New Suggested Enhancing Safe Apneic Time in Edentulous Elderly: A Comparison of Transnasal Humidified Rapid-Insufflation Ventilatory Exchange and Facemask.

BACKGROUND Edentulous elderly patients often face challenges in airway management and are susceptible to hypoxemia. Transnasal humidified rapid-insufflation ventilatory exchange (THRIVE) provides high-flow nasal oxygenation, potentially extending safe apneic time (SAT). This study compared the efficacy of THRIVE versus facemask ventilation in improving oxygenation and extending SAT in edentulous elderly patients. MATERIAL AND METHODS Patients with more than 10 missing teeth and who were over 65 years old were randomly assigned to the facemask group (Group M, n=25) or the THRIVE group (Group T, n=25). Patients in Group M were pre-oxygenated with a facemask (6 L/min, FiO2 100%), while patients in Group T were pre-oxygenated with their mouths closed via THRIVE (30 L/min, FiO2 100%). After anesthesia induction, patients in Group M were ventilated with pressure-controlled ventilation. In Group T, the patient's mouth was kept closed, and the flow rate was adjusted to 70 L/min. Four min after cisatracurium administration, ventilation was stopped in Group M while Group T continued to receive oxygen (70 L/min, FiO2 100%).The primary outcome was SAT, which was attained at 4 min after injection of cisatracurium and ended when SpO2 decreased to 95% or when apneic time reached 480 s. A secondary outcome was the reoxygenation time, defined as the time from the beginning of mechanical ventilation to the time when SpO2 98% was reached. RESULTS An SAT of 480 s was reached by all patients in Group T, but by only 6 patients in Group M (P<0.05). Compared with Group M, the reoxygenation time in Group T was significantly shorter (P<0.05). CONCLUSIONS As compared to facemask, THRIVE can extend the SAT, improve oxygenation, and reduce reoxygenation time.

Comparing the Effectiveness and Safety of MedAn with the Nishikawa Blade and UE Videolaryngoscopes for Left-Sided Double-Lumen Endobronchial Tube Intubation: A Randomized Controlled Trial.

BACKGROUND The purpose of this study was to compare the effectiveness and safety of the MedAn videolaryngoscope with the Nishikawa blade (MedAn) vs the UE videolaryngoscope (UE) for intubation with a left-sided double-lumen endobronchial tube (LDLT) in patients with normal airways. MATERIAL AND METHODS We randomly categorized 106 patients scheduled to undergo elective thoracic surgery with LDLT for one-lung ventilation into 2 groups: the UE group (Group UE) and the MedAn group (Group MedAn), using the MedAn or UE for LDLT intubation. The primary outcome was time to successful intubation. The Cormack-Lehane classification of laryngeal view was the key secondary outcome. Other secondary outcomes included first-attempt and overall intubation success rates, laryngoscopy time, LDLT placement time, operators' subjective evaluation of videolaryngoscopes, hemodynamic changes during videolaryngoscopic intubation, and adverse outcomes. RESULTS The time to successful intubation and LDLT placement time of Group MedAn were 42.0 (32.35, 47.0) s and 23.0 (18.0, 26.0) s, and it was shorter than in Group UE (median, 42 s vs 49 s, 23 s vs 30 s, P<0.001). Group MedAn had a better laryngeal view (P=0.03) and less subglottic/tracheal mucosal injury (P<0.001) than Group UE. Moreover, the operators' subjective grading of ease of laryngoscopy, quality of view, and ease of LDLT placement were higher in Group MedAn than in Group UE (P<0.05). CONCLUSIONS Compared with the UE, the MedAn could reduce the intubation time and provide a better laryngeal view and sufficient intubation space for safer LDLT intubation in patients with normal airways.

A risk prediction model based on machine learning for postoperative cognitive dysfunction in elderly patients with non-cardiac surgery.

BACKGROUND: Early identification of elderly patients undergoing non-cardiac surgery who may be at high risk for postoperative cognitive dysfunction (POCD) can increase the chances of prevention for them, as extra attention and limited resources can be allocated more to these patients. AIM: We performed this analysis with the aim of developing a simple, clinically useful machine learning (ML) model to predict the probability of POCD at 3 months in elderly patients after non-cardiac surgery. METHODS: We collected information on patients who received surgical treatment at Nanjing First Hospital from May 2020 to May 2021. We used LASSO regression to select key features and built 5 ML models to assess the risk of POCD at 3 months in elderly patients after non-cardiac surgery. The Shapley Additive exPlanations (SHAP) and methods were introduced to interpret the best model. RESULTS: A total of 415 patients with non-cardiac surgery were included. The support vector machine (SVM) was the best-performing model of the five ML models. The model showed excellent performance compared to the other four models. The SHAP results showed that VAS score, age, intraoperative hypotension, and preoperative hemoglobin were the four most important features, indicating that the SVM model had good interpretability and reliability. The website of the web-based calculator was https://modricreagan-non-3-pocd-9w2q78.streamlit.app/ . CONCLUSION: Based on six important perioperative variables, we successfully established a series of ML models for predicting POCD occurrence at 3 months after surgery in elderly non-cardiac patients, with SVM model being the best-performing model. Our models are expected to serve as decision aids for clinicians to monitor screened high-risk patients more closely or to consider further interventions.

