Anesthesiologists have recently demonstrated growing interest in obstructive sleep apnea (OSA) syndrome. Patients with this pathophysiologic entity have an increased risk of postoperative complications.1–3 Upper airway obstruction may occur more readily, and/or arousal responses may be compromised, particularly when patients receive opioids4 or neuromuscular-blocking drugs during surgery.5 OSA patients are at a higher risk of hypertensive episodes,6 myocardial infarction,7 stroke events,8 and arrhythmia.9 Unfortunately, OSA is frequently underdiagnosed. Some authors estimate that 82% of men and 93% of women have undiagnosed OSA.10 OSA affects approximately 3% to 17% of the general population, depending on age and sex.11 In a population of surgical patients, Ramachandran et al.12 recorded an incidence of 7.17% of patients with a documented diagnosis of OSA. The interest in screening for OSA preoperatively is undeniable. In recent years, the American Society of Anesthesiologists (ASA) suggested OSA screening and recommended perioperative management.13–15 The preoperative anesthesia consultation may be a good time for screening.16 Effective preoperative screening of OSA may also help refine criteria for ambulatory procedure eligibility.17,18 In the current atmosphere of perioperative safety improvement, the identification of high-risk patients is, indeed, essential.
The gold-standard diagnosis of OSA relies on polysomnography (PSG),19 which has to be performed in a sleep study center.20 However, around the world, access to PSG is not always easy. An increasing number of portable devices for home screening are now commercially available.19 However, offering home screening to all patients is not readily affordable.
For this reason, the availability of a simple, fast, and efficient screening tool is of interest. The ideal tool should not be too time consuming for the clinician, should be easy to use, and should have a strong ability to predict PSG results. Practitioners could then identify patients to be referred to a sleep study center for confirmation or invalidation of the diagnosis.
Several other scores have already been developed for that purpose, including the score of Berlin,21 the STOP Questionnaire,22,23 the STOP-Bang Questionnaire,24 or the ASA checklist.14,25 There were 2 aims for our current study. First, we aimed to develop a simple, fast, and easily performed predictive score for OSA of at least comparable sensitivity (Se) and specificity (Sp), to previously described scores. Second, we sought to find a score based exclusively on the morphologic characteristics. The reason was that, although existing scores have good Se and Sp, they often require confidence in patient responses to questioning. Hence, we wanted it analogous to other routinely used anthropometric scores, such as those designed to predict difficult intubation. The studied morphologic characteristics were chosen according to the previously described anthropometric risk factors for OSA, namely obesity, large neck circumference (NC), unfavorable Mallampati (MP) score, macroglossia, and retrognathia as assessed by the distance between the thyroid and the chin (DTC).26–31 Not surprisingly, these chosen morphologic characteristics shared similarities with those indicating potential difficult intubation.32–42 We added sex to these 4 items. Male sex is a risk factor for OSA, in whole or in part attributable to morphologic differences in female sex.43 The proposed name for this score was DES-OSA, DES being the acronym for 2 of the participating investigators (ED and SD) and OSA for obstructive sleep apnea. The purpose of this study was to describe its development method and assess its performance in patients scheduled for overnight PSG.
The study was approved by our Institutional Ethics Committee (CHU Liege, Liege, Belgium, with reference number: 2006/219). The IRB waived the requirement patient consent. This study was registered before patient enrollment (EudraCT: 2006-006558-92).
One hundred forty-nine consecutive adult patients, aged 18 years or older, were recruited for our derivation group. These patients were initially admitted for an overnight PSG in our university hospital sleep study center. Their general practitioners had prescribed a PSG for heavy snoring, and witnessed or suspected sleep apnea, and also for insomnia, abnormal movements during the night, sleep of poor quality, and/or complaints of excessive fatigue.
Monitoring during PSG was achieved using a polysomnographic device S7000 (EMBLA MedCare®, Denver, CO), which allows continuous recording of a 5-channel electroencephalogram (200 Hz), left and right electro-oculogram (200 Hz), electrocardiogram (200 Hz), submental electromyogram (200 Hz), left and right tibial electromyogram, thoracic–abdominal movements, nasal air flow (Philips Respironics®, Murrysville, PA), PSG pulse wave, peripheral blood oxygen saturation (Nonin®, Plymouth, MN), and snoring sound (piezoelectric sensor from EMBLA®, Natus Europe GmbH, Planegg, Germany). Signals were recorded on a dedicated computer using the Somnologica Software (MedCare).
