Development of quantitative tools to predict patients with difficult intubation to minimize treatment related complications

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ABSTRACT Endotracheal mouth/nose breathing need 10% this to performing subjective poorly standard Unfortunately, these airway examination systems in clinical practice perform only modestly, with sensitivities of 20-62%, specificities of 82-97%, and very low positive predictive values, generally less than 30%, unless very liberal definitions of difficulty are used. There are likely a number of reasons for this poor performance, including the relative rarity of difficult intubation, the multifactorial etiology and varying definition of difficult intubation, inter-observer variability in test results, failure to validate potential systems in patients independent of those used to derive the test, and the inadequacy of the tests themselves. intubation (EI) is a common medical procedure in which a plastic tube is introduced via the into the trachea, to provide respiratory support during general anesthesia or to ameliorate difficulty in cases of respiratory failure, cardiac arrest, or other forms of critical illness. The global for EI is likely at least 150 million based on the WHO estimate of surgical need worldwide. Approximately of EI attempts are difficult, and approximately 1/2000 are deemed impossible. The clinica l significance of “can't intubate, can't ventilate” scenario is extremely important: 25% of anesthetic related deaths are due airway mishaps. Patients are typically assessed for anatomic features that might predict difficulty in EI prior to the procedure. In practice, anesthesiologists and other airway experts likely weigh other factors in anticipating a difficult airway, including habitus, facial appearance, and perhaps other understood hunches. The use of this examination to predict difficult intubation is considered the of care in modern anesthesiology practice. When personnel Conversely, not learning and intubation. identify accuracy (Mallampati anesthesiologists reduce mobilization difficulty the airway is anticipated, more advanced techniques may be employed, additional may be recruited for assistance, surgical airway expertise (i.e., tracheostomy) may be on standby these techniques are expensive, time consuming, and uncomfortable to patients, so they should be overused. We hypothesize that anesthesiologists' visual assessment can be modeled through deep to identify patients with difficult intubation with high accuracy. Through innovative use of deep learning sophisticated image analysis, this research will identify facial features tha accurately predict difficult The research will utilize frontal as well as profile facial photographs to build a generative model to difficult intubation patients. The developed model will be subjected to rigorous statistical analysis for and reproducibility . In a clinical trial, the proposed model will be compared against the bedside tests + thryomental distance). The project will 1) result in innovative software tools to facilitate and 2) substantially reduce unnecessary healthcare expenses. We expect that this model will the probability of an unexpected difficult intubation and allow anesthesiologists to better prepare by of alternative techniques, equipment, or operators. with . t