The Association of Laryngeal Position on Videolaryngoscopy and Time Taken to Intubate Using Spatial Point Pattern Analysis of Prospectively Collected Quality Assurance Data

Abstract

BACKGROUND: During videolaryngoscopy (VL), the larynx appears within the defined area of the video screen, and its location can be measured as a point within this space. Spatial statistics offer methods to explore the relationship between location data and associated variables of interest. The aims of this study were to use spatial point pattern analysis to explore if the position of the larynx on VL is associated with longer times to intubate, increased risk of a needing >1 intubation attempt, or percentage of glottic opening. METHODS: Quality assurance data and clinical notes from all prehospital intubations using C-MAC Pocket Monitor with CMAC-4 blade (Karl Storz) from January 1, 2018, to July 31, 2020, were reviewed. We extracted 6 measurements corresponding to the time taken to obtain the initial and then best laryngeal view, time to manipulate a bougie, and time to place the endotracheal tube, as well a percentage of glottic opening and a number of intubation attempts. Larynx location was the middle of the base of glottis, in cm from the left and bottom on the C-MAC screen. Two plots were produced to summarize the base of glottis location and time to perform each time component of intubation. Next, a cross mark function and a maximum absolute deviation hypothesis test were performed to assess the null hypotheses that the spatial distributions were random. The association between glottis location and >1 intubation attempt was assessed by a spatial relative risk plot. RESULTS: Of 619 eligible intubations, 385 had a video for analysis. The following time variables had a nonrandom spatial distribution with a tendency for longer times when the larynx was offcenter to the top or right of the screen: laryngoscope passing from teeth to glottis, glottis first view to best view of the larynx, time from bougie appearing to being placed in the cords, and overall time from teeth to endotracheal tube passing through cords. There was no increased relative risk for >1 intubation attempt. CONCLUSIONS: Spatial point pattern analysis identified a relationship between the position of the larynx during VL and prolonged intubation times. We did not find a relationship between larynx location and >1 attempt. Whether the location of the larynx on the screen is a marker for difficult VL or if optimizing the larynx position to the center of the screen improves intubation times would require further prospective studies. (Anesth Analg 2022;134:1288-96) KEY POINTS• Question: Is the position of the larynx on videolaryngoscopy associated with longer times to intubate or an increased risk of a second attempt at intubation, using spatial point pattern analysis? • Findings: Laryngeal location to the top and right of the screen is associated with longer times for intubation and bougie manipulation but not requirement for >1 attempt. • Meaning: Larynx location may be associated with intubation performance measures. GLOSSARY CCP = critical care paramedic; C-MAC = Karl Storz C-MAC videolaryngoscopy range; CMAC-4 = Karl Storz C-MAC macintosh siz...

Development of an Artificial Intelligence–Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation

“…Stage-specific risk factors for intubation have been identified through partially segmented analyses of image readings. 15 However, these analyses have focused on specific stages and have not involved the entire process. Our feasibility study of the data obtained by the PGY resident physician during her initial practice suggested that the AI model precisely evaluated the entire and segmental phases of TI.…”

“…However, many factors result in errors, such as sampling delay and the assistant's movement speed; therefore, performing image analysis can be difficult. Here, we selected the cut points by referencing previous reports on landmarks in a TI video recording 15,25 and the practice experience of experts. Through a feasibility study, the AI program was trained to identify each cut point.…”

“…Each phase requires a different operating technique. 15 Artificial intelligence (AI)-based approaches represent an advance in airway management. [15][16][17] However, most of these approaches have been developed for the analysis of targets, such as the vocal cord.…”

“…15 Artificial intelligence (AI)-based approaches represent an advance in airway management. [15][16][17] However, most of these approaches have been developed for the analysis of targets, such as the vocal cord. In this study, we trained the AI object detection system YOLO (You Only Look Once) [18][19][20] to recognize key targets within the airway and to identify different cutoff points through comparison with expert-selected cut points (ground truth).…”