An investigator, not with the sleep center, first received patients presenting for their PSG. He or she examined them and recorded some of their demographic and morphologic characteristics: age, sex, ASA physical status, weight (expressed in kilograms), height (expressed in meters), NC (expressed in centimeters), DTC (<5 cm, between 5 and 6 cm, or >6 cm), modified MP score, and cervical mobility. The DTC was measured as follows: with the patient’s head in neutral position, the investigator faced him or her and measured the distance between the mental protuberance and the thyroid cartilage40 (Fig. 1). The NC was measured at the thyroid cartilage level.34 The modified MP score was defined as follows: class I corresponded to a good visualization of the soft palate, uvula, and tonsils; class II corresponded to a visibility of the hard and soft palate with pillars obscured by the base of the tongue; class III corresponded to a visible soft and hard palate and base of uvula; and class IV corresponded to the absence of soft palate visibility, with hard palate visible only.44 Cervical mobility was assessed through the evaluation of the ability to extend and flex the neck. Cervical mobility was considered reduced when maximum angle between flexion and extension was <90°.34
On the day after PSG, another investigator extracted the apnea–hypopnea index (AHI) from the PSG. AHI is defined as the number of apnea and hypopnea per hour, and corresponds to OSA syndrome severity. In sleep medicine literature, an AHI value >30 events/h reflects severe OSA.45 Moderate OSA is defined by the AHI ranging between 15 and 29.99 events/h and mild OSA by the AHI between 5 and 14.99 events/h. Severe OSA increases the complication rate during the perioperative period.2,3 In a recent study, Mutter et al.46 demonstrated that severe OSA predisposes to postoperative respiratory and cardiovascular complications. This is why, in this study, AHI served as a reference for defining OSA severity, and testing how DES-OSA would predict this severity.
The analysis of PSG-recorded data included determination of sleep stages according to the Rechtschaffen and Kales scoring rules47 at 30-second adjacent epochs. The periods of arousal were also considered, as determined by the American Sleep Disorders Association.48 They were defined as longer than 3-second bursts of α waves occurring in 1 of the electroencephalogram traces (C4-A1) during nonrapid eye movement sleep or occurring concomitantly to an increased muscle activity during rapid eye movement sleep. The criteria for the definition of apnea and hypopnea were in agreement with the American Academy of Sleep Medicine rules.31
The data were compiled in a computer file. To build the DES-OSA score, each of the morphologic parameters and possible combinations were then tested for their ability to predict the AHI value. Sample size was calculated a priori to keep the possibility to perform multiple comparisons between obtained prediction probabilities (PK). For that purpose, we chose a Bonferroni-corrected α threshold of 0.001, a relevant PK difference of 0.05, with a SD of 0.1. For a power of 0.99, a sample size of 132 was needed. We recruited 149 patients to compensate for eventual missing data. For some parameters, several weightings were tested, alone and in combinations. Construction of the score occurred progressively, adding one parameter after the other, and using PK,49 as well as area under receiver operating characteristic (ROC) curves, number of true positives (TPs), false positives (FPs), true negatives (TNs), false negatives (FNs), Se, Sp, positive predictive value, and negative predictive value (NPV) at predicting an AHI >30 events/h. The Cohen κ coefficient was also calculated to assess the degree of concordance between combinations and AHI (Supplemental Digital Content 1, http://links.lww.com/AA/B311). The finally retained parameters and weights, with best statistics at predicting an AHI >30 events/h, were the following: MP class (MP) I = 0, class II = 2, and class III and IV = 3; DTC (centimeters) >6 = 1, 5–6 = 2, and <5 = 3; body mass index (BMI; kilogram per square meter) >28 = 1, >39 = 2, and >41 = 3; NC (centimeters) >37 = 1, >42 = 2, and >48 = 3; and sex: 1 for males and 0 for females. The final score was the addition of all individual scores and constituted the DES-OSA score. In a second analysis, best DES-OSA thresholds indicating increased probability of an AHI >5, >15, or >30 events/h were determined using those with the best combination of Se, Sp, and κ coefficient (Supplemental Digital Content 2, http://links.lww.com/AA/B312). Finally, DES-OSA–AHI pairs of data for each patient were entered into a probit analysis, to define with precision the 95% confidence interval of those thresholds and the probability of observing an AHI >5, >15, or >30 events/h when the DES-OSA score is higher than the considered threshold (Supplemental Digital Content 2, http://links.lww.com/AA/B312). If that probability is >0.5, it is higher than simple heads or tails probability. In that case, the risk of at least mild, moderate-to-severe, or severe OSA is increased, depending on the considered threshold. Analyses were performed using IBM SPSS Statistics® software (version 22.0, IBM Corp., Armonk, NY) or XLSAT for Mac® (version 2015.2.02, Addinsoft SARL®, Paris, France), and MedCalc® (version 15.6.1, MedCalc, Ostend, Belgium). Confidence intervals of FN rates were calculated using the asymptotic method.
