Classifying High-Risk Patients for Persistent Opioid Use After Major Spine Surgery: A Machine-Learning Approach.

Background: Persistent opioid use is a common occurrence after surgery and prolonged exposure to opioids may result in escalation and dependence. The objective of this study was to develop machine-learning-based predictive models for persistent opioid use after major spine surgery.

Methods: Five classification models were evaluated to predict persistent opioid use: logistic regression, random forest, neural network, balanced random forest, and balanced bagging.

Impact of SGLT2 Inhibitors on Renal Function in Type 2 Diabetic Patients with Coronary Artery Disease Undergoing Percutaneous Intervention: A Systematic Review and Meta-Analysis.

Background And Objectives: Contrast agents directly cause kidney toxicity in patients undergoing Percutaneous Intervention for cardiovascular disease with Type 2 diabetes. This meta-analysis aims to evaluate the effects of SGLT2-i on renal function in individuals undergoing Percutaneous Intervention.

Methods: The databases used for the search included PubMed, Scopus, Cochrane Central Registry of Controlled Trials, and Google Scholar.

Development and benchmarking of machine learning models to classify patients suitable for outpatient lower extremity joint arthroplasty.

Study Objective: Performing hip or knee arthroplasty as an outpatient surgery has been shown to be operationally and financially beneficial for selected patients. By applying machine learning models to predict patients suitable for outpatient arthroplasty, health care systems can better utilize resources efficiently. The goal of this study was to develop predictive models for identifying patients likely to be discharged same-day following hip or knee arthroplasty.