The Difficult Airway Management Trial: "The DIFFICAIR-Trial"
The Difficult Airway Management Trial: "The DIFFICAIR-Trial"
In general anesthetic the patient is deprived of his awareness and ability to breathe. It is
 therefore one of the most important tasks in anesthesia to ensure the patient's airway and
 breathing. It has been shown both in Denmark and internationally that failed management of
 the patient's airway is the main anesthesia-related cause of death and brain damage.
 Therefore, it is very important and highly prioritized among anesthesia personal, to be able
 to identify patients with a difficult airway. The aim of "The DIFFICAIR Trial" is to reduce
 the incidence of UNEXPECTED difficult airway management by optimizing assessment of the
 patient's airway before anesthesia. There is an international consensus on the importance of
 proper identification of patients with a difficult airway prior to anesthesia. Enabling
 optimal preparation and thus reducing mortality and complications. The DIFFICAIR Trial is a
 nationwide multicentre trial with approx. 70,000 patients. 28 of the country's anesthesia
 departments is randomized either to airway assessment based on the physicians' clinical
 judgment (current practice) or to use an objective risk score for airway evaluations
 including anatomical conditions known to be associated with difficult airway management. Data
 from The Danish Anesthesia Database is used to compare the success rates of the two methods.
 We hope that by using a systematic airway assessment we may reduce the number of unexpected
 difficult airway managements and thereby reducing the associated complications and death.
 Based on data we will contribute to a national recommendation for airway assessment before
 anesthesia.
The full trial protocol is published in Trials (2013) Volume: 14, Issue: 1, Pages: 347 ISSN:
 1745-6215 DOI: 10.1186/1745-6215-14-347 PubMed: 24152537
 http://www.trialsjournal.com/content/14/1/347/abstract

 Detailed statistical analysis plan for the difficult airway management (DIFFICAIR) trial:

 Introduction The difficult airway management trial (DIFFICAIR) is a stratified, parallel
 group, cluster (cluster = department) randomized and multicentre trial involving 28
 departments of anaesthesia in Denmark. The DIFFICAIR trial compares the effect of two
 regimens of preoperative airway assessment on the frequency of unanticipated difficult airway
 management.

 Prediction of difficult airway management remains a pivotal challenge in anaesthesia.
 Difficult tracheal intubation and difficult mask ventilation may cause serious patient
 complications [1-6]. By allocating experienced personnel and relevant equipment, better
 prediction of difficult airway management may reduce complications and thereby associated
 morbidity and mortality. There is no single predictor that is sufficiently valid in
 predicting difficult tracheal intubation[7-12]. However, several studies show that by
 combining multiple predictors of difficult tracheal intubation, the positive and the negative
 predictive value of the assessment increases[12]. In Denmark as well as internationally,
 there is no clear recommendation on how to perform airway assessment. Consequently, airway
 assessment in Denmark is based exclusively on the individual anaesthesiologist's preoperative
 clinical assessment. It is poorly documented how accurately this preoperative clinical
 assessment predicts actual airway management conditions.

 The "Simplified Airway Risk Index" (SARI)[13] is based on a multivariate model for airway
 assessment described by El-Ganzouri and colleagues enabling an estimation of the likelihood
 of a difficult direct laryngoscopy. The SARI contains seven individual predictors for a
 difficult direct laryngoscopy, each given a weighted score of 0-1 or 0-2. A summarized value
 of the SARI score > 3 indicate that a future direct laryngoscopy will be difficult. It is
 unknown, whether the SARI score predicts difficult intubation better or worse than a clinical
 assessment. The rationale for this trial was to prospectively compare the effect of the SARI
 with an unspecified clinical airway assessment on the frequency of unanticipated difficult
 airway management.

 The target population was adult patients undergoing anaesthesia. Twenty-eight departments of
 anaesthesia were randomized to one of two groups. Intervention departments used the SARI
 score for preoperative airway assessment. The intervention group additionally did an
 assessment of risk factors for difficult mask ventilation as described by Kheterpal and
 colleagues[14-16]. Departments in the control group continued normal practice of preoperative
 airway assessment. All data were registered in the DAD. A more detailed trial protocol
 describing background, design and rationale has been published in TRIALS[17].