To validate our new score on an independent sample, 100 additional consecutive adult patients presenting to our university hospital sleep study center for PSG were recruited, similarly to the previous patients. The DES-OSA score of those validation patients was calculated before PSG and submitted to the same kind of analyses as mentioned earlier, namely Se, Sp, κ coefficient, area under the ROC curve, and probit analyses.
Ten patients were excluded from the derivation group for technical reasons, mainly related to inaccuracy of data collection during PSG (disconnection during PSG resulting in a record of <4 hours). The mean AHI of the whole group of patients was 26.33 events/h (range, 0–113.6). Among the recruited patients, 48 (34.53%) had severe OSA (AHI > 30 events/h). The distribution of patients according to their AHI is shown in Table 1.
The morphologic characteristics of our derivation population are summarized in Table 1. The mean BMI was 28.95 kg/m2 (range, 15.24–41.1 kg/m2), and the mean NC was 40.26 cm (range, 29–52 cm). Seventeen patients (12.23%) had an MP score of I, 82 patients (59%) had an MP score of II, and 40 patients (28.77%) had an MP score of III or IV. Eighty patients (57.55%) had a DTC >6 cm, 56 patients (40.29%) had a DTC between 5 and 6 cm, and 3 patients (2.16%) had a DTC <5 cm. No patients had limited cervical mobility that could have influenced DTC.
Detailed results of PK analyses performed at each step of index construction can be found in Supplemental Digital Content 1 (http://links.lww.com/AA/B311). The combination with the highest PK value and κ coefficient was selected as the DES-OSA score. It corresponds to the sum of 5 weighted morphologic parameters, namely the MP score, DTC, BMI, NC, and male sex. Table 2 provides a method for calculating the DES-OSA score rapidly. The distribution of patients according to their DES-OSA score is given in Table 1.
Demographic characteristics, ASA physical status, as well as AHI, MP score, DTC, BMI, NC, and DES-OSA score distributions were not the same in the derivation and validation groups (Table 1). Detailed results of the analyses performed on post hoc validation patient data are reported in Supplemental Digital Content 3 (http://links.lww.com/AA/B313). However, obtained Se, Sp, area under the ROC curve, and probit models were highly comparable (Tables 3–5).
Ses, Sps, and κ coefficient analyses revealed that the optimal DES-OSA thresholds for detecting increased risk of an AHI >5, >15, or >30 events/h with optimal Se and Sp were >5, >6, and >7, respectively (Table 3). Full details on those analyses can be found in Supplemental Digital Content 2 (http://links.lww.com/AA/B312). With a DES-OSA >5 for detecting at least mild OSA, the number of TPs was 91 (65.5%), TNs 21 (15.1%), FPs 8 (5.7%), and FNs 19 (13.7%). With a threshold of 6 for detecting moderate-to-severe OSA, the number of TPs was 64 (46.0%), TNs 41 (29.5%), FPs 15 (10.8%), and FNs 19 (13.7%). With a threshold of 7 for detecting severe OSA, the number of TPs was 36 (25.9%), TNs 70 (50.4%), FPs 21 (15.1%), and FNs 12 (8.6%). The Ses (95% confidence interval [CI]) of DES-OSA at detecting at least mild, moderate-to-severe, or severe OSA were 82.7% (74.5–88.7), 77.1% (66.9–84.9), and 75% (61.0–85.1), respectively. The corresponding Sps were 72.4% (54.0–85.4), 73.2% (60.3–83.1), and 76.9% (67.2–84.4). The associated positive predictive values (95% CI) were 91.9% (86.6–97.3), 81.0% (72.4–89.7), and 63.2% (50.6–75.7), and the NPVs were 52.5% (37.0–68.0), 68.3% (56.6–80.1), and 85.4% (77.7–93.0). The same calculations were also performed for other DES-OSA thresholds, and detailed results can be found in Supplemental Digital Content 2 (http://links.lww.com/AA/B312). By using the DES-OSA score, ROC curve analyses led to an area under the curve (95% CI) of 0.832 (0.762–0.902), 0.805 (0.734–0.876), and 0.834 (0.757–0.911) for DES-OSA at predicting an AHI >5, >15, and >30 events/h, respectively (Table 4). ROC curves are illustrated in Figure 2. The same calculations performed on data issued from the validation group yielded very similar results, as reported in Tables 3 to 5, with no statistically significant differences.
