%). The length of hospital stay was longer in HR-OSA by 2 days when compared with LR-OSA (5.0 ± 4.2 vs 3.4 ± 2.8 days; mean difference 2.01; 95% credible interval, 0.77–3.24; P= .005). Meta-regression to adjust for baseline confounding factors and subgroup analysis did not materially change the results.


This systematic review and meta-analysis suggests that HR-OSA is related with higher risk of postoperative adverse events and longer length of hospital stay when compared with LR-OSA patients. Our findings support the implementation of the STOP-Bang screening tool for perioperative risk stratification.

Obstructive sleep apnea (OSA) is characterized by intermittent upper airway collapse resulting in hypoxemia and recurrent episodes of arousal from sleep. It has a prevalence of 9%–25% in the general adult population.1,2 OSA has a variable prevalence in the surgical population. In bariatric patients undergoing surgery, its prevalence can be 70%.3

The presence of OSA and a higher postoperative adverse outcome has been documented in patients undergoing various types of elective surgeries.4–6 OSA poses an economic burden, as it increases the duration of hospital stay and health care expenditures.7 A majority of OSA patients are undiagnosed and untreated during the perioperative period.8 The Society of Anesthesia and Sleep Medicine recently published a guideline, recommending a preoperative screening for OSA.9 The STOP-Bang is a validated screening tool to identify high-risk OSA (HR-OSA) patients (STOP-Bang ≥3) in the perioperative period.10,11 The 8 dichotomous items are snoring, tiredness, observed apnea, high blood pressure, body mass index (BMI; >35 kg/m2), age (>50 years), neck circumference (male >43 cm; female >41 cm), and male sex. The score may vary from 0 to 8. Patients can be classified for OSA risk based on their corresponding scores. The STOP-Bang scores between 0 and 2 are classified as low-risk OSA (LR-OSA) and those with a score between 3 and 8 should be classified as HR-OSA.

Many patients with high STOP-Bang scores may have undiagnosed and untreated OSA contributing to comorbidities in this group. Screening for OSA allows preoperative risk stratification, anesthetic management with risk minimization, and appropriate postoperative monitoring. These measures may prevent postoperative complications. Another large study, comprising around 27,000 patients undergoing general and vascular surgeries, found that patients with untreated OSA had higher risk of cardiopulmonary complications including unplanned reintubations and myocardial infarctions compared with OSA patients on continuous positive airway pressure (CPAP) therapy.12 In a matched cohort analysis comparing undiagnosed with diagnosed OSA surgical patients, undiagnosed OSA before surgery revealed a 3-fold increased risk (P< .009) of cardiac complications consisting of mainly cardiac arrests and shock.13 A retrospective study showed that patients who satisfy 3 criteria of STOP-Bang questionnaire had more postoperative complications and increased duration of hospital stay than the OSA patients on CPAP therapy.14

Several studies in the literature have used the STOP-Bang tool to classify patients as HR-OSA and LR-OSA. These studies compared the complications associated with each category and suggested that STOP-Bang may be used as a risk identification tool during the perioperative period.15,16 Assessment of the instrument has consistently shown a linear association between increasing postoperative complications or postoperative critical care conditions with higher scores.17,18 However, meaningful conclusions may not be drawn as different studies carry different methodologies, sample sizes, data limitations, type of outcomes, and variations in the severity of postoperative complications. The objective of this meta-analysis is to determine the association of postoperative complications in patients screened as HR-OSA with the STOP-Bang questionnaire.


Search Strategy

This analysis was planned in agreement with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A systematic search was performed to identify prospective or retrospective cohort studies related to high/low risk OSA and perioperative outcomes (Figure 1). The following databases were systematically searched through from January 1, 2008, to October 31, 2016, for relevant studies: EMBASE, Medline (via PubMed), Cochrane Central Register of Controlled Trials, Medline In-process, Cochrane Database of Systematic Reviews, and CINAHL.

Figure 1.:
Flow diagram of search strategy used for systematic review and meta-analysis. AHI indicates apnea-hypopnea index; ICU, intensive care unit; PACU, postanesthesia care unit.

