Postoperative Analgesic Efficacy of Preemptive and Postoperative Lornoxicam or Tramadol in Lumbar Disc Surgery

The Adequacy of Anesthesia Guidance for Vitreoretinal Surgeries with Preemptive Paracetamol/Metamizole

Despite the possibility of postoperative pain occurrence, in some patients, vitreoretinal surgeries (VRSs) require performance of general anesthesia (GA). The administration of intraoperative intravenous rescue opioid analgesics (IROA) during GA constitutes a risk of perioperative adverse events. The Adequacy of Anesthesia (AoA) concept consists of an entropy electroencephalogram to guide the depth of GA and surgical pleth index (SPI) to optimize the titration of IROA. Preemptive analgesia (PA) using cyclooxygenase-3 (COX-3) inhibitors is added to GA to minimize the demand for IROA and reduce postoperative pain. The current analysis evaluated the advantage of PA using COX-3 inhibitors added to GA with AoA-guided administration of IROA on the rate of postoperative pain and hemodynamic stability in patients undergoing VRS. A total of 165 patients undergoing VRS were randomly allocated to receive either GA with AoA-guided IROA administration with intravenous paracetamol/metamizole or with preemptive paracetamol or metamizole. Preemptive paracetamol resulted in a reduction in the IROA requirement; both preemptive metamizole/paracetamol resulted in a reduced rate of postoperative pain as compared to metamizole alone. We recommend using intraoperative AOA-guided IROA administration during VRS to ensure hemodynamic stability alongside PA using both paracetamol/metamizole to reduce postoperative pain.

Machine Learning Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review

INTRODUCTION

A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms' use in clinical practice.

METHODS

We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords "machine learning," "opioid," and "prediction." The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients.

RESULTS

Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation.

CONCLUSIONS

ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.