Complete results of probit analyses can be found in Supplemental Digital Content 2 (http://links.lww.com/AA/B312). According to our probit models, a DES-OSA value >5 is associated with a 80% probability of mild OSA, >6 with a 70% probability of moderate-to-severe OSA, and >7 with a 50% probability of severe OSA (Table 5). Probit analyses of validation group data yielded very similar results.
In this study, we present a new morphologic predictive score for detecting at least mild, moderate-to-severe, or severe OSA (AHI >5, >15, or >30 events/h, respectively), the DES-OSA score. This score ranges between 1 and 13, and a threshold value >5, >6, or >7 seems to be appropriate to identify patients at an increased risk of at least mild, moderate-to-severe, or severe OSA, respectively. Indeed, in this study, the DES-OSA score had a Se (95% CI) of 82.7% (74.5–88.7), 77.1% (66.9–84.9), and 75.0% (61.0–85.1), and a Sp (95% CI) of 72.4% (54.0–85.4), 73.2% (60.3–83.1), and 76.9% (67.2–84.4) for detecting patients with an AHI >5, >15, or >30 events/h. Among those thresholds, the one for detecting severe OSA is probably the most important, because the patients with severe OSA have the highest perioperative risk and may be not eligible for ambulatory surgery if their conditions are not optimized, or if their surgery requires postoperative narcotics.17,46 The study of Mutter et al.46 confirmed (if need be) the importance of OSA screening on patient outcomes. However, no major study has focused on the impact of a systematic preoperative OSA screening on a patient’s outcome.
This new score has 4 main advantages. First, with the exception of NC, the other 4 variables of this score, namely DTC, MP score, BMI, and sex are routinely collected during the preoperative visit in some centers. Therefore, DES-OSA can be easily implemented during the preoperative examination and can be performed within a short time.
Second, compared with current scores, DES-OSA seems to perform at least equally in terms of Se and Sp. Although an adequate between-score comparison would necessitate a specifically designed study, this future study will measure all scores within a single population sample. Information on their respective predictive ability can be inferred from their already reported performance, all the more so as the criterion for severe OSA is always the same, namely an AHI >30 events/h. Currently, we are conducting a study comparing these 4 scores within the same population sample.
Chung et al.,22 in 2008, proposed the STOP Questionnaire. This questionnaire is based on the presence of 4 symptoms: Snoring, Tired, Observed apnea, and high blood Pressure. Thereafter, Chung et al.22,24 developed the STOP-Bang Questionnaire, which is currently the most used score for preoperative screening of OSA. This score combines 4 other criteria with the STOP Questionnaire: BMI >35 kg/m2, age >50 years, NC >40 cm, and male gender. The STOP-Bang has good Se and Sp at detecting an AHI >30 events/h.24 The Sleep Disorders Questionnaire (SDQ),50 which performs better in females than males, was developed by Douglass et al.50 in 1994.15 Despite these useful predictive abilities, the 175 items composing the SDQ do not make it applicable for routine anesthetic practice. More recently, Ramachandran et al.12 published the Perioperative Sleep Apnea Prediction (P-SAP) score. This score evaluates 9 independent perioperative predictors, namely thyromental distance (<6 cm), age (>43 years), male sex, high blood pressure, MP score III or IV, type 2 diabetes mellitus, BMI (>30 kg/m2), the presence of a thick neck, and snoring. One point is assigned to each of those risk factors. When choosing a threshold of >4, it also has useful screening abilities.12 P-SAP incorporates, among others, the same morphologic criteria as the DES-OSA score, although with different weightings. P-SAP also includes patient responses to questioning (search for a history of diabetes mellitus, high blood pressure, and snoring). Appreciation of these elements can be subjective, and confidence in patient responses to questioning may sometimes be uncertain. At some point, there might be a need to objectively identify these risk factors, through blood tests performance for diabetes mellitus, spaced out blood pressure measurements, and PSG. This may reduce the simplicity and speed of P-SAP assessment. The OSA50, developed by Chai-Coetzer et al.,51 considers weighted variables: Obesity, Snoring, Apneas, and Age >50 years. For a cutoff score >5, its Se at detecting an AHI >30 events/h is 100%, but Sp is poor.51 In the study by Chai-Coetzer et al., this score must be coupled with home oximetry, which limits its easiness and practicability.