 In order to prevent outcome reporting bias and results based on data driven analysis it has
 been increasingly encouraged to prospectively publish a trial protocol [18, 19]. The same
 argument applies for a prospective publication of a statistical analysis plan. Concordantly,
 the International Conference on Harmonisation (ICH) of Good Clinical Practice (GCP)
 recommends that clinical trials are analysed according to a pre-specified plan[19].

 This analysis plan has been written while the data collection from the DIFFICAIR trial was
 still on-going and trial data not accessible. The data analysis of the main publication will
 follow this plan. The statistical analysis was approved by the DIFFICAIR steering committee
 on 29 December 2013. The last day of data collection was 31 December 2013. The involved
 departments were given one additional month to ensure registration of all patients in the
 Danish Anaesthesia Database. On 31 January 2014 the database was locked and data extracted.
 The statistical analysis plan was published on www.clinicaltrials.gov before the last data
 entry and before data was extracted and data management commenced.

 The DIFFICAIR trial protocol has been written according to the SPIRIT guidelines and has been
 public on www.difficair.com since the beginning of the trial and is registered at
 www.clinicaltrials.gov (NCT01718561). The Danish Anaesthesia Database and the Danish Society
 of Anaesthesiology and Intensive care Medicine (DASAIM) endorsed the trial.

 The trial is carried out in accordance with the Helsinki declaration. The Scientific Ethics
 Committee of Copenhagen County has declared that it is regarded as a quality assurance
 project and thus should not be reported to the committee system (Journal No.: H-3-2012-FSP2).
 The trial is approved by The Danish Data Protection Agency (J.No.: 2007-58- 0015/HIH-2011-10,
 I-Suite nr: 02079)

 Objective The primary aim of the DIFFICAIR trial is to compare the effect of using a
 systematic airway assessment with a standard clinical airway assessment on the frequency of
 unanticipated difficult airway management. Both are registered in the database. The primary
 null hypothesis is • There is no difference in the proportion of unanticipated difficult
 intubations when the preoperative airway assessment is based on the SARI score compared with
 a preoperative airway assessment based on the individual anaesthesiologist's assessment.

 The alternative hypothesis is

 • The use of a systematic SARI airway assessment, registration of the SARI and risk factors
 for difficult mask ventilation, and continuous education in airway assessment will decrease
 the relative risk of a difficult intubation with 30%, corresponding to a NNT of 180 patients.

 Methods Randomisation and sample size Our sample size calculation was based on an adjustment
 for the stratification and the cluster randomized design [20, 21]. Since there are no
 previous records of the trial's primary outcome measure, "unanticipated difficult intubation"
 a baseline study was conducted based on data from the Danish Anaesthesia Database (DAD). In
 order to reject or detect a 30 % relative risk reduction in the proportions of unanticipated
 difficult intubation between the intervention group and the control group approximately 30
 departments were required in a 15 months period. Calculations were based on a maximum risk of
 type 1 error of 5% and risk of type 2 error of maximum 20 % (80 % power).

 A total of 28 departments were included and randomized 1:1 using a computer generated list.
 The sample size calculation was based on an average cluster size of 1,611 patients. We
 estimated the average cluster size in the DIFFICAIR trial to approximately 2,500 patients,
 giving a total of 70,000 included patients during the trial period. The enhanced sample size
 allows for a potentially slight loss of clusters according to the power calculation, from 30
 to potentially 26. Our sample size estimation might be of a conservative nature, calling for
 more clusters than necessary[22].

 Populations The DIFFICAIR trial focuses on two essential elements of airway management i.e.
 tracheal intubation by direct laryngoscopy and mask ventilation. This statistical analysis
 plan will address analysis of the data regarding tracheal intubation. Data analysis regarding
 prediction of difficult mask ventilation will be handled in an analogous way, but will not be
 further elaborated in the present paper.

