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

Trials

Jørn Wetterslev¹  Charlotte Vallentin Rosenstock²  Lars Hyldborg Lundstrøm²  Anders Kehlet Nørskov¹ 

[1] Copenhagen Trial Unit, Center for Clinical Intervention Research, Copenhagen University Hospital, Rigshospitalet, Copenhagen 2100, Denmark;Department of Anaesthesiology, Nordsjællands Hospital, Copenhagen University Hospital, Hillerød, Capital region of Denmark 3400, Denmark

关键词: Difficult intubation;    Cluster analysis;    Airway management;    Cluster randomized trial;    Statistical analysis plan;   

Others  :  806154
DOI  :  10.1186/1745-6215-15-173

 received in 2014-01-03, accepted in 2014-05-08,  发布年份 2014

【 摘 要 】

Background

Preoperative airway assessment in Denmark is based on a non-specific clinical assessment left to the discretion of the responsible anesthesiologist. The DIFFICAIR trial compares the effect of using a systematic and consistent airway assessment versus a non-specific clinical assessment on the frequency of unanticipated difficult airway management.

To prevent outcome bias and selective reporting, we hereby present a detailed statistical analysis plan as an amendment (update) to the previously published protocol for the DIFFICAIR trial.

Method/Design

The DIFFICAIR trial is a stratified, parallel group, cluster (cluster = department) randomized multicenter trial involving 28 departments of anesthesia in Denmark randomized to airway assessment either by the Simplified Airway Risk Index (SARI) or by a usual non-specific assessment. Data from patients’ preoperative airway assessment are registered in the Danish Anesthesia Database. An objective score for intubation grading the severity, that is the severity of the intubations, as well as the frequency of unanticipated difficult intubation, is measured for each group.

Primary outcome measures are the fraction of unanticipated difficult and easy intubations.

The database is programmed so that the registration of the SARI is mandatory for the intervention group but invisible to controls.

Data recruitment was commenced in October 2012 and ended in ultimo December 2013.

Conclusion

We intend to increase the transparency of the data analyses regarding the DIFFICAIR trial by an a priori publication of a statistical analysis plan.

Trial registration

ClinicalTrials.gov: NCT01718561.

【 授权许可】

   
2014 Nørskov et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [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 19962004 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, 107:127-132.
• [6]Cook T, Woodall N, Frerk C: Major complications of airway management in the UK:results of the Fourth National Audit Project of the royal college of anaesthesiologists and the difficult airway society. Part 1: Anaesthesia. Br J Anaesth 2011, 106:617-631.
• [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, 107:659-667.
• [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, 103:283-290.
• [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, 64:1081-1088.
• [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, 6:1-10.
• [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. BioMed Central Full Text
• [18]Chan A, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Krleža-Jerić K, Hróbjartsson A, Mann H, Dickersin K, Berlin JA, Doré CJ, Parulekar WR, Summerskill WSM, Groves T, Schulz KF, Sox HC, Rockhold FW, Rennie D, Moher D: SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med 2013, 158:200-207.
• [19]Dwan K, Gamble C, Williamson PR, Altman DG: Reporting of clinical trials: a review of research funders’ guidelines. Trials 2008, 9:66. BioMed Central Full Text
• [20]Campbell MK, Piaggio G, Elbourne DR, Altman DG: Consort 2010 statement: extension to cluster randomised trials. BMJ 2012, 345:e5661.
• [21]Hayes RJ, Bennett S: Simple sample size calculation for cluster-randomized trials. Int J Epidemiol 1999, 28:319-326.
• [22]Kahan B, Morris T: Reporting and analysis of trials using stratified randomisation in leading medical journals: review and reanalysis. BMJ 2012, 345:e5840.
• [23]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, 8:687-698.
• [24]Fergusson D, Aaron SD, Guyatt G, Hébert P: Post-randomisation exclusions: the intention to treat principle and excluding patients from analysis. BMJ 2002, 325:652-654.
• [25]Klar N, Donner A: Current and future challenges in the design and analysis of cluster randomization trials. Stat Med 2001, 20:3729-3740.
• [26]Hayes RJ, Moulton LH: Cluster Randomised Trials. Pharmaceutical Statistics, vol 11, Issue 1.
• [27]Kahan BC, Morris TP: Assessing potential sources of clustering in individually randomised trials. BMC Med Res Methodol 2013, 13:58. BioMed Central Full Text
• [28]Zhang J, Yu KF: What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 1998, 280:1690-1691.
• [29]Schafer JL: Multiple imputation: a primer. Stat Methods Med Res 1999, 8:3-15.
• [30]Gøtzsche PC: Blinding during data analysis and writing of manuscripts. Control Clin Trials 1996, 17:285-290.
• [31]Peters T: Comparison of methods for analysing cluster randomized trials: an example involving a factorial design. Int J Epidemiol 2003, 32:840-846.
• [32]Ma J, Raina P, Beyene J, Thabane L: Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study. BMC Med Res Methodol 2013, 13:9. BioMed Central Full Text