No predictive score will ever have 100% Se and Sp. Indeed, the pharyngeal wall is heavily involved in the genesis of apneas, and some factors influencing pharyngeal collapsibility are not detectable by questioning or by simple clinical examination. Such factors are, for example, lung volume, ventilation control stability (loop gain theory), rostral fluid shifts, and genetic influence.52 As already mentioned earlier, DES-OSA has the advantage of being brief and easy to implement for the practitioner. This is similar to STOP, STOP-Bang, P-SAP, or OSA50 scores. Our score seems to have an advantageous Se/Sp combination relative to these tests (as defined by the Youden Index [= Se + Sp − 1], but this point must be confirmed in a future study) and is easier to perform. In contrast, the Berlin questionnaire, ASA checklist, and SDQ require long testing durations and are therefore not routinely easily includable in the preanesthetic evaluation.
The third quality of the DES-OSA is its development from a sleep study center general population and not from a selected severe OSA high-risk population. As described in the Methods section, the patients were referred for a PSG for suspected apnea, but also for insomnia, abnormal movements during the night, poor-quality sleep, and/or excessive fatigue complaints. However, to test whether the DES-OSA is closer to the reality of anesthetic practice than other tests, a dedicated study should be performed. This study should be designed to assess its performance in a general surgical population, that is, in patients seen during the preanesthetic visit. In addition, because the DES-OSA was developed from a European population, it may be better adapted than other scores to European practice. Indeed, ethnic factors may influence apnea mechanisms and incidence.53 Ravesloot et al.54 evaluated some individual factors regarding their predictive value for OSA in a European bariatric population. Individually, they did not find any correlation between these parameters and OSA. Therefore, our weighted score might be the first screening score for predicting severe OSA in a European population. It should be tested in other ethnic groups to check whether it performs adequately in those populations.
Finally, the DES-OSA is based on the morphologic criteria only. This excludes possible bias related to ignorance of the patient’s health status, language barrier, and dependence on a third party to conduct an adequate preanesthetic interview.
The 5 morphologic criteria included in our score have already been used in other scores, including the P-SAP score.12,15 The NC and sex have been included by Chung et al.24 in the STOP-Bang Questionnaire. Obesity is now a well-recognized OSA risk factor.55 In 2003, a relationship between the MP score and OSA was demonstrated.56 This is also true for retrognathia, particularly in nonobese patients.53 The DES-OSA does not involve original or new criteria, but proposes new weighting of existing criteria. The new weighting remains categorical, but with more categories than a simple “yes” or “no,” as in the STOP-Bang, P-SAP, and OSA50.
This study has limitations. First, one may argue that there was biased selection of criteria included in the final score. This selection was based on our willingness to find an easy-to-perform test, based on the morphologic criteria only. The selection was performed in a structured manner, adding one criterion after the other, and checking for an increase in prediction ability using PK value at each step. Because of this, we found a score with a PK of >0.74 for OSA, which is a reasonably high predictive ability. Further Se/Sp, ROC curve, κ coefficient, and probit analyses confirmed the predictive qualities of the DES-OSA for at least mild, moderate-to-severe, and severe OSA in the initial sample of patients and in the validation sample. Second, some of the chosen criteria may not be reliably obtained in some patients. For example, limited cervical mobility could sidestep the issue of measuring DTC. We were not able to address this issue in this study, because none of our patients had limited cervical mobility. This point should be addressed in a future study. Third, threshold values >5, >6, and >7 were chosen for at least mild, moderate-to-severe, and severe OSA respectively. This choice was based on the results of Se/Sp and κ coefficient analyses. However, the low FN rates of 13.7% (7.98–19.4), 13.7% (7.98–19.4), and 8.6% (3.9–13.3) for an AHI >5, >15, and >30 events/h and the high NPVs (95% CI) of 52.5% (37.0–68.0), 68.3% (56.6–80.1), and 85.4% (77.7–93.0) for an AHI >5, >15, and >30 events/h) with those thresholds seems to support choosing them. Finally, this study was performed in a sleep center, in a population where the incidence of OSA is higher than in the general population. This may have biased our results. The only way of overcoming this limitation is a validation of the DES-OSA in other populations, first in a general population and second in patients scheduled to undergo surgery. The DES-OSA score has promising abilities for detecting OSA. With these ideas in mind, prospective studies will be performed in our institution to compare several current predictive scores in a single population sample and to assess their efficacy at detecting OSA in a general and surgical population.