The search included the combination of the following MESH key words: “obstructive sleep apnea,” “STOP-Bang questionnaire,” “perioperative complications.” The selection of the studies was not restricted by country of origin or language. Two authors independently performed the literature search (M.N. and F.C.) and the articles obtained from the search were reviewed. To ensure complete search of literature, a citation search was performed on the relevant articles. First the abstracts, then the full-text of the selected studies were inspected separately by 2 reviewers (M.N. and F.C.) to decide the inclusion criteria. Any disagreements were resolved with the consultation of another author (J.W.).

Study Selection Criteria

Articles were assessed by 2 authors independently. We included only original studies (1) that used STOP-Bang questionnaire to screen for OSA; (2) that reported at least 1 postoperative adverse event in the HR-OSA and LR-OSA patients; (3) that are either prospective or retrospective cohort studies; and (4) that included an adult population aged 18 years and above. Disagreements regarding the inclusion of the articles were resolved by consulting other coauthors. We defined postoperative complications as cardiorespiratory events or any other complications requiring intensive care unit (ICU) admission. The exclusion criteria disallowed studies that reported exclusively desaturation as complication and complications without a comparison or control group.

Data Extraction

Data extracted included study ID, study type, exposure definition (STOP-Bang screening tool to classify HR-OSA and LR-OSA), diagnosis of outcome (eg, perioperative complications), and patient characteristics including number, age, sex, BMI, type of operation, preexisting medical conditions, and American Society of Anesthesiologists (ASA) class. To assess the quality of the studies included in our systematic review and meta-analysis (SRMA), we rated each study using the Newcastle–Ottawa scale.19 The study authors were contacted by email for any missing data. If needed, unadjusted odds ratio (OR) was manually calculated for inclusion in the meta-analysis, and if this was not possible, the study was excluded.

Outcome Definition

The recorded composite outcome of postoperative complications was defined as the number of subjects having at least one of the specific complications postoperatively: (1) postoperative cardiac arrhythmias defined as any patient developing arrhythmias after surgery and before hospital discharge; (2) reintubation as any patient who required the assistance of a ventilator after extubation due to respiratory failure, hypoxia, or pneumonia; and (3) other postoperative complications included myocardial infarction, ICU admission, laryngospasm, bronchospasm, prolonged mechanical ventilation, acute pulmonary edema/congestive cardiac failure (CCF). For meta-analysis, the data on desaturation were not considered as a complication unless it led to reintubations, ICU admission, or hospital readmission.

Quantitative Data Synthesis

The meta-analyses of risk estimates were conducted for postoperative outcomes and exposure to HR-OSA compared with LR-OSA patients. We applied continuity correction to studies with sparse data by the addition of 0.5 to all cells for calculation of the log OR. We statistically summarized pooled estimates through Bayesian random–effects models via Monte Carlo Markov Chain simulations with noninformative prior distributions and Gibbs sampling for the mean and variance parameters with 95% credible intervals (CrI) on all outcomes.20 We needed to additionally specify priors for θ (theta) and τ2 (gamma) in the model. The random-effects model assumes a normal prior for each individual trial effect μi. We considered fairly noninformative normal and inverse gamma prior distributions for the parameters θ and τ2: θ∼ normal (0, 10,000) and τ2 ∼ inverse gamma (0.0001, 0.0001).

Brooks–Gelman21 criteria were used to visually assess the convergence.20 We chose a Bayesian model because of the direct interpretation of CrI of the posterior-effect estimates as belief that the effect lies in that region. Unlike the frequentist approach, the Bayesian method incorporates experimental and prior information through the Bayes theorem, to come up with the posterior distribution to make all inferences about the estimate of interest. Given the smaller number of studies (and more variables) in our analysis, we deemed that the correlation to belief would be useful in the context of subjective clinical judgment. Nonoverlapping CrI indicated significant differences between groups with a 95% probability of a true difference. We also presented estimates from DerSimonian and Laird random-effects model as a direct comparison to the Bayesian model.

