Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach
Moustafa, M., El-Metainy, S., Mahar, K., Mahmoud Abdel-magied, E. (2017). Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach. EKB Journal Management System, 33(2), 153-158. doi: 10.1016/j.egja.2017.02.002
Moustafa Abdelaziz Moustafa; Shahira El-Metainy; Khaled Mahar; Essam Mahmoud Abdel-magied. "Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach". EKB Journal Management System, 33, 2, 2017, 153-158. doi: 10.1016/j.egja.2017.02.002
Moustafa, M., El-Metainy, S., Mahar, K., Mahmoud Abdel-magied, E. (2017). 'Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach', EKB Journal Management System, 33(2), pp. 153-158. doi: 10.1016/j.egja.2017.02.002
Moustafa, M., El-Metainy, S., Mahar, K., Mahmoud Abdel-magied, E. Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach. EKB Journal Management System, 2017; 33(2): 153-158. doi: 10.1016/j.egja.2017.02.002

Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach

Egyptian Journal of Anaesthesia
Volume 33, Issue 2, April 2017, Pages 153-158    PDF (1.55 M)
DOI: 10.1016/j.egja.2017.02.002
Authors
Moustafa Abdelaziz Moustafa; Shahira El-Metainy; Khaled Mahar; Essam Mahmoud Abdel-magied
Abstract
Background
Preoperative identification of patients whose trachea will be difficult to intubate would decrease the rate of anesthesia related adverse respiratory events. Each test for airway examination may predict a separate aspect of airway. A computer-based approach is tested in this study to precisely evaluate difficult laryngoscopy.
Aim of the work
Aim of the work was to evaluate the efficacy and accuracy of a multiparameter computer-based system for prediction of difficult laryngoscopy.
Study design
50 Adult patients presenting for non emergency surgery at Alexandria main university hospital from February 2015 to Feruary 2016 with unanticipated difficult laryngoscopy were assessed postoperatively according to selected nine airway parameters. The same was done for their matched 50 controls after full recovery from general anesthesia. All data were entered into an information–based computer system where they were converted into numerical variables.
Results
Processed data have been designed as a software termed . Positive predictive value was 76%, Negative predictive value was 76%, Matthews correlation coefficient was 0.52 and area under the ROC curve was 0.79.
Conclusion
is a machine learning program for prediction of difficult laryngoscopy. New cases can be entered to the training set thus improving the accuracy of the software.
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