In conclusion, this study describes a new scoring system to predict OSA, based on the morphologic characteristics. It does not require patient responses to questions and is quick and easy to use. Although derived and validated in a general sleep clinic population, it may have utility in the perioperative setting. Its Se and Sp seem comparable with current screening methods, and it could prove useful in identifying preoperative patients at risk of OSA. A threshold value of >5, >6, and >7 should allow identifying at least mild, moderate-to-severe, and severe OSA patients, respectively, with confidence, a low incidence of FNs and a reasonable amount of FPs, hence enabling the best use of PSG diagnostic resources. The anesthesiologist’s choice to refer the patient to a sleep center will be guided by the expected severity of OSA according to the result of the DES-OSA score, but also by postoperative opioid requirement and type of surgery. For this, the result of the OSA Scoring System (proposed by the ASA) could be helpful. This 6-point scale gives a theoretical probability of postoperative complications among OSA patients.13 Future studies must be performed to address this point.57
Name: Eric Deflandre, MD, FCCP.
Contribution: This author helped design the study, conduct the study, and write the manuscript.
Attestation: Eric Deflandre has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
Name: Stephanie Degey, MHS.
Contribution: This author helped design the study and conduct the study.
Attestation: Stephanie Degey has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Jean-Francois Brichant, MD, PhD.
Contribution: This author helped design the study and write the manuscript.
Attestation: Jean-Francois Brichant has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Robert Poirrier, MD, PhD.
Contribution: This author helped design the study and conduct the study.
Attestation: Robert Poirrier has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Vincent Bonhomme, MD, PhD.
Contribution: This author helped design the study, analyze the data, and write the manuscript.
Attestation: Vincent Bonhomme has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
This manuscript was handled by: David Hillman, MD.
The authors want to acknowledge Sandrine Remy, Pol Hans, and Laurent Cambron as participating investigators.
1. Liao P, Yegneswaran B, Vairavanathan S, Zilberman P, Chung F. Postoperative complications in patients with obstructive sleep apnea: a retrospective matched cohort study. Can J Anaesth. 2009;56:819–28
2. Memtsoudis SG, Stundner O, Rasul R, Chiu YL, Sun X, Ramachandran SK, Kaw R, Fleischut P, Mazumdar M. The impact of sleep apnea on postoperative utilization of resources and adverse outcomes. Anesth Analg. 2014;118:407–18
3. 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;144:903–14
4. Dahan A, Aarts L, Smith TW. Incidence, reversal, and prevention of opioid-induced respiratory depression. Anesthesiology. 2010;112:226–38
5. Eikermann M, Blobner M, Groeben H, Rex C, Grote T, Neuhäuser M, Beiderlinden M, Peters J. Postoperative upper airway obstruction after recovery of the train of four ratio of the adductor pollicis muscle from neuromuscular blockade. Anesth Analg. 2006;102:937–42
6. Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342:1378–84
7. Leung RS, Bradley TD. Sleep apnea and cardiovascular disease. Am J Respir Crit Care Med. 2001;164:2147–65
8. Durgan DJ, Bryan RM. Cerebrovascular consequences of obstructive sleep apnea. J Am Heart Assoc. 2012;1:1–15
9. Bazan V, Grau N, Valles E, Felez M, Sanjuas C, Cainzos-Achirica M, Benito B, Jauregui-Abularach M, Gea J, Bruguera-Cortada J, Marti-Almor J. Obstructive sleep apnea in patients with typical atrial flutter: prevalence and impact on arrhythmia control outcome. Chest. 2013;143:1277–83
10. Young T, Evans L, Finn L, Palta M. Estimation of the clinically diagnosed proportion of sleep apnea syndrome in middle-aged men and women. Sleep. 1997;20:705–6
11. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177:1006–14