Heterogeneity was tested and quantified with the τ2 and I2 statistics, respectively.22 A random-effects analysis was used to estimate the OR. A method suggested by Higgins and Thompson22 was used to calculate the CI of I2. The effect of the individual studies on the final homogeneity was assessed by Galbraith plot.23 Meta-regression analysis was performed for each of the confounding factors and on the various subgroups (specific study characteristics and quality indicators including study type, presence or absence of clear definition of study outcomes, baseline versus postoperative characteristics, and comorbidity status). The publication bias was investigated using the Begg’s test and Egger’s test.24 Statistical tests were conducted using Review Manager (RevMan 5.3) and OpenBUGS v3.0.25 Statistical significance is considered if the P value (2 sided) is <.05. Supplemental Digital Content 1, Material S1,, provides the study protocol.


Figure 1 summarizes our strategy of literature search. From the initial search, we identified 119 citations. Of these citations, 21 studies were retrieved for complete review and 11 were excluded for various reasons (see Supplemental Digital Content 2, Material S2, Ten studies with a total of 23,609 patients (7877 HR-OSA versus 15,732 LR-OSA) and a variety of surgical procedures: head and neck, thoracic, abdominal, vascular, genitourinary, and orthopedic surgeries were incorporated for the meta-analysis. Of these, 8 studies were prospective15,16,26–31 and 2 were retrospective in nature.14,17 The baseline patient characteristics of the included articles are described in Table 1. The summary of baseline clinical characteristics between the HR-OSA group and LR-OSA group are compared in Table 2.

Table 1.:
Baseline Patient Characteristics
Table 2.:
Summary of the Comparison of Baseline Clinical Characteristics

Pooled analysis showed that the baseline parameters were different between the HR-OSA and LR-OSA groups for the following variables: age, male sex, BMI, ASA status, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, smoking, arrhythmias, ischemic heart disease (IHD)/CCF, and stroke/cerebrovascular accidents. A detailed systematic review of the 10 studies is described in Supplemental Digital Content 3, Table S3,, and the assessment of the quality of the studies is summarized in Supplemental Digital Content 4, Table S4,

Postoperative Outcomes

Figure 2.:
Meta-analysis of any postoperative complications and between high- and low-risk OSA patients undergoing surgery. The odds ratio of each included study is plotted. A pooled estimate of overall odds ratio (diamonds) and 95% confidence intervals (width of diamonds) summarizes the effect size using the random-effects model. CI indicates confidence interval; D-L, DerSimonian and Laird; OR, odds ratio; MCMC, Monte Carlo Markov Chain; OSA, obstructive sleep apnea.
Figure 3.:
Funnel plot for the association of postoperative complications in patients screened as high-risk obstructive sleep apnea versus low-risk obstructive sleep apnea. No evidence for substantial publication bias was found by the Begg’s test (P= .531) or Egger’s test (P= .324). Number indicates the study references in the manuscript. According to classic fail-safe N, 281 missing studies were required to bring the P value to more than α, suggesting the absence of the publication bias. OR indicates odds ratio; SE, standard error.

Figure 2A summarizes the results on the various postoperative outcomes. Overall, postoperative complications were 3.9-fold higher in the HR-OSA versus LR-OSA patients (pooled OR, 3.93; 95% CrI, 1.85–7.77, P= .003, τ2 = 1.40). The posterior distributions were fairly normal for most studies. Posteriors for almost all studies (except 2 studies)16,17 were more closely centered on the overall mean (pooled lnOR) of 1.3. Our visual diagnostics also raised no concern for these parameters. We also checked Monte Carlo Markov Chain convergence for study-specific μi (see Supplemental Digital Content 5, Material S5, No evidence for substantial publication bias was found by the Begg’s test (P= .531) or Egger’s test (P= .324) for postoperative complications (Figure 3, Supplemental Digital Content 6, Figure S6, Galbraith plot and funnel plot showed 1 study was a potential source of heterogeneity16 and another study17 to be an outlier. The distinct findings (large study [N = 13,023] with small standard error = 0.08) in the Lockhart et al16 study may have resulted in the large heterogeneity found in our pooled analysis. When this single study estimate (OR, 0.95) was excluded, the overall effect estimate was elevated and the heterogeneity score improved (pooled RR, 5.51, 95% CrI, 3.50–7.68, τ2 = 0.11; P for heterogeneity = .228, I2 = 24.2%). Further, when we excluded the outlier,17 the pooled effect estimate was stabilized and other statistics were improved (pooled RR 4.52, 95% CrI, 2.72–7.07, τ2 = 0.13; P for heterogeneity = .430, I2 = 0.0%; results not shown).

