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Predicting difficult intubation: a multivariable analysis

Keyvan Karkouti , MD FRCPC MSc^*, D. Keith Rose, MD FRCPC, D. Wigglesworth, MSc and Marsha M. Cohen, MD FRCPC PhD

^* From the Departments of Anaesthesia, Toronto General Hospital, University Health Network, and
St. Michael's Hospital, and
the Center for Research in Women's Health, Department of Health Administration, and Department of Anaesthesia, Sunnybrook Health Science Centre, University of Toronto, Canada. Work undertaken at St. Michael's Hospital.

Address correspondence to: Dr. K. Karkouti, Toronto General Hospital, University Health Network, Bell Wing 4-645, 200 Elizabeth Street, Toronto, Ontario, M5G 2C4 Canada Email: keyvan.karkouti{at}uhn.on.ca

Purpose: To develop a clinically useful and valid model for predicting difficult laryngoscopic tracheal intubation in patients with seemingly normal airways by adhering to the principles of multivariable model development.

Methods: This was an observational study performed at a tertiary-care teaching hospital. Preoperatively, 444 randomly selected patients requiring tracheal intubation for elective surgery were assessed. In addition, 27 patients in whom tracheal intubation was difficult, but were not assessed preoperatively, were assessed postoperatively. One assessor, blinded to the intubation information, collected the predictor variables. A reliable definition for difficult intubation was used and all attempts were made to eliminate sources of bias. Multivariable modeling was performed using logistic regression and the model was validated using the bootstrapping technique.

Results: Of the 461 patients included in the analysis, 38 were classified as difficult to intubate. Multivariable analysis identified three airway tests that were highly significant for predicting difficult tracheal intubation. These were: 1) "mouth opening", 2) "chin protrusion", and 3) "atlanto-occipital extension". Using these tests, a validated, highly reliable and predictive model is produced to determine the probability of difficult intubation for patients. At a selected probability cut-off value, the model is 86.8% sensitive and 96.0% specific.

Conclusion: A simple and accurate multivariable model, consisting of three airway tests, is produced for predicting difficult laryngoscopic tracheal intubation. Additional studies will be required to determine the accuracy and feasibility of this model when applied to a large sample of new patients by multiple anesthesiologists.

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