12. Ramachandran SK, Kheterpal S, Consens F, Shanks A, Doherty TM, Morris M, Tremper KK. Derivation and validation of a simple perioperative sleep apnea prediction score. Anesth Analg. 2010;110:1007–15
13. American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea.. Practice guidelines for the perioperative management of patients with obstructive sleep apnea: an updated report by the American Society of Anesthesiologists Task Force on Perioperative Management of Patients with Obstructive Sleep Apnea. Anesthesiology. 2014;120:268–86
14. Munish M, Sharma V, Yarussi KM, Sifain A, Porhomayon J, Nader N. The use of practice guidelines by the American Society of Anesthesiologists for the identification of surgical patients at high risk of sleep apnea. Chron Respir Dis. 2012;9:221–30
15. Ramachandran SK, Josephs LA. A meta-analysis of clinical screening tests for obstructive sleep apnea. Anesthesiology. 2009;110:928–39
16. Mehta V, Subramanyam R, Shapiro CM, Chung F. Health effects of identifying patients with undiagnosed obstructive sleep apnea in the preoperative clinic: a follow-up study. Can J Anaesth. 2012;59:544–55
17. Ankichetty S, Chung F. Considerations for patients with obstructive sleep apnea undergoing ambulatory surgery. Curr Opin Anaesthesiol. 2011;24:605–11
18. Kurrek MM, Cobourn C, Wojtasik Z, Kiss A, Dain SL. Morbidity in patients with or at high risk for obstructive sleep apnea after ambulatory laparoscopic gastric banding. Obes Surg. 2011;21:1494–8
19. Flemons WW, Littner MR. Measuring agreement between diagnostic devices. Chest. 2003;124:1535–42
20. Vaughn BV, Giallanza P. Technical review of polysomnography. Chest. 2008;134:1310–9
21. Netzer NC, Stoohs RA, Netzer CM, Clark K, Strohl KP. Using the Berlin Questionnaire to identify patients at risk for the sleep apnea syndrome. Ann Intern Med. 1999;131:485–91
22. Chung F, Yegneswaran B, Liao P, Chung SA, Vairavanathan S, Islam S, Khajehdehi A, Shapiro CM. STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology. 2008;108:812–21
23. Ahmadi N, Chung SA, Gibbs A, Shapiro CM. The Berlin questionnaire for sleep apnea in a sleep clinic population: relationship to polysomnographic measurement of respiratory disturbance. Sleep Breath. 2008;12:39–45
24. Chung F, Subramanyam R, Liao P, Sasaki E, Shapiro C, Sun Y. High STOP-Bang score indicates a high probability of obstructive sleep apnoea. Br J Anaesth. 2012;108:768–75
25. Gross JB, Bachenberg KL, Benumof JL, Caplan RA, Connis RT, Coté CJ, Nickinovich DG, Prachand V, Ward DS, Weaver EM, Ydens L, Yu SAmerican Society of Anesthesiologists Task Force on Perioperative Management. . Practice guidelines for the perioperative management of patients with obstructive sleep apnea: a report by the American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Anesthesiology. 2006;104:1081–93
26. Malhotra A, White DP. Obstructive sleep apnoea. Lancet. 2002;360:237–45
27. Tsuiki S, Isono S, Ishikawa T, Yamashiro Y, Tatsumi K, Nishino T. Anatomical balance of the upper airway and obstructive sleep apnea. Anesthesiology. 2008;108:1009–15
28. Nuckton TJ, Glidden DV, Browner WS, Claman DM. Physical examination: Mallampati score as an independent predictor of obstructive sleep apnea. Sleep. 2006;29:903–8
29. Kapur VK. Obstructive sleep apnea: diagnosis, epidemiology, and economics. Respir Care. 2010;55:1155–67
30. Sakakibara H, Tong M, Matsushita K, Hirata M, Konishi Y, Suetsugu S. Cephalometric abnormalities in non-obese and obese patients with obstructive sleep apnoea. Eur Respir J. 1999;13:403–10
31. Epstein LJ, Kristo D, Strollo PJ Jr, Friedman N, Malhotra A, Patil SP, Ramar K, Rogers R, Schwab RJ, Weaver EM, Weinstein MDAdult Obstructive Sleep Apnea Task Force of the American Academy of Sleep Medicine. . Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med. 2009;5:263–76
32. Chung F, Yegneswaran B, Herrera F, Shenderey A, Shapiro CM. Patients with difficult intubation may need referral to sleep clinics. Anesth Analg. 2008;107:915–20
33. Siyam MA, Benhamou D. Difficult endotracheal intubation in patients with sleep apnea syndrome. Anesth Analg. 2002;95:1098–102