Length of Hospital Stay

A total of 1241 patients (HR-OSA versus LR-OSA: 524 vs 717) from 4 studies provided the data on the length of the hospital stay (Figure 2B).14,26,28,31 The duration of the hospital stay was 2.1 days longer in HR-OSA versus LR-OSA patients (5.0 ± 4.2 vs 3.4 ± 2.8 days; pooled mean difference 2.01; 95% CrI, 0.77–3.24; P= .005; τ2 = 0.55).


Table 3.:
Meta-regression Analysis of the Baseline Confounding Factors
Table 4.:
Quality Assessment: Meta-regression and Sensitivity Analysis of Various Subgroups

To address the issue of the differences in the baseline characteristics, we performed the meta-regression analysis for each of these confounding factors (as a continuous variable), to measure its impact on the outcomes. These confounding baseline characteristics slightly changed the OR, but not significantly affecting the overall estimate of the outcome (Table 3). Further, to maximally address these and other limitations, we performed the meta-regression analysis (as categorical variable) and sensitivity analysis on the various subgroups based on the study design, quality of the studies, study quality scores, STOP-Bang scores, measured outcome definitions, loss of patients to follow-up, and medical comorbidities. Once again these factors slightly changed the OR, but did not impact the final inference or results of postoperative complications for HR-OSA versus LR-OSA groups (Table 4). Finally, robustness of the pooled estimates was checked by influence analyses. Each of the studies was individually omitted from the data set, followed in each case by recalculation of the pooled estimate of the remaining studies.


This is the first meta-analysis comparing the incidence of postoperative complications among HR-OSA versus LR-OSA patients undergoing surgical procedures. Postoperative complications are almost 4-fold higher in HR-OSA versus LR-OSA patients. The duration of the hospital stay was 2 days longer in HR-OSA versus LR-OSA patients. Meta-regression analysis adjusting for baseline confounding factors and subgroup analysis did not materially change the results.

Despite advancements in anesthetic and perioperative care, postoperative complications are still a significant problem in OSA surgical patients.6 Two respective meta-analyses on 13 and 17 studies found that adverse cardiopulmonary events were increased by 2- to 3-fold in OSA versus non-OSA patients after noncardiac surgical procedures.5,32 The ASA’s Guidelines recommended preoperative identification of patients with OSA.12 The recent Guidelines on the Preoperative Screening and Assessment of OSA patients by the Society of Anesthesia and Sleep Medicine also recommended screening for HR-OSA patients in the preoperative period.9 In practice, identifying OSA patients at risk of perioperative complications remains a challenge and variation in clinical practices exists among the different institutions. This meta-analysis provides evidence to support the implementation of the STOP-Bang tool to identify the HR-OSA patients in the perioperative period. This will allow risk stratification and minimization for HR-OSA patients.

The higher incidence of postoperative adverse events in patients with HR-OSA may be due to various reasons. Patients with OSA may experience perioperative complications due to sedatives and opioids.6 These drugs may impair the chemoreceptor responses to hypoxia and hypercarbia, and inhibit the protective arousal reflex triggered by bouts of hypoxia. The ensuing prolonged apnea can culminate in respiratory arrest and sudden unexpected death. Sedatives and narcotics can reduce pharyngeal muscle tone, increasing upper airway collapsibility, and worsening the existing OSA.