 The part of the DIFFICAIR trial regarding prediction of difficult intubation comprises two
 populations; 1) patients that were primarily attempted intubated by direct laryngoscopy; 2)
 patients that were primarily attempted intubated by direct laryngoscopy (population 1) plus
 patients anticipated to be difficult to intubate and therefore scheduled for and intubated
 with an advanced method (e.g. video laryngoscopic or fibre optic intubation).

 The results of population 1 and 2 will be presented in one publication. Due to the extent of
 data, further publications presenting data from the DIFFICAIR trial will follow, but further
 elaboration on data analysis exceeds the content frame of this paper.

 Adjusting and stratification variables Each cluster (department) was randomized to a control
 or intervention group, making this the intervention group indicator. The trial site may
 account for further intervention heterogeneity and will be used for adjustment in the
 analysis of the intervention effect. Further, a stratification variable that grouped the
 departments according to whether the proportion of unanticipated difficult intubation at
 baseline was ≥ or < 2% will be used for adjustment according to recent evidence of increased
 power in the analysis of stratified trials[21].

 Assumed confounding covariates We define age; gender; ASA classification; emergency/elective
 procedure; Body Mass Index (BMI); and use of neuromuscular blocking agents as covariates that
 are possible confounders, necessitating adjusted analyses of the primary outcome and
 pre-defined subgroup analyses.

 Primary outcomes

 The primary outcome measures are:

 1. Fraction of unanticipated difficult intubations = all intubations with unanticipated
 difficulties [False negative] / all patients primarily (attempted) intubated by direct
 laryngoscopy

 2. Fraction of unanticipated easy intubations = all intubations with anticipated
 difficulties that were easy [False Positive] / all patients primarily (attempted)
 intubated by direct laryngoscopy

 2. The two primary outcomes are linked and simultaneous low fractions are desirable for the
 optimal prediction of a difficult intubation.

 Secondary outcomes

 1. 48 hour mortality

 2. 30 day mortality

 3. Fraction = anticipated difficult intubations planned for, and intubated by an advanced
 method / all patients (attempted) intubated.

 4. Fraction = unanticipated difficult intubations [False Negative] / all difficult
 intubations ([False negative] + [True Positive])

 5. Sensitivity

 6. Specificity

 7. Positive predictive value

 8. Negative predictive value

 9. Positive Likelihood Ratio = (Sensitivity / (1-Specificity))

 10. Negative Likelihood Ratio = ((1-Sensitivity) / Specificity)

 11. The Receiver Operating Characteristic (ROC) curve. A graphical representation of
 sensitivity as a function of (1-Specificity).

 Outcomes 5-11 are measured for prediction of difficult intubation for both intervention
 groups.

 Datapoints Baseline covariates

 Individual level:

 1. Sex

 2. Age

 3. Height

 4. Weight

 5. BMI

 6. American Society of Anesthesiologists (ASA) Classification

 7. Use of neuromuscular blocking agents

 8. Hospital unit

 9. Region

 10. Anticipated difficult tracheal intubation

 11. Anticipated difficult mask ventilation

 12. Scheduled airway

 13. Priority; Acute/Elective

 14. Surgical procedure codes

 15. Intubation score

 16. Mask ventilation score

 Intervention covariates

 1. Mouth opening

 2. Thyro-mental distance

 3. Modified Mallampati classification

 4. Jaw protrusion

 5. Neck mobility

 6. Previous difficult airway management

 7. Number of completed risk factors

 8. The calculated SARI score

 9. Dichotomised SARI score (< or ≥ 4)

 10. Snoring

 11. Sleep apnoea

 12. Presence of beard

 13. Changes in the neck due to radiation

 Cluster level summaries

 1. Mean cluster size

 2. Mean number of intubated patients

 3. Fraction of private hospitals

 4. Mean fraction of unanticipated difficult intubation

 5. Age

 6. BMI

 7. ASA classification

 Definition of difficult intubation In the DAD an intubation score is programmed based on
 numbers of intubation attempts and use of equipment.