34. Gonzalez H, Minville V, Delanoue K, Mazerolles M, Concina D, Fourcade O. The importance of increased neck circumference to intubation difficulties in obese patients. Anesth Analg. 2008;106:1132–6
35. Hiremath AS, Hillman DR, James AL, Noffsinger WJ, Platt PR, Singer SL. Relationship between difficult tracheal intubation and obstructive sleep apnoea. Br J Anaesth. 1998;80:606–11
36. Nuckton TJ, Glidden DV, Browner WS, Claman DM. Physical examination: Mallampati score as an independent predictor of obstructive sleep apnea. Sleep. 2006;29:903–8
37. Rodrigues MM, Dibbern RS, Goulart CW. Nasal obstruction and high Mallampati score as risk factors for obstructive sleep apnea. Braz J Otorhinolaryngol. 2010;76:596–9
38. De Jong A, Molinari N, Pouzeratte Y, Verzilli D, Chanques G, Jung B, Futier E, Perrigault PF, Colson P, Capdevila X, Jaber S. Difficult intubation in obese patients: incidence, risk factors, and complications in the operating theatre and in intensive care units. Br J Anaesth. 2015;114:297–306
39. Shah PN, Sundaram V. Incidence and predictors of difficult mask ventilation and intubation. J Anaesthesiol Clin Pharmacol. 2012;28:451–5
40. Lewis M, Keramati S, Benumof JL, Berry CC. What is the best way to determine oropharyngeal classification and mandibular space length to predict difficult laryngoscopy? Anesthesiology. 1994;81:69–75
41. Tripathi M, Pandey M. Short thyromental distance: a predictor of difficult intubation or an indicator for small blade selection? Anesthesiology. 2006;104:1131–6
42. Baker PA, Depuydt A, Thompson JM. Thyromental distance measurement—fingers don’t rule. Anaesthesia. 2009;64:878–82
43. Lin CM, Davidson TM, Ancoli-Israel S. Gender differences in obstructive sleep apnea and treatment implications. Sleep Med Rev. 2008;12:481–96
44. Samsoon GL, Young JR. Difficult tracheal intubation: a retrospective study. Anaesthesia. 1987;42:487–90
45. Adesanya AO, Lee W, Greilich NB, Joshi GP. Perioperative management of obstructive sleep apnea. Chest. 2010;138:1489–98
46. 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;121:707–18
47. Rechtshaffen A, Kales A A Manual of Standardized Terminology and Scoring System for Sleep Stages of Human Subjects. 1968 Washington, DC U.S. Government Printing Office, NIH Publication No. 204
48. . EEG arousals: scoring rules and examples: a preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep. 1992;15:173–84
49. Smith WD, Dutton RC, Smith NT. Measuring the performance of anesthetic depth indicators. Anesthesiology. 1996;84:38–51
50. Douglass AB, Bornstein R, Nino-Murcia G, Keenan S, Miles L, Zarcone VP Jr, Guilleminault C, Dement WC. The Sleep Disorders Questionnaire. I: creation and multivariate structure of SDQ. Sleep. 1994;17:160–7
51. Chai-Coetzer CL, Antic NA, Rowland LS, Catcheside PG, Esterman A, Reed RL, Williams H, Dunn S, McEvoy RD. A simplified model of screening questionnaire and home monitoring for obstructive sleep apnoea in primary care. Thorax. 2011;66:213–9
52. Kapur VK. Obstructive sleep apnea: diagnosis, epidemiology, and economics. Respir Care. 2010;55:1155–67
53. Cakirer B, Hans MG, Graham G, Aylor J, Tishler PV, Redline S. The relationship between craniofacial morphology and obstructive sleep apnea in whites and in African-Americans. Am J Respir Crit Care Med. 2001;163:947–50
54. Ravesloot MJ, van Maanen JP, Hilgevoord AA, van Wagensveld BA, de Vries N. Obstructive sleep apnea is underrecognized and underdiagnosed in patients undergoing bariatric surgery. Eur Arch Otorhinolaryngol. 2012;269:1865–71
55. Isono S. Obstructive sleep apnea of obese adults: pathophysiology and perioperative airway management. Anesthesiology. 2009;110:908–21
56. Liistro G, Rombaux P, Belge C, Dury M, Aubert G, Rodenstein DO. High Mallampati score and nasal obstruction are associated risk factors for obstructive sleep apnoea. Eur Respir J. 2003;21:248–52
57. Deflandre E, Brichant JF, Bonhomme V. Obstructive sleep apnea and detection of non-adherence to CPAP in OSA: limits of the preanesthesia visit. Minerva Anestesiol. 2015;81:1042–3
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