Age, sex, BMI, and hypertension are component of the STOP-Bang scores, while diabetes mellitus, IHD, and cerebrovascular disease are some of the accepted comorbidities of OSA. This along with the other associated comorbidities like smoking and chronic obstructive pulmonary disease contributes to higher prevalence of these diseases in HR-OSA compared with the LR-OSA group resulting in the inherent differences between the groups. In our study, 52% of the patients in the HR-OSA group were ASA physical status III or greater. HR-OSA patients had a higher prevalence of cardiovascular risk factors than LR-OSA patients.33 These considerations highlight the importance of identifying and treating HR-OSA as an important step to improve management of chronic diseases, decrease complications, and reduce health care spending.

In our systematic review, the prevalence of HR-OSA was 33.3% in the surgical population, although none of the studies confirmed the diagnosis of OSA by polysomnography. The STOP-Bang score 3 or greater has the best balance between sensitivity and specificity: 84% for detecting any OSA (apnea-hypopnea index [AHI] >5 events/h), 93% for detecting moderate to severe OSA (AHI >15 events/h), and almost 100% for detecting severe OSA (AHI >30 events/h). The corresponding specificities were 56.4%, 43%, and 37%.10 If patients score between 0 and 2 on the STOP-Bang, they are classified as LR-OSA, and a possibility of moderate to severe OSA can be ruled out. STOP-Bang score 3 or greater may have false-positive patients. Depending on the prevalence of OSA in the surgical population, the cutoff of STOP-Bang score can be increased to detect moderate to severe OSA with a higher accuracy.34–36

Limited data are available on the perioperative management of HR-OSA patients. There is evidence that CPAP can be beneficial in the perioperative setting.12,13,37,38 A recent meta-analysis found that the AHI was reduced by 25 events/h and the duration of hospital stay was 0.4 days less in treated versus untreated OSA patients.39 In this meta-analysis, no information about percentage of patients treated or compliant with CPAP was available in the studies.

This SRMA has several limitations. The various surgical risk factors were not taken into consideration in this SRMA. Second, this SRMA included clinically and methodologically diverse studies. Although no evidence of bias was noticed, the pooled analysis found considerable heterogeneity, primarily attributable to differences in the patient population and variance in the summary measures. However, the pooled analysis of retrospective studies showed 0% heterogeneity and improved results. The baseline confounding factors of the patient population included in our study slightly changed the final estimate, but did not significantly interfere with the outcome. However, some of the important comorbidities like coronary artery disease, IHD, and CCF were not reported by many of the studies. These comorbidities were not taken into consideration while performing the meta-regression analysis. These unreported comorbidities and other unknown confounding factors may have introduced some bias into our mean estimate. Despite these limitations, our meta-analysis offers a comprehensive analysis of the available evidence on the association of perioperative complications in HR-OSA patients undergoing surgical procedures. In future studies, it will be useful to evaluate the various STOP-Bang scores for perioperative complications for specific surgical procedures.


This meta-regression analysis suggests that patients with HR-OSA had almost a 4-fold higher risk of postoperative complications. The duration of hospital stay was longer in HR-OSA versus LR-OSA patients. This analysis supports the implementation of the STOP-Bang questionnaire as a perioperative risk stratification tool to identify the HR-OSA patients.


The authors thank Marina Englesakis, HBA, MLIS (Information Specialist, Health Sciences Library, University Health Network, Toronto, ON, Canada) and Brie McConnell, MLIS (Media and Information Manager, London Health Science Centre, Western University) for their assistance with the literature search.


Name: Mahesh Nagappa, MD.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Name: Jayadeep Patra, PhD.

Contribution: This author helped analyze the data and write the manuscript.

Name: Jean Wong, FRCPC.

Contribution: This author helped conduct the study and write the manuscript.

Name: Yamini Subramani, MD.

Contribution: This author helped write the manuscript.

Name: Mandeep Singh, FRCPC.

Contribution: This author helped write the manuscript.

Name: George Ho, BSc.

Contribution: This author helped write the manuscript.

Name: David T. Wong, FRCPC.

Contribution: This author helped write the manuscript.

