Risk Factors for Anesthesia-Related Airway Patient Safety Incidents: A Single-Center Retrospective Case-Control Analysis from 2009 to 2022.

Objective Airway-related patient safety incident (PSI) has always been the top concern of anesthesiologists because this type of incidents could severely threaten patient safety if not treated immediately and properly. This study intends to reveal the composition, prognosis, and to identify risk factors for airway related incidents reported by anesthesiologists. Methods All airway related PSIs reported by anesthesiologists in a Chinese academic hospital between September 2009 and May 2022 were collected from the PSI reporting system. Patients with airway incidents reported were matched 1:1 with controls based on sex and type of surgery. Univariable and multivariable analysis were performed to find risk factors associated with airway incident occurrence, and to evaluate influence of airway PSIs on patient prognosis. Results Among 1,038 PSIs voluntarily reported by anesthesiologists during the study period, 281 cases (27.1%) were airway-related incidents, with an overall reporting incidence of 4.74 per 10,000 among 592,884 anesthesia care episodes. Only ASA physical status was found to be significant independent predictor of these airway PSIs (P = 0.020). Patients with airway PSIs reported had longer extubation time (0.72 ± 1.56 d vs. 0.16 ± 0.77 d, 95%CI: 0.29 to 0.82, P < 0.001), longer ICU length of stay (LOS) (1.63 ± 5.71 d vs. 0.19 ± 0.84 d, 95%CI: 0.57 to 2.32, P= 0.001), longer post operative LOS (10.56 ± 13.09 d vs. 7.59 ± 10.76 d, 95%CI: 0.41 to 5.53, P = 0.023), and longer total in-hospital LOS (14.99 ± 15.18 d vs. 11.62 ± 11.88 d, 95%CI: 0.46 to 6.27,P = 0.024). Conclusions This single-center retrospective case-control study describes the composition of airway-related PSIs reported by anesthesiologists within thirteen years. Airway incidents might influence patient prognosis by elongating extubation time and LOS. Airway PSI data were worth analyzing to improve patient safety.

Association between the use of a stylet in endotracheal intubation and postoperative arytenoid dislocation: a case-control study.

BACKGROUNDS: Arytenoid dislocation (AD) is a rare but severe complication after general anesthesia with endotracheal intubation. We conducted a case-control study at Peking Union Medical College Hospital to identify risk factors associated with AD, including the use of an intubation stylet. METHODS: Patients who experienced AD were matched 1:3 with controls based on gender, age and type of surgery. Multiple conditional logistic regression was performed to determine associations between potential risk factors and AD. RESULTS: Twenty-six AD cases were retrospectively identified from 2004 through 2016. On average, arytenoid dislocation occurred in 2 cases per year, with an incidence of 0.904/100,000 (approximately 0.01%). The 26 patients who experienced AD and 78 matched control patients were enrolled in this study. All enrolled patients underwent endotracheal intubation, and a stylet was used for intubation for 38.5% (10/26) of the AD patients and 64.1% (50/78) of the controls (OR = 0.23, 0.07-0.74). A higher incidence of AD was significantly associated with longer duration of operation (OR = 1.74, 1.23-2.47). CONCLUSIONS: The use of an intubation stylet for endotracheal intubation appears to protect against AD. Prolonged operation time increases the risk of AD. These factors should be considered when assessing the risks of AD associated with endotracheal intubation and in efforts to avoid this complication.

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation.

The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated.

Systemic Multiple Aneurysms Caused by Vascular Ehlers-Danlos Syndrome.

Systemic multiple aneurysms are rare and usually associated with collagen tissue disease, such as Ehlers-Danlos syndrome (EDS) or Marfan syndrome. In the present case, we describe a 39-year-old male patient with systemic multiple aneurysms and acute intraperitoneal hemorrhage who was clinically diagnosed with vascular EDS. Coil embolization of the distal segment of the common hepatic artery was performed, which resolved the patient's symptoms. With this case presentation, we aim to increase the awareness of vascular EDS among clinicians and emphasize the extreme fragility of the arteries in patients with vascular EDS.