 1. A maximum of two intubation attempts - Only by direct laryngoscopy

 2. A maximum of two intubation attempts in which other intubation equipment or assistive
 devices for direct laryngoscopy is used (e.g. videolaryngoscope)

 3. Three intubation attempts or more - Regardless of intubation method

 4. Intubation failed despite attempts

 Tracheal intubation by direct laryngoscopy is pre-defined in the DAD as unproblematic by a
 score = 1 and difficult by a score ≥ 2. In our primary analyses and sample size calculation
 we will use the same definition as DAD.

 General analysis principles

 1. Unless otherwise stated, all main analyses will compare the two intervention groups
 using intention-to-treat (ITT) [23].

 2. In order to ensure a correct type 1 error rate, all main analyses will account for the
 clustered design of the trial and the stratification variable [24-26]. Analyses will be
 based on individual patient level data but clustering of patients and the stratification
 variable will be accounted for in a mixed effects model.

 3. In all analyses a maximum level of 5 % (two sided) type 1 error will be regarded as
 statistically significant unless otherwise stated

 4. Main analyses will be according to ITT adjusted for cluster and stratification
 variables. Sensitivity analyses will be performed adjusted and unadjusted for the prior
 listed potential confounding covariates. We will discuss if results differ from the main
 analyses. The conclusion of the trial will be based on the primary analyses.

 5. Test of interaction will be applied for subgroup analyses.

 6. Risks are reported as relative risks. When relative risks are calculated from odds
 ratios with 95% confidence interval (CI) it will be done according to Zhang et Yu[27].

 7. For missing data exceeding a rate of 5 %, and with a statistical significant Little's
 test, indicating that the missing data is not a completely random sample of the total
 data, point estimates with 95 % CI will be calculated using a worst/best case scenario
 imputation on the missing values. If the imputation of a worst/best case scenario
 implies different conclusions, multiple imputations will be performed on the missing
 values assuming missingness at random[28]. Unadjusted and complete case analyses will
 also be presented.

 8. In order to avoid rejecting a true null hypothesis we will address the problem of
 multiplicity by Bonferroni adjustments on the secondary outcome measures. If unadjusted
 analyses are insignificant (P>0,05), Bonferroni adjustments will not be applied. In case
 the adjustment changes an unadjusted significant P-value to a non-significant P-value,
 this will be discussed.

 9. To ensure complete objectivity, the statistician will be blinded for the intervention
 group in the primary outcome analysis. After data collection, a third party data manager
 will generate a complete data set with blinded coding of the intervention groups and
 other variables possibly revealing the intervention. The statistician performs the
 primary outcome analysis on this data set. If the primary outcome differs between groups
 we will construct different conclusions reflecting the results, considering that
 significant differences of the intervention could be both of benefit or harm. After
 writing the conclusions, we will uncover the code of the blinding, and subsequently the
 correct conclusion will be employed[29].

 Statistical analyses Trial profile The flow of study participants will be displayed in a
 Consolidated Standards of Reporting Trials (CONSORT) diagram at a cluster level and at
 individual level. The number of clusters fulfilling the inclusion criterion and the number of
 clusters included in primary and secondary analyses will be presented. The number of patients
 who fulfilled study inclusion criteria as well as the number included in the primary and
 secondary analyses will be reported. Reasons for exclusions of clusters and patients in the
 primary and secondary analyses will be reported.

 Primary outcome Frequencies and percentages per group will be reported with 95 % CI. The
 primary outcome is presented as odds ratios and relative risk ratios.

 The first analysis of the primary outcome will be adjusted for the stratification- and the
 cluster-variable performed according to the ITT principle including patients that met the
 inclusion- and not the exclusion-criteria. A logistic regression including a mixed effect
 model will be used. Intervention group and stratification variable are regarded as fixed
 effects and trial site is regarded as random effects in the model.

 The second analysis of the primary outcome will be adjusted for the stratification- and
 cluster-variables as well as baseline covariates assumed as confounders incorporated in a
 mixed effect model.