Name: Frances Chung, FRCPC.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

This manuscript was handled by: David Hillman, MD.


1. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177:1006–1014.
2. Senaratna C V, Perret JL, Lodge CJ, Lowe AJ, Campbell BE, Matheson MC, Hamilton GS, Dharmage SC. Prevalence of obstructive sleep apnea in the general population: A systematic review. Sleep Med Rev. 2017;34:70–81.
3. Hallowell PT, Stellato TA, Petrozzi MC, et al. Eliminating respiratory intensive care unit stay after gastric bypass surgery. Surgery. 2007;142:608–612.
4. Kaw R, Chung F, Pasupuleti V, Mehta J, Gay PC, Hernandez AV. Meta-analysis of the association between obstructive sleep apnoea and postoperative outcome. Br J Anaesth. 2012;109:897–906.
5. Hai F, Porhomayon J, Vermont L, Frydrych L, Jaoude P, El-Solh AA. Postoperative complications in patients with obstructive sleep apnea: a meta-analysis. J Clin Anesth. 2014;26:591–600.
6. Opperer M, Cozowicz C, Bugada D, et al. Does obstructive sleep apnea influence perioperative outcome? A qualitative systematic review for the Society of Anesthesia and Sleep Medicine Task Force on Preoperative Preparation of Patients with Sleep-Disordered Breathing. Anesth Analg. 2016;122:1321–1334.
7. Mokhlesi B, Hovda MD, Vekhter B, Arora VM, Chung F, Meltzer DO. Sleep-disordered breathing and postoperative outcomes after elective surgery: analysis of the nationwide inpatient sample. Chest. 2013;144:903–914.
8. Singh M, Liao P, Kobah S, Wijeysundera DN, Shapiro C, Chung F. Proportion of surgical patients with undiagnosed obstructive sleep apnoea. Br J Anaesth. 2013;110:629–636.
9. Chung F, Memtsoudis SG, Ramachandran SK, et al. Society of anesthesia and sleep medicine guidelines on preoperative screening and assessment of adult patients with obstructive sleep apnea. Anesth Analg. 2016;123:452–473.
10. Chung F, Yegneswaran B, Liao P, et al. STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology. 2008;108:812–821.
11. Nagappa M, Liao P, Wong J, et al. Validation of the STOP-Bang Questionnaire as a screening tool for obstructive sleep apnea among different populations: a systematic review and meta-analysis. PLoS One. 2015;10:e0143697.
12. Abdelsattar ZM, Hendren S, Wong SL, Campbell DA Jr, Ramachandran SK. The impact of untreated obstructive sleep apnea on cardiopulmonary complications in general and vascular surgery: a cohort study. Sleep. 2015;38:1205–1210.
13. Mutter TC, Chateau D, Moffatt M, Ramsey C, Roos LL, Kryger M. A matched cohort study of postoperative outcomes in obstructive sleep apnea: could preoperative diagnosis and treatment prevent complications? Anesthesiology. 2014;121:707–718.
14. Proczko MA, Stepaniak PS, de Quelerij M, et al. STOP-Bang and the effect on patient outcome and length of hospital stay when patients are not using continuous positive airway pressure. J Anesth. 2014;28:891–897.
15. Vasu TS, Doghramji K, Cavallazzi R, et al. Obstructive sleep apnea syndrome and postoperative complications: clinical use of the STOP-BANG questionnaire. Arch Otolaryngol Head Neck Surg. 2010;136:1020–1024.
16. Lockhart EM, Willingham MD, Abdallah AB, et al. Obstructive sleep apnea screening and postoperative mortality in a large surgical cohort. Sleep Med. 2013;14:407–415.
17. Seet E, Chua M, Liaw CM. High STOP-BANG questionnaire scores predict intraoperative and early postoperative adverse events. Singapore Med J. 2015;56:212–216.
18. Chia P, Seet E, Macachor JD, Iyer US, Wu D. The association of pre-operative STOP-BANG scores with postoperative critical care admission. Anaesthesia. 2013;68:950–952.
