BMC anesthesiology
-
To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway management. ⋯ Algorithms for machine learning provide insightful information for anticipating challenging airway management. By making it possible to forecast airway difficulties more accurately, these techniques can potentially improve clinical practice and patient outcomes.
-
The effect of ramelteon, a melatonin receptor agonist, on survival in septic patients remains unknown. The purpose of this retrospective cohort study was to explore the relationship between ramelteon exposure and survival outcomes in septic patients. ⋯ This exploratory, retrospective study suggests an association between ramelteon exposure and reduced 30-day and 90-day mortality in septic patients compared with the non-exposure group. Considering the limitations of the retrospective design and the potential for unmeasured confounding, well-designed prospective studies and randomized controlled trials will be needed to confirm these findings.
-
Review
The anesthesiologist's guide to critically assessing machine learning research: a narrative review.
Artificial Intelligence (AI), especially Machine Learning (ML), has developed systems capable of performing tasks that require human intelligence. In anesthesiology and other medical fields, AI applications can improve the precision and efficiency of daily clinical practice, and can also facilitate a personalized approach to patient care, which can lead to improved outcomes and quality of care. ML has been successfully applied in various settings of daily anesthesiology practice, such as predicting acute kidney injury, optimizing anesthetic doses, and managing postoperative nausea and vomiting. ⋯ Understanding evaluation metrics is essential, as they provide detailed information on model performance and their ability to discriminate between individual class rates. This article offers a comprehensive framework in assessing the validity, applicability, and limitations of models, guiding responsible and effective integration of ML technologies into clinical practice. A balance between innovation, patient safety and ethical considerations must be pursued.
-
Disorientation is an early indicator of developing postoperative delirium (POD), which is associated with increased mortality and cognitive decline. The well-established "Confusion-Assessment-Method-for-Intensive-Care-Unit" (CAM-ICU) for diagnosing POD in intubated patients cannot make use of the feature 'disorientation', as this requires verbal communication. Other tools such as the 4AT test for disorientation but are not established in ICU settings. We therefore combined test-variables of the CAM-ICU (level of consciousness, fluctuating mental status and inattention) with verbal testing for disorientation to develop and enhance diagnostic accuracy of the "Confusion Assessment Method for Intermediate Care Unit" (CAM-IMC). In the present study we describe the development and the evaluation of the diagnostic accuracy of the CAM-IMC. ⋯ The CAM-IMC demonstrates excellent test performance for diagnosing POD in non-intubated patients by combining features of the CAM-ICU with 'disorientation'. Given an aging community with an increasing delirium risk, the CAM-IMC provides a highly structured assessment tool for POD. It enables early and accurate detection of delirium, which is critical for timely intervention and improved patient outcomes. The CAM-IMC appears to be a useful tool to be implemented in units for not-intubated patients and seems to be the perfect match where the CAM-ICU is already in use for monitoring POD.
-
Postoperative sore throat is a frequent and distressing complication caused by airway instrumentation during general anesthesia. The discomfort can lead to immediate distress, delayed recovery and reduce patient satisfaction. The objective of this study was to determine the effectiveness of preoperative ketamine gargle on the occurrence of postoperative sore throat among adult patients who underwent surgery under general anesthesia with endotracheal tube. ⋯ Preoperative ketamine gargle before induction of general anesthesia is effective to reduce the occurrence of postoperative sore throat in adult patients undergoing surgery under general anesthesia with an endotracheal tube. Further studies with large sample size, better study quality and optimal reporting could be conducted to determine the long-term efficacy and safety of ketamine gargle in different surgical populations.