 The third analysis of the primary outcome will compare the patients in the control group that
 met the inclusion- and not the exclusion-criteria with patients in the intervention group who
 received the protocoled intervention. That is, a per protocol analysis of control group vs.
 the subgroup in the intervention group that had a sufficiently registered SARI. Interaction
 test will be performed in the intervention group between patients receiving
 sufficient/insufficient SARI registration.

 In an alternative sensitivity analysis of the first analysis of the primary outcome we will
 employ a different cut-off value for difficult intubation using ≥ 3 instead of ≥ 2 as the
 definition of difficult intubation.

 Secondary outcomes Frequencies, proportions, percentages, odds and risk ratios are presented
 with 95 % CI for each group. A chi- squared test is used to assess the effect of the
 intervention on binary outcomes. For categorical outcomes and the adjusted analyses logistic
 regression analysis will be performed.

 Baseline comparisons of patient characteristics Baseline characteristics are presented for
 each intervention group. Frequencies, proportions and percentages will be used to summarize
 discrete variables. In case of missing values, percentages are presented with the actual
 denominator and otherwise calculated according to the number of participating patients.
 Continuous variables are summarized using standard measures of central tendency and
 dispersion using either mean ± SD for data with normal distribution or median and
 interquartile range for non-normally distributed data.

 Baseline comparisons of cluster characteristics Cluster characteristics are presented for
 each group, control and intervention. Unless otherwise stated, data will be presented as
 means with SD for data with normal distribution or median and interquartile range for
 non-normally distributed data.

 Outline of figures and tables The first figure will be a CONSORT flow chart on individual
 patient level and cluster level. The second figure will illustrate the SARI score and
 tutorial instruments. The third figure will demonstrate the registration in the DAD,
 including the intubation score. The fourth figure will present baseline data from each
 intervention group on individual and cluster level and the fifth figure will be outlining the
 main outcome results for each intervention group.

 Discussion In order to avoid outcome reporting bias and data driven results this paper
 presents the detailed statistical analysis plan for the main publication of the DIFFICAIR
 trial. The DIFFICAIR trial raises two important questions i.e. is it possible via the
 intervention to reduce the frequency of difficult intubation and/or difficult mask
 ventilation? This plan only addresses the statistical analysis of the population of intubated
 patients. This is because our sample size calculations were based on the same population; the
 SARI was developed as a prediction tool for difficult intubation; and due to the extent of
 data.

 By adjusting our primary outcome analysis for different design variables, such as clustering
 and stratification we strive to eliminate inflated type 1 error rates as a consequence of the
 trial design. A mixed effect model is applied based on an evaluation of each variable as
 having random or fixed effects[30, 31].

 When multiple comparisons are performed between two groups, you may risk accepting an
 intervention effect erroneously (Type 1 error). There are several approaches that deal with
 multiple testing. We chose to employ a Bonferroni adjustment on the secondary outcome
 measures in order to evaluate, identify and discuss dubious significant outcomes that may be
 due to statistical multiplicity.

 The value of a diagnostic test is usually presented as sensitivity and specificity. We have
 chosen (1 - total accuracy) i.e. the proportion of unanticipated difficult intubations (False
 Negative, FN) and the proportion of unanticipated easy intubations (False Positive, FP). Both
 scenarios are of clinical relevance since the FN are at risk of hypoxia, increased morbidity
 and even death, while the FP are at risk of being imposed unnecessary discomfort by e.g.
 awake intubation. At the same time, both the FN and FP can take up unnecessary resources.
 Sensitivity and specificity are more difficult to interpret intuitively. Consequently, we
 chose to present more transparent primary outcomes. Using proportions of unanticipated
 difficult intubation allowed us to perform a baseline cohort study, on which we based our
 sample size and power calculations.

 By publishing this paper where we pre-specify our methods and analyses, it is our hope that
 the results from the DIFFICAIR trial will be as transparent and robust as possible.