19. Wells GA, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomized Studies in Meta-analyses. 2014. Ottawa, Ontario: The Ottawa Health Research Institute; Available at: http//www.ohrica/programs/clinical_epidemiology/oxfordasp.
20. Cowles MK, Carlin BP. Markov chain Monte Carlo convergence diagnostics: a comparative review. J Am Stat Assoc. 1996;91:883–904.
21. Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat. 1998;7:434–455.
22. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558.
23. Galbraith RF. A note on graphical presentation of estimated odds ratios from several clinical trials. Stat Med. 1988;7:889–894.
24. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–634.
25. Lunn D, Spiegelhalter D, Thomas A, Best N. The BUGS project: evolution, critique and future directions. Stat Med. 2009;28:3049–3067.
26. Chudeau N, Raveau T, Carlier L, et al. The STOP-Bang questionnaire and the risk of perioperative respiratory complications in urgent surgery patients: a prospective, observational study. Anaesthesia Crit Care Pain Med. 2016;35:347–353.
27. Gokay P, Tastan S, Orhan ME. Is there a difference between the STOP-BANG and the Berlin Obstructive Sleep Apnoea Syndrome questionnaires for determining respiratory complications during the perioperative period? J Clin Nurs. 2016;25:1238–1252.
28. Xará D, Mendonça J, Pereira H, Santos A, Abelha FJ. Adverse respiratory events after general anesthesia in patients at high risk of obstructive sleep apnea syndrome. Braz J Anesthesiol. 2015;65:359–366.
29. Acar HV, Yarkan Uysal H, Kaya A, Ceyhan A, Dikmen B. Does the STOP-Bang, an obstructive sleep apnea screening tool, predict difficult intubation? Eur Rev Med Pharmacol Sci. 2014;18:1869–1874.
30. Corso RM, Petrini F, Buccioli M, et al. Clinical utility of preoperative screening with STOP-Bang questionnaire in elective surgery. Minerva Anestesiol. 2014;80:877–884.
31. Pereira H, Xará D, Mendonça J, Santos A, Abelha FJ. Patients with a high risk for obstructive sleep apnea syndrome: postoperative respiratory complications. Rev Port Pneumol. 2013;19:144–151.
32. Kaw R, Pasupuleti V, Walker E, Ramaswamy A, Foldvary-Schafer N. Postoperative complications in patients with obstructive sleep apnea. Chest. 2012;141:436–441.
33. Agrawal S, Gupta R, Lahan V, Mustafa G, Kaur U. Prevalence of obstructive sleep apnea in surgical patients presenting to a tertiary care teaching hospital in India: a preliminary study. Saudi J Anaesth. 2013;7:155–159.
34. Chung F, Chau E, Yang Y, Liao P, Hall R, Mokhlesi B. Serum bicarbonate level improves specificity of STOP-Bang screening for obstructive sleep apnea. Chest. 2013;143:1284–1293.
35. Chung F, Abdullah HR, Liao P. STOP-Bang Questionnaire: a practical approach to screen for obstructive sleep apnea. Chest. 2016;149:631–638.
36. Nagappa M, Wong J, Singh M, Wong DT, Chung F. A practical applications of STOP-Bang Questionnaire in anesthesia, surgery and perioperative medicine: a review. Curr Opin Anesthesiol. 2017;30:118–125.
37. Liao P, Luo Q, Elsaid H, Kang W, Shapiro CM, Chung F. Perioperative auto-titrated continuous positive airway pressure treatment in surgical patients with obstructive sleep apnea: a randomized controlled trial. Anesthesiology. 2013;119:837–847.
38. Chung F, Nagappa M, Singh M, Mokhlesi B. CPAP in the perioperative setting: evidence of support. Chest. 2016;149:586–597.
39. Nagappa M, Mokhlesi B, Wong J, Wong DT, Kaw R, Chung F. The effects of continuous positive airway pressure on postoperative outcomes in obstructive sleep apnea patients undergoing surgery: a systematic review and meta-analysis. Anesth Analg. 2015;120:1013–1023.

Supplemental Digital Content

Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Anesthesia Research Society.