Expert consensus on difficult airway assessment

Background:Identifying a potentially difficult airway is crucial both in anaesthesia in the operating room(OR)and non-operation room sites.There are no guidelines or expert consensus focused on the assessment of the difficult airway before,so this expert consensus is developed to provide guidance for airway assessment,making this process more standardized and accurate to reduce airway-related complications and improve safety.Methods:Seven members from the Airway Management Group of the Chinese Society of Anaesthesiology(CSA)met to discuss the first draft and then this was sent to 15 international experts for review,comment,and approval.The Grading of Recommendations,Assessment,Development and Evaluation(GRADE)is used to determine the level of evidence and grade the strength of recommendations.The recommendations were revised through a three-round Delphi survey from experts.Results:This expert consensus provides a comprehensive approach to airway assessment based on the medical history,physical examination,comprehensive scores,imaging,and new developments including transnasal endoscopy,virtual laryngoscopy,and 3D printing.In addition,this consensus also reviews some new technologies currently under development such as prediction from facial images and voice information with the aim of proposing new research directions for the assessment of difficult airway.Conclusions:This consensus applies to anesthesiologists,critical care,and emergency physicians refining the preoperative airway assessment and preparing an appropriate intubation strategy for patients with a potentially difficult airway.

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival probability.Machine learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care.Methods:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)registry.We applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU admission.The model was iteratively cross-validated in different subsets of the study cohort.Results:The final study population consisted of 675 patients.The XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)patients.The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources.