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.
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.
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