Christopher W Connor1, Scott Segal. 1. Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA. chris.connor@bmc.org
Abstract
BACKGROUND: Bedside airway evaluation is conduced before anesthesia, but all current methods perform modestly, with low sensitivity and positive predictive value. We hypothesized that subjective features of patients' anatomies improve anesthesiologists' ability to predict difficult intubation, and derived a computer model to do so, based on analysis of photographs of patients' faces. METHODS: Eighty male patients were divided into 2 equal cohorts for model derivation and validation. Each cohort consisted of 20 easy and 20 challenging intubations, defined as >1 attempt by an operator with at least 12 months of anesthesia experience, grade 3 or 4 laryngoscopic view, need for a second operator, or nonelective use of an alternative airway device. Photographs of each subject's face were analyzed by software that resolves each face into 61 facial proportions derived from an algorithm that models the face as a single point in a 50-dimensional eigenspace. Each parameter was tested for discriminatory ability by logistic regression, and combinations of 11 variables with P ≤ 0.1, plus Mallampati class and thyromental distance, were tested exhaustively by all possible binomial quadratic logistic regression models. Candidate models were cross-validated by maximizing the product of the area under the receiver operating characteristic curves obtained in the derivation and validation cohorts. RESULTS: The best model included 3 facial parameters and thyromental distance. It correctly classified 70 of 80 subjects (P < 10(-8)). In contrast, the best combination of Mallampati class and thyromental distance correctly classified 47 of 80 (P = 0.073). Sensitivity, specificity, and area under the curve for the computer model were 90%, 85%, and 0.899, respectively. CONCLUSIONS: Computerized analysis of facial structure and thyromental distance can classify easy versus difficult intubation with accuracy significantly outperforming popular clinical predictive tests.
BACKGROUND: Bedside airway evaluation is conduced before anesthesia, but all current methods perform modestly, with low sensitivity and positive predictive value. We hypothesized that subjective features of patients' anatomies improve anesthesiologists' ability to predict difficult intubation, and derived a computer model to do so, based on analysis of photographs of patients' faces. METHODS: Eighty male patients were divided into 2 equal cohorts for model derivation and validation. Each cohort consisted of 20 easy and 20 challenging intubations, defined as >1 attempt by an operator with at least 12 months of anesthesia experience, grade 3 or 4 laryngoscopic view, need for a second operator, or nonelective use of an alternative airway device. Photographs of each subject's face were analyzed by software that resolves each face into 61 facial proportions derived from an algorithm that models the face as a single point in a 50-dimensional eigenspace. Each parameter was tested for discriminatory ability by logistic regression, and combinations of 11 variables with P ≤ 0.1, plus Mallampati class and thyromental distance, were tested exhaustively by all possible binomial quadratic logistic regression models. Candidate models were cross-validated by maximizing the product of the area under the receiver operating characteristic curves obtained in the derivation and validation cohorts. RESULTS: The best model included 3 facial parameters and thyromental distance. It correctly classified 70 of 80 subjects (P < 10(-8)). In contrast, the best combination of Mallampati class and thyromental distance correctly classified 47 of 80 (P = 0.073). Sensitivity, specificity, and area under the curve for the computer model were 90%, 85%, and 0.899, respectively. CONCLUSIONS: Computerized analysis of facial structure and thyromental distance can classify easy versus difficult intubation with accuracy significantly outperforming popular clinical predictive tests.
Authors: H Ilper; A Grossbach; C Franz-Jäger; C Byhahn; M Klages; H H Ackermann; K Zacharowski; T Kunz Journal: Anaesthesist Date: 2018-02-01 Impact factor: 1.041
Authors: Joao B Rezende-Neto; Argenil J Oliveira; Mario P Neto; Fernando A Botoni; Sandro B Rizoli Journal: World J Emerg Surg Date: 2011-11-02 Impact factor: 5.469