Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery

Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model

Background

Current models for predicting intraoperative hemorrhage in cesarean scar ectopic pregnancy (CSEP) are constrained by known risk factors and conventional statistical methods. Our objective is to develop an interpretable prediction model using machine learning (ML) techniques to assess the risk of intraoperative hemorrhage during CSEP in women, followed by external validation and clinical application.

Methods

This multicenter retrospective study utilized electronic medical record (EMR) data from four tertiary medical institutions. The model was developed using data from 1680 patients with CSEP diagnosed and treated at Qilu Hospital of Shandong University, Chongqing Health Center for Women and Children, and Dezhou Maternal and Child Health Care Hospital between January 1, 2008, and December 31, 2023. External validation data were obtained from Liao Cheng Dong Chang Fu District Maternal and Child Health Care Hospital between January 1, 2021, and December 31, 2023. Random forest (RF), Lasso, Boruta, and Extreme Gradient Boosting (XGBoost) were employed to identify the most influential variables in the model development data set; the best variables were selected based on reaching the λmin value. Model development involved eight machine learning methods with ten-fold cross-validation. Accuracy and decision curve analysis (DCA) were used to assess model performance for selection of the optimal model. Internal validation of the model utilized area under the receiver operating characteristic curve (AUC), sensitivity, specificity, Matthews correlation coefficient, and F1 score. These same indicators were also applied to evaluate external validation performance of the model. Finally, visualization techniques were used to present the optimal model which was then deployed for clinical application via network applications.

Findings

Setting λmin at the value of 0.003, the optimal variable combination containing 9 variables was selected for model development. The optimal prediction model (Bayes) had an accuracy of 0.879 (95% CI: 0.857-0.901) an AUC of 0.882 (95% CI: 0.860-0.904), a DCA curve maximum threshold probability of 0.41, and a maximum return of 7.86%. The internal validation accuracy was 0.869 (95% CI: 0.847-0.891), an AUC of 0.822 (95% CI: 0.801-0.843), a sensitivity of 0.938, a specificity of 0.422, a Matthews correlation coefficient of 0.392, and an F1 score of 0.925. In the external validation, the accuracy was 0.936 (95% CI: 0.913-0.959), an AUC of 0.853 (95% CI: 0.832-0.874), a sensitivity of 0.954, a specificity of 0.5, a Matthews correlation coefficient of 0.365, and an F1 score of 0.966. This indicates that the prediction model performed well in both internal and external validation.

Interpretation

The developed prediction model, deployed in the network application, is capable of forecasting the risk of intraoperative hemorrhage during CSEP. This tool can facilitate targeted preoperative assessment and clinical decision-making for clinicians. Prospective data should be utilized in future studies to further validate the extended applicability of the model.

Funding

Natural Science Foundation of Shandong Province; Qilu Hospital of Shandong University.

Artificial intelligence and its clinical application in Anesthesiology: a systematic review

PURPOSE

Application of artificial intelligence (AI) in medicine is quickly expanding. Despite the amount of evidence and promising results, a thorough overview of the current state of AI in clinical practice of anesthesiology is needed. Therefore, our study aims to systematically review the application of AI in this context.

METHODS

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline and Web of Science for articles published up to November 2022 using terms related with AI and clinical practice of anesthesiology. Articles that involved animals, editorials, reviews and sample size lower than 10 patients were excluded. Characteristics and accuracy measures from each study were extracted.

RESULTS

A total of 46 articles were included in this review. We have grouped them into 4 categories with regard to their clinical applicability: (1) Depth of Anesthesia Monitoring; (2) Image-guided techniques related to Anesthesia; (3) Prediction of events/risks related to Anesthesia; (4) Drug administration control. Each group was analyzed, and the main findings were summarized. Across all fields, the majority of AI methods tested showed superior performance results compared to traditional methods.

CONCLUSION

AI systems are being integrated into anesthesiology clinical practice, enhancing medical professionals' skills of decision-making, diagnostic accuracy, and therapeutic response.

A Narrative Review on Intraoperative Adverse Events: Risks, Prevention, and Mitigation

INTRODUCTION

Adverse events from surgical interventions are common. They can occur at various stages of surgical care, and they carry a heavy burden on the different parties involved. While extensive research and efforts have been made to better understand the etiologies of postoperative complications, more research on intraoperative adverse events (iAEs) remains to be done.

METHODS

In this article, we reviewed the literature looking at iAEs to discuss their risk factors, their implications on surgical care, and the current efforts to mitigate and manage them.

RESULTS

Risk factors for iAEs are diverse and are dictated by patient-related risk factors, the nature and complexity of the procedures, the surgeon's experience, and the work environment of the operating room. The implications of iAEs vary according to their severity and include increased rates of 30-day postoperative morbidity and mortality, increased length of hospital stay and readmission, increased care cost, and a second victim emotional toll on the operating surgeon.

CONCLUSIONS

While transparent reporting of iAEs remains a challenge, many efforts are using new measures not only to report iAEs but also to provide better surveillance, prevention, and mitigation strategies to reduce their overall adverse impact.

Factors associated with unintended perianesthesia hypothermia

Background

Our hypothesis was that total intravenous anesthesia (TIVA) is associated with an increase in hypothermia.

Methods

Inclusion criteria were patients from the National Anesthesia Clinical Outcomes Registry undergoing a general anesthetic during 2019. Data collected included patient age, sex, American Society of Anesthesiologists physical status classification system score (ASAPS), duration of anesthetic, use of TIVA, type of procedure, and hypothermia. Continuous variables were compared using Student's t test or Mann Whitney rank sum as appropriate. Mixed effects multiple logistic regression was performed to determine the association between independent variables and hypothermia.

Results

There was a low incidence of hypothermia (1.2%). Patients who became hypothermic were older, had a higher median ASAPS, and had a higher rate of TIVA. TIVA patients had a significantly increased odds for hypothermia when controlling for covariates. Patients undergoing obstetrical, thoracic, or radiological procedures had increased odds for hypothermia. In a matched cohort subset, TIVA was associated with a greater rate and increased odds for hypothermia.

Conclusions

The novel and noteworthy finding was the association between TIVA and perianesthesia hypothermia. Thoracic, radiologic, and obstetrical procedures were associated with greater rates of and odds for hypothermia. Other identified factors can help to stratify patients for risk for hypothermia.

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

BACKGROUND

The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation.

METHODS

A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies.

RESULTS

A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications.

CONCLUSIONS

The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.

EDITOR’S PERSPECTIVE

Retracted: Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery

[This retracts the article DOI: 10.1155/2022/8661324.].