The effect of varying inhaled oxygen concentrations of high-flow nasal cannula oxygen therapy during gastroscopy with propofol sedation in elderly patients: a randomized controlled study.

BACKGROUND: Despite evidence that high-flow nasal cannula oxygen therapy (HFNC) promotes oxygenation, its application in sedated gastroscopy in elderly patients has received little attention. This study investigated the effect of different inhaled oxygen concentrations (FiO2) of HFNC during sedated gastroscopy in elderly patients. METHODS: In a prospective randomized single-blinded study, 369 outpatients undergoing regular gastroscopy with propofol sedation delivered by an anesthesiologist were randomly divided into three groups (n = 123): nasal cannula oxygen group (Group C), 100% FiO2 of HFNC group (Group H100), and 50% FiO2 of HFNC (Group H50). The primary endpoint in this study was the incidence of hypoxia events with pulse oxygen saturation (SpO2) ≤ 92%. The secondary endpoints included the incidence of other varying degrees of hypoxia and adverse events associated with ventilation and hypoxia. RESULTS: The incidence of hypoxia, paradoxical response, choking, jaw lift, and mask ventilation was lower in both Group H100 and Group H50 than in Group C (P < 0.05). Compared with Group H100, Group H50 showed no significant differences in the incidence of hypoxia, jaw lift and mask ventilation, paradoxical response, or choking (P > 0.05). No patients were mechanically ventilated with endotracheal intubation or found to have complications from HFNC. CONCLUSION: HFNC prevented hypoxia during gastroscopy with propofol in elderly patients, and there was no significant difference in the incidence of hypoxia when FiO2 was 50% or 100%. TRIAL REGISTRATION: This single-blind, prospective, randomized controlled trial was approved by the Ethics Committee of Nanjing First Hospital (KY20201102-04) and registered in the China Clinical Trial Center (20/10/2021, ChiCTR2100052144) before patients enrollment. All patients signed an informed consent form.

Online interpretable dynamic prediction models for postoperative delirium after cardiac surgery under cardiopulmonary bypass developed based on machine learning algorithms: A retrospective cohort study.

OBJECTIVE: Postoperative delirium (POD) is strongly associated with poor early and long-term prognosis in cardiac surgery patients with cardiopulmonary bypass (CPB). This study aimed to develop dynamic prediction models for POD after cardiac surgery under CPB using machine learning (ML) algorithms. METHODS: From July 2021 to June 2022, clinical data were collected from patients undergoing cardiac surgery under CPB at Nanjing First Hospital. A dataset from the same center (October 2022 to November 2022) was also used for temporal external validation. We used ML and deep learning to build models in the training set, optimized parameters in the test set, and finally validated the best model in the validation set. The SHapley Additive exPlanations (SHAP) method was introduced to explain the best models. RESULTS: Of the 885 patients enrolled, 221 (25.0%) developed POD. 22 (22.0%) of 100 validation cohort patients developed POD. The preoperative and postoperative artificial neural network (ANN) models exhibited optimal performance. The validation results demonstrated satisfactory predictive performance of the ANN model, with area under the receiver operator characteristic curve (AUROC) values of 0.776 and 0.684 for the preoperative and postoperative models, respectively. Based on the ANN algorithm, we constructed dynamic, highly accurate, and interpretable web risk calculators for POD. CONCLUSIONS: We successfully developed online interpretable dynamic ANN models as clinical decision aids to identify patients at high risk of POD before and after cardiac surgery to facilitate early intervention or care.

Machine learning-based risk prediction of hypoxemia for outpatients undergoing sedation colonoscopy: a practical clinical tool.

OBJECTIVES: Hypoxemia as a common complication in colonoscopy under sedation and may result in serious consequences. Unfortunately, a hypoxemia prediction model for outpatient colonoscopy has not been developed. Consequently, the objective of our study was to develop a practical and accurate model to predict the risk of hypoxemia in outpatient colonoscopy under sedation. METHODS: In this study, we included patients who received colonoscopy with anesthesia in Nanjing First Hospital from July to September 2021. Risk factors were selected through the least absolute shrinkage and selection operator (LASSO). Prediction models based on logistic regression (LR), random forest classifier (RFC), extreme gradient boosting (XGBoost), support vector machine (SVM), and stacking classifier (SCLF) model were implemented and assessed by standard metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity and specificity. Then choose the best model to develop an online tool for clinical use. RESULTS: We ultimately included 839 patients. After LASSO, body mass index (BMI) (coefficient = 0.36), obstructive sleep apnea-hypopnea syndrome (OSAHS) (coefficient = 1.32), basal oxygen saturation (coefficient = -0.14), and remifentanil dosage (coefficient = 0.04) were independent risk factors for hypoxemia. The XGBoost model with an AUROC of 0.913 showed the best performance among the five models. CONCLUSION: Our study selected the XGBoost as the first model especially for colonoscopy, with over 95% accuracy and excellent specificity. The XGBoost includes four variables that can be quickly obtained. Moreover, an online prediction practical tool has been provided, which helps screen high-risk outpatients with hypoxemia swiftly and conveniently.

