Anesthesiology

Toward Opioid-Free Ambulatory Surgery: A Prospective Study Using Machine Learning to Predict Postoperative Opioid Use.

BACKGROUND: Postoperative opioid use has the risk of dependence and diversion. We developed an opioid-sparing regimen and identified factors associated with postoperative opioid use. STUDY DESIGN: The Toward Opioid-Free Ambulatory Surgery program was developed by establishing a regimen of ibuprofen 600 mg and acetaminophen 650 mg, alternating every 3 hours, with a rescue prescription of oxycodone ...

Perioperative blood loss in cardiac surgery: Validation of machine learning-derived clusters.

BackgroundThere is no universally accepted definition of perioperative blood loss in cardiac surgery. Existing methods are based on chest tube output and are not normalised for patient weight.ObjectiveTo validate machine learning-derived blood loss severity clusters based on a haemoglobin mass loss per kilogram (Hb/kg index).MethodsThis single-center prospective study included 195 patients undergo...

Large language models versus human examinee performance on Israeli anesthesiology board examinations.

Effectiveness of Thoracic Paravertebral Block in Minimally Invasive Vertebroplasty: A Retrospective Cohort Study Focusing on Pain Trajectories and Sagittal Spinal Alignment.

PURPOSE: To evaluate the effectiveness of ultrasound-guided thoracic paravertebral block (TPVB) combined with general anesthesia (GA) versus GA alone ...

[Application of multi-parametric vital sign monitoring systems in the integrated management of the perioperative stage of gastric cancer].

The perioperative management of gastric cancer is a crucial factor influencing patient prognosis. The traditional model has limitations such as fragme...

Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.

INTRODUCTION: Postoperative delirium (POD) adversely affects clinical outcomes among older adults undergoing spine surgery. However, existing predicti...

Unsupervised Phenomapping of Perioperative Risk in Stable Coronary Artery Disease: Revealing Limitations of the Revised Cardiac Risk Index.

AIMS: To derive unsupervised clinical phenotypes in patients with stable coronary artery disease (CAD) undergoing elective non-cardiac surgery and eva...

Extubation Decision Support in Critical Care: A Multimodal Machine Learning Framework Integrating Segmented Radiographs and Routine Clinical Data.

Extubation failure remains a major challenge in critically ill patients and is associated with adverse clinical outcomes. Current extubation decisions...

Predictive value of Hypotension Prediction Index in liver transplantation: a prospective study.

BACKGROUND: Intraoperative hypotension (IOH) is associated with increased postoperative morbidity and mortality. This prospective study evaluated the ...

VitalDB Arrhythmia Database: An Anesthesiologist-Validated Large-scale Intraoperative Arrhythmia Dataset with Beat and