 Conclusion This paper presents the principles of analyses used in the DIFFICAIR trial for the
 first publication of the main outcomes for the population of intubated patients. Our approach
 aims to minimize the risk of data-driven results and outcome reporting bias.

 References

 1. Cooper GM, McClure JH: Anaesthesia chapter from Saving mothers' lives; reviewing
 maternal deaths to make pregnancy safer. Br J Anaesth 2008, 100:17-22.

 2. Hove LD, Steinmetz J, Christoffersen JK, Møller A, Nielsen J, Schmidt H, Moller A:
 Analysis of deaths related to anesthesia in the period 1996-2004 from closed claims
 registered by the Danish Patient Insurance Association. Anesthesiology 2007,
 106:675-680.

 3. Peterson GN, Domino KB, Caplan RA, Posner KL, Lee LA, Cheney FW: Management of the
 difficult airway - A closed claims analysis. Anesthesiology 2005, 103:33-39.

 4. Rosenstock C, Møller J, Hauberg A: Complaints related to respiratory events in
 anaesthesia and intensive care medicine from 1994 to 1998 in Denmark. Acta Anaesthesiol
 Scand 2001, 45:53-58.

 5. McClure JH, Cooper GM, Clutton-Brock TH: Saving mothers' lives: reviewing maternal
 deaths to make motherhood safer: 2006-8: a review. Br J Anaesth 2011, 107:127-32.

 6. Cook T, Woodall N, Frerk C: Major complications of airway management in the United
 Kingdom. Natl Audit Proj 2011.

 7. Lundstrøm LH, Vester-Andersen M, Møller AM, Charuluxananan S, L'hermite J, Wetterslev J:
 Poor prognostic value of the modified Mallampati score: a meta-analysis involving 177
 088 patients. Br J Anaesth 2011, 107:659-67.

 8. Lee A, Fan LTY, Gin T, Karmakar MK, Ngan Kee WD: A systematic review (meta-analysis) of
 the accuracy of the Mallampati tests to predict the difficult airway. Anesth Analg 2006,
 102:1867-1878.

 9. Lundstrøm LH, Møller AM, Rosenstock C, Astrup G, Wetterslev J: High body mass index is a
 weak predictor for difficult and failed tracheal intubation: a cohort study of 91,332
 consecutive patients scheduled for direct laryngoscopy registered in the Danish
 Anesthesia Database. Anesthesiology 2009, 107:266-274.

 10. Lundstrøm LH, Møller AM, Rosenstock C, Astrup G, Gätke MR, Wetterslev J: Avoidance of
 neuromuscular blocking agents may increase the risk of difficult tracheal intubation: a
 cohort study of 103,812 consecutive adult patients recorded in the Danish Anaesthesia
 Database. Br J Anaesth 2009, 103:283-90.

 11. Lundstrøm LH, Møller AM, Rosenstock C, Astrup G, Gätke MR, Wetterslev J: A documented
 previous difficult tracheal intubation as a prognostic test for a subsequent difficult
 tracheal intubation in adults. Anaesthesia 2009, 64:1081-8.

 12. Shiga T, Wajima Z, Inoue T, Sakamoto S, Sakamoto A: Predicting difficult intubation in
 apparently normal patients. Anesthesiology 2005, 103:429-437.

 13. El-Ganzouri AR, McCarthy RJ, Tuman KJ, Tanck EN, Ivankovich AD: Preoperative airway
 assessment: predictive value of a multivariate risk index. Anesth Analg 1996,
 82:1197-1204.

 14. Kheterpal S, Han R, Tremper KK, Shanks AM, Tait AR, O'Reilly M, Ludwig TA, Martin L:
 Incidence and predictors of difficult and impossible mask ventilation. Anesthesiology
 2006, 105:885-891.

 15. Kheterpal S, Martin L, Shanks AM, Tremper KK: Prediction and outcomes of impossible mask
 ventilation: a review of 50,000 anesthetics. Anesthesiology 2009, 110:891-897.

