Accuracy of a Dual-Sensor Heat-Flux (DHF) Non-Invasive Core Temperature Sensor in Pediatric Patients Undergoing Surgery.

Accurate temperature measurement is crucial for the perioperative management of pediatric patients, and non-invasive thermometry is necessary when invasive methods are infeasible. A prospective observational study was conducted on 57 patients undergoing elective surgery. Temperatures were measured using a dual-sensor heat-flux (DHF) thermometer (Tcore™) and a rectal temperature probe (TRec), and the agreement between the two measurements was assessed. The DHF measurements showed a bias of +0.413 °C compared with those of the TRec. The limits of agreement were broader than the pre-defined ±0.5 °C range (-0.741 °C and +1.567 °C). Although the DHF sensors tended to overestimate the core temperature compared to the rectal measurements, an error grid analysis demonstrated that 95.81% of the DHF measurements would not have led to a wrong clinical decision, e.g., warming or cooling when not necessary. In conclusion, the low number of measurements that would have led to incorrect decisions suggests that the DHF sensor can be considered an option for continuous temperature measurement when more invasive methods are infeasible.

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

BACKGROUND: Inadvertent intraoperative hypothermia is a common complication that affects patient comfort and morbidity. As the development of hypothermia is a complex phenomenon, predicting it using machine learning (ML) algorithms may be superior to logistic regression. METHODS: We performed a single-center retrospective study and assembled a feature set comprised of 71 variables. The primary outcome was hypothermia burden, defined as the area under the intraoperative temperature curve below 37 °C over time. We built seven prediction models (logistic regression, extreme gradient boosting (XGBoost), random forest (RF), multi-layer perceptron neural network (MLP), linear discriminant analysis (LDA), k-nearest neighbor (KNN), and Gaussian naïve Bayes (GNB)) to predict whether patients would not develop hypothermia or would develop mild, moderate, or severe hypothermia. For each model, we assessed discrimination (F1 score, area under the receiver operating curve, precision, recall) and calibration (calibration-in-the-large, calibration intercept, calibration slope). RESULTS: We included data from 87,116 anesthesia cases. Predicting the hypothermia burden group using logistic regression yielded a weighted F1 score of 0.397. Ranked from highest to lowest weighted F1 score, the ML algorithms performed as follows: XGBoost (0.44), RF (0.418), LDA (0.406), LDA (0.4), KNN (0.362), and GNB (0.32). CONCLUSIONS: ML is suitable for predicting intraoperative hypothermia and could be applied in clinical practice.

Intravenous diclofenac and orphenadrine for the treatment of postoperative pain after remifentanil-based anesthesia : A double-blinded, randomized, placebo-controlled study.

BACKGROUND: Postoperative intravenous diclofenac reduces patient opioid demand and is commonly used in surgical units. Orphenadrine is mainly used in combination with diclofenac for musculoskeletal injuries and postoperative pain control. The objective of this study was to compare the analgesic efficacy of diclofenac-orphenadrine, diclofenac alone and saline. METHODS: We performed a double-blind, randomized, placebo-controlled, parallel-group, single-center clinical study investigating the opioid-sparing effect of a combination of diclofenac and orphenadrine versus diclofenac alone versus isotonic saline solution. Initially 72 patients were included and received total intravenous anesthesia during cruciate ligament surgery. All patients were postoperatively treated with a patient-controlled analgesia (PCA) device containing hydromorphone. Pharmacological safety was assessed by laboratory parameters, vital signs, and delirium detection scores. RESULTS: There was no significant difference between the groups in cumulative dose of PCA analgesics required after 24 h postsurgery, with 5.90 mg (SD ± 2.90 mg) in the placebo group, 5.73 mg (SD ± 4.75 mg) in the diclofenac group, and 4.13 mg (SD ± 2.57 mg) in the diclofenac-orphenadrine group. Furthermore, there was no significant difference between the groups in cumulative dose of PCA analgesics required 2 h postsurgery (n = 65). Mean dose of hydromorphone required after 2 h was 1.54 mg (SD ± 0.57 mg) in the placebo group, 1.56 mg (SD ± 1.19 mg) in the diclofenac-only group, and 1.37 mg (SD ± 0.78 mg) in the diclofenac-orphenadrine group. However, when comparing the diclofenac-orphenadrine group and the diclofenac group combined to placebo there was a significant reduction in PCA usage in the first 24 h postsurgery. In total, there were 25 adverse events reported, none of which were rated as severe. CONCLUSION: Orphenadrine-diclofenac failed to significantly reduce postoperative opioid requirements. However, in an exploratory post hoc analysis the diclofenac-orphenadrine and the diclofenac group combined versus placebo showed a tendency to reduce opioid demand in postoperative pain control. Further research is required to determine the value of orphenadrine as an adjuvant in a multimodal approach for postoperative pain management.

Virtual and Augmented Reality Applications in Medicine: Analysis of the Scientific Literature.

BACKGROUND: Virtual reality (VR) and augmented reality (AR) have recently become popular research themes. However, there are no published bibliometric reports that have analyzed the corresponding scientific literature in relation to the application of these technologies in medicine. OBJECTIVE: We used a bibliometric approach to identify and analyze the scientific literature on VR and AR research in medicine, revealing the popular research topics, key authors, scientific institutions, countries, and journals. We further aimed to capture and describe the themes and medical conditions most commonly investigated by VR and AR research. METHODS: The Web of Science electronic database was searched to identify relevant papers on VR research in medicine. Basic publication and citation data were acquired using the "Analyze" and "Create Citation Report" functions of the database. Complete bibliographic data were exported to VOSviewer and Bibliometrix, dedicated bibliometric software packages, for further analyses. Visualization maps were generated to illustrate the recurring keywords and words mentioned in the titles and abstracts. RESULTS: The analysis was based on data from 8399 papers. Major research themes were diagnostic and surgical procedures, as well as rehabilitation. Commonly studied medical conditions were pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders. Overall, contributions to the literature were globally distributed with heaviest contributions from the United States and United Kingdom. Studies from more clinically related research areas such as surgery, psychology, neurosciences, and rehabilitation had higher average numbers of citations than studies from computer sciences and engineering. CONCLUSIONS: The conducted bibliometric analysis unequivocally reveals the versatile emerging applications of VR and AR in medicine. With the further maturation of the technology and improved accessibility in countries where VR and AR research is strong, we expect it to have a marked impact on clinical practice and in the life of patients.