Colonoscopy under sedation is an effective technique for the inspection and treatment of alimentary canal diseases, but hypoxemia associated with this process cannot be ignored, since prolonged or severe hypoxemia may result in several serious consequences.We wanted to develop a practical and accurate model to predict the risk of hypoxemia for outpatient colonoscopy under sedation, which could help clinicians make more accurate and objective judgments to prevent patients from being harmed.A total of 839 patients were included in our study and we constructed five machine learning models and selected the best one, which demonstrated satisfactory performance. On this basis, a user-friendly data interface has been developed for convenient application. Clinicians can log in to this interface at any time and it will automatically calculate the patient's risk of hypoxemia when entering patient information.This study offers evidence that machine learning algorithms can accurately predict the risk of hypoxemia for outpatient colonoscopy under sedation and the model we developed is a practical and interpretable tool that could be used as a clinical decision-making aid.

An interpretable artificial neural network model for predicting hypoxemia via an online tool in adult (18-64) patients during esophagogastroduodenoscopy.

Background: The hypoxemia risk in adult (18-64) patients treated with esophagogastroduodenoscopy (EGD) under sedation often poses a dilemma for anesthesiologists. We aimed to establish an artificial neural network (ANN) model to solve this problem, and introduce the Shapley additive explanations (SHAP) algorithm to further improve the interpretability. Methods: The relevant data of patients underwent routine anesthesia-assisted EGD were collected. Elastic network was used to filter the optimal features. Airway-ANN and Basic-ANN models were established based on all collected indicators and remaining variables excluding airway assessment indicators, respectively. The performance of Basic-ANN, Airway-ANN and STOP-BANG was evaluated by the area under the precision-recall curve (AUPRC) on temporal validation set. The SHAP was used for revealing the predictive behavior of our best model. Results: 999 patients were eventually included. The AUPRC value of Airway-ANN model was significantly higher than Basic-ANN model in the temporal validation set (0.532 vs 0.429, P < 0.05). And the performance of both two ANN models was significantly better than that of STOP-BANG score (both P < 0.05). The Airway-ANN model was deployed to the cloud (http://njfh-yxb.com.cn:2022/airway_ann). Conclusion: Our online interpretable Airway-ANN model achieved satisfying ability in identifying the hypoxemia risk in adult (18-64) patients undergoing EGD.

Dynamic prediction of hypoxemia risk at different time points based on preoperative and intraoperative features: machine learning applications in outpatients undergoing esophagogastroduodenoscopy.

BACKGROUND: Hypoxemia often occurs in outpatients undergoing anesthesia-assisted esophagogastroduodenoscopy (EGD). However, there is a scarcity in tools to predict the hypoxemia risk. We aimed to solve this problem by developing and validating machine learning (ML) models based on preoperative and intraoperative features. METHODS: All data were retrospectively collected from June 2021 to February 2022. The most appropriate predictive features were selected by the least absolute shrinkage and selection operator, which were incorporated and modelled by 4 ML algorithms. The area under the precision-recall curve (AUPRC) was used as the main evaluation metric to select the best models, and the selected models were compared with the STOP-BANG score. Their predictive performance was visually interpreted by SHapley Additive exPlanations. The primary endpoint of this study was hypoxemia during the procedure, defined as at least one reading of pulse oximetry < 90% without probes misplacement from the anesthesia induction beginning to the end of EGD, while the secondary endpoint was hypoxemia during induction, from the induction beginning to the start of endoscopic intubation. RESULTS: Of 1160 patients in the derivation cohort, 112 patients (9.6%) developed intraoperative hypoxemia, of which 102 (8.8%) occurred during the induction period. In temporal and external validation, no matter whether based on preoperative variables or still based on preoperative plus intraoperative variables, our models showed excellent predictive performance for the two endpoints, significantly better than STOP-BANG score. In the model interpretation section, preoperative variables (airway assessment indicators, pulse oximeter oxygen saturation and BMI) and intraoperative variables (the induced propofol dose) made the highest contribution to the predictions.To our knowledge, our ML models were the first to predict hypoxemia risk, which achieved excellent overall predictive ability integrating various clinical indicators. These models have the potential to become an effective tool for adjusting sedation strategies flexibly and reducing the workload of anesthesiologists.KEY MESSAGESThis study is the first model employing ML methods based on preoperative and preoperative plus intraoperative variables for predicting the risk of hypoxemia during induction and the whole EGD procedure respectively.Our four models achieved satisfactory predictive performance and outperformed STOP-BANG score in terms of AUPRC in the temporal and external validation cohorts respectively.We found that the relevant variables of airway assessment should be fully taken into account when analyzing the risk factor of hypoxemia, and the effect of patients' age on their hypoxemia risk should be considered in conjunction with the propofol dose.