Journal of anesthesia
-
Journal of anesthesia · Jul 2024
Pediatric cardiac surgery: machine learning models for postoperative complication prediction.
Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machine Learning (ML)-assisted tools have the potential to enhance the recognition of patients at risk of complications and predict potential issues, ultimately improving outcomes. ⋯ Our machine learning-assisted tools provide an additional perspective and enhance the predictive capabilities of traditional scoring methods. These tools can assist anesthesiologists in making well-informed decisions. Furthermore, we have successfully demonstrated the feasibility of creating a practical white-box model. The next steps involve conducting clinical validation and multicenter cross-validation.
-
Journal of anesthesia · Jul 2024
Relationship between epidural catheter migration beneath the skin and subcutaneous fat thickness assessed using postoperative CT imaging: a retrospective cross-sectional study.
The causes of epidural catheter migration beneath the skin have not been previously investigated. We hypothesized that greater subcutaneous fat thickness might be associated with increased catheter migration beneath the skin. ⋯ We found a negative correlation between epidural catheter migration beneath the skin and subcutaneous fat thickness. Anesthesiologists should be aware of the possibility of substantial subcutaneous curving of the catheter, especially in patients with scant subcutaneous fat.