 16. Kheterpal S, Healy D, Aziz M: Incidence, Predictors, and Outcome of Difficult Mask
 Ventilation Combined with Difficult Laryngoscopy: A Report from the Multicenter
 Perioperative Outcomes Group. Anesthesiology 2013:1-10.

 17. Nørskov AK, Rosenstock CV, Wetterslev J, Lundstrøm LH: Incidence of unanticipated
 difficult airway using an objective airway score versus a standard clinical airway
 assessment: the DIFFICAIR trial -- trial protocol for a cluster randomized clinical
 trial. Trials 2013, 14:347.

 18. Chan A-W, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Hro A, Mann H, Dickersin K,
 Berlin JA, Dore CJ, Parulekar WR, Summerskill WSM, Groves T, Schulz KF, Sox HC, Rockhold
 FW, Rennie D, Moher D, Krleža-Jerić K, Hróbjartsson A, Doré CJ: Research and Reporting
 Methods SPIRIT 2013 Statement : Defining Standard Protocol Items for Clinical Trials.
 Ann Intern Med 2013, 158:200-7.

 19. Dwan K, Gamble C, Williamson PR, Altman DG: Reporting of clinical trials: a review of
 research funders' guidelines. Trials 2008, 9:66.

 20. Hayes RJ, Bennett S: Simple sample size calculation for cluster-randomized trials. Int J
 Epidemiol 1999, 28:319-26.

 21. Kahan B, Morris T: Reporting and analysis of trials using stratified randomisation in
 leading medical journals: review and reanalysis. BMJ 2012, 345:e5840.

 22. Crespi CM, Wong WK, Wu S: A new dependence parameter approach to improve the design of
 cluster randomized trials with binary outcomes. Clin Trials 2011, 8:687-98.

 23. Fergusson D, Aaron SD, Guyatt G, Hébert P: Post-randomisation exclusions: the intention
 to treat principle and excluding patients from analysis. BMJ 2002, 325:652-4.

 24. Klar N, Donner A: Current and future challenges in the design and analysis of cluster
 randomization trials. Stat Med 2001, 20:3729-3740.

 25. Hayes RJ, Moulton LH: Cluster Randomised Trials (Chapman & Hall/CRC Interdisciplinary
 Statistics). Chapman and Hall/CRC; 2009:338.

 26. Kahan BC, Morris TP: Assessing potential sources of clustering in individually
 randomised trials. BMC Med Res Methodol 2013, 13:58.

 27. Zhang J, Yu KF: What's the relative risk? A method of correcting the odds ratio in
 cohort studies of common outcomes. JAMA 1998, 280:1690-1.

 28. Schafer JL: Multiple imputation: a primer. Stat Methods Med Res 1999, 8:3-15.

 29. Gøtzsche PC: Blinding during data analysis and writing of manuscripts. Control Clin
 Trials 1996, 17:285-90.

 30. Kahan BC, Morris TP: Adjusting for multiple prognostic factors in the analysis of
 randomised trials. BMC Med Res Methodol 2013, 13:99.

 31. Kahan BC, Morris TP: Analysis of multicentre trials with continuous outcomes: when and
 how should we account for centre effects? Stat Med 2013, 32:1136-49.
Inclusion Criteria:

 Cohort 1:

 Patients with a preoperative airway plan of either

 -  Non / unknown

 -  Spontaneous respiration

 -  Mask Ventilation

 -  Laryngeal Mask etc.. (any kind)

 -  Intubation by direct laryngoscopy

 and primarily (attempted) intubated by direct laryngoscopy

 Cohort 2:

 All patients in cohort 1, and patients with an airway plan of either

 -  Intubation with video laryngoscope

 -  Intubation with flexible fiber optic scope

 -  Intubation by another method (Fastrach, Trachlight, etc.).

 which was expected difficult to intubate by direct laryngoscopy

 Cohort 3:

 All patients who are mask ventilated.

 Exclusion Criteria:

 -  Children under 15 years

 -  Patients who can not co-operate for the examination (unconscious, demented, severely
 traumatized, etc..)