Nasotracheal prolonged safe extubation in acute respiratory failure post-thyroidectomy: An efficacious technique to avoid tracheotomy? A retrospective analysis of a large case series

Development of a Machine-Learning Model for Prediction of Extubation Failure in Patients with Difficult Airways after General Anesthesia of Head, Neck, and Maxillofacial Surgeries

(1) Background: Extubation failure after general anesthesia is significantly associated with morbidity and mortality. The risk of a difficult airway after the general anesthesia of head, neck, and maxillofacial surgeries is significantly higher than that after general surgery, increasing the incidence of extubation failure. This study aimed to develop a multivariable prediction model based on a supervised machine-learning algorithm to predict extubation failure in adult patients after head, neck, and maxillofacial surgeries. (2) Methods: A single-center retrospective study was conducted in adult patients who underwent head, neck, and maxillofacial general anesthesia between July 2015 and July 2022 at the Shanghai Ninth People's Hospital. The primary outcome was extubation failure after general anesthesia. The dataset was divided into training (70%) and final test sets (30%). A five-fold cross-validation was conducted in the training set to reduce bias caused by the randomly divided dataset. Clinical data related to extubation failure were collected and a stepwise logistic regression was performed to screen out the key features. Six machine-learning methods were introduced for modeling, including random forest (RF), k-nearest neighbor (KNN), logistic regression (LOG), support vector machine (SVM), extreme gradient boosting (XGB), and optical gradient boosting machine (GBM). The best performance model in the first cross-validation dataset was further optimized and the final performance was assessed using the final test set. (3) Results: In total, 89,279 patients over seven years were reviewed. Extubation failure occurred in 77 patients. Next, 186 patients with a successful extubation were screened as the control group according to the surgery type for patients with extubation failure. Based on the stepwise regression, seven variables were screened for subsequent analysis. After training, SVM and LOG models showed better prediction ability. In the k-fold dataset, the area under the curve using SVM and LOG were 0.74 (95% confidence interval, 0.55-0.93) and 0.71 (95% confidence interval, 0.59-0.82), respectively, in the k-fold dataset. (4) Conclusion: Applying our machine-learning model to predict extubation failure after general anesthesia in clinical practice might help to reduce morbidity and mortality of patients with difficult airways after head, neck, and maxillofacial surgeries.

Outcomes of 1,327 patients operated on through twelve multispecialty surgical volunteerism missions: A retrospective cohort study

BACKGROUND

Surgical volunteer missions (SVMs) have become a popular approach for reducing the burden of surgical disease worldwide. The aim of this study was to evaluate the outcomes of 12 surgical missions between 2006 and 2018 from the mission entitled "Operation Giving Back Bohol" Tagbilaran, Philippines and discuss the lessons learned during these missions in particular seven challenges that every volunteer surgeon should be familiar with.

METHODS

This was a retrospective descriptive study of prospectively collected data on all patients treated during one SVM. The data collected included gender, age, diagnosis, types of surgeries performed, and perioperative adverse events.

RESULTS

During the study period 1327 operations were performed (842 females (63.4%) and 485 males (36.6%); (male-to-female ratio 0.59); mean age 37 ± 18 years. The majority of operations were for thyroid disease (31.6%), followed by hernia (17.3%), hysterectomies/salpingo-oophorectomies (12.2%), soft tissue tumors (9.9%), cleft lip/palate repairs (7.2%), breast (6.4%), gallbladder disease (4.7%), cataract (2.9%), parotid masses (1.4%) and others (6.4%). For each mission, there were an average 5.5 days of operating, performing a median of 105.5 (80-148) cases per mission. There were 27 complications (2%), of which, 22 were postoperative bleeding and two temporary tracheostomies. The mortality rate was 0.15% (2/1327). In one patient, the family withdrew care following compassionate last ditch effort thyroidectomy for advanced cancer and one patient died as a result of intracranial bleeding from a brain tumor, which was unrecognized before mastectomy.

CONCLUSIONS

Surgical volunteerism missions are safe and valuable in lessening the burden of surgical disease globally when performed in an organized fashion and with continuity of care. However, there is need for standardization of surgical care provided during SVMs and creation of a world-wide database of all SVMs, and each surgeon and others who participate in these mission should be familiar with critical elements and challenges for the successful mission.