Predicting Intraoperative Hypothermia Burden during Non-Cardiac Surgery: A Retrospective Study Comparing Regression to Six Machine Learning Algorithms

Global trends in artificial intelligence research in anesthesia from 2000 to 2023: a bibliometric analysis

BACKGROUND

Interest in artificial intelligence (AI) research in anesthesia is growing rapidly. However, there is a lack of bibliometric analysis to measure and analyze global scientific publications in this field. The aim of this study was to identify the hotspots and trends in AI research in anesthesia through bibliometric analysis.

METHODS

English articles and reviews published from 2000 to 2023 were retrieved from the Web of Science Core Collection (WoSCC) database. The extracted data were summarized and analyzed using Microsoft Excel, and bibliometric analysis were conducted with VOSviewer software.

RESULTS

AI research literature in anesthesia has exhibited rapid growth in recent years. The United States leads in the number of publications and citations, with Stanford University as the most prolific institution. Hyung-Chul Lee is the author with the highest number of publications. The journal Anesthesiology is highly recognized and authoritative in this field. Recent keywords include "musculoskeletal pain", "precision medicine", "stratification", "images", "mean arterial pressure", " enhanced recovery after surgery", "frailty", "telehealth", "postoperative delirium" and "postoperative mortality" indicating hot topics in AI research in anesthesia.

CONCLUSIONS

Publications on AI research in the field of anesthesia have experienced rapid growth over the past two decades and are likely to continue increasing. Research areas such as depth of anesthesia (DOA) and drug infusion (including electroencephalography and deep learning), perioperative risk assessment and prediction (covering mean arterial pressure, frailty, postoperative delirium, and mortality), image classification and recognition (for applications such as ultrasound-guided nerve blocks, vascular access, and difficult airway assessment), and perioperative pain management (particularly musculoskeletal pain) have garnered significant attention. Additionally, topics such as precision medicine, enhanced recovery after surgery, and telehealth are emerging as new hotspots and future directions in this field.

Predictive tool for the risk of hypothermia during laparoscopic gynecologic tumor resection

OBJECTIVE

Based on the machine learning algorithm, construct a hypothermia prediction model for gynecological tumor resection under laparoscopic general anesthesia.

METHODS

This research conducted a retrospective analysis, gathering data from individuals who had undergone minimally invasive surgical procedures for gynecological tumors in a Chinese Hospital, ranging from June 2018 to August 2024. During this timeframe, a total of 308 cases were examined for analysis, with 70% of the cases allocated to the modeling dataset and the remaining 30% designated for the validation dataset. The factors associated with intraoperative hypothermia were identified within the modeling dataset. Subsequently, three predictive models were established: a Classification and Regression Trees model, a Random Forest model, and a Support Vector Machine model.

RESULTS

The incidence of intraoperative hypothermia in 308 cases was 30.84%. The results showed that age, body mass index, preoperative body temperature, preoperative albumin, operating room temperature and peritoneal lavage fluid volume were the influencing factors of intraoperative hypothermia. Using these variables, it was determined that the Random Forest model had demonstrated strong predictive performance.

CONCLUSION

The prediction model developed using the random forest algorithm exhibits excellent predictive capabilities, which are highly significant for pinpointing the critical factors contributing to intraoperative hypothermia in patients undergoing laparoscopic procedures.

Application value of machine learning models in predicting intraoperative hypothermia in laparoscopic surgery for polytrauma patients

BACKGROUND

Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications. Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.

AIM

To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.

METHODS

This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023. Of these, 154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio. In the training set, 53 cases experienced intraoperative hypothermia and 101 did not. Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery. The area under the curve (AUC), sensitivity, and specificity were calculated.

RESULTS

Comparison of the hypothermia and non-hypothermia groups found significant differences in sex, age, baseline temperature, intraoperative temperature, duration of anesthesia, duration of surgery, intraoperative fluid infusion, crystalloid infusion, colloid infusion, and pneumoperitoneum volume (P < 0.05). Differences between other characteristics were not significant (P > 0.05). The results of the logistic regression analysis showed that age, baseline temperature, intraoperative temperature, duration of anesthesia, and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery (P < 0.05). Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence (P > 0.05). The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets, respectively.

CONCLUSION

Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery, which improved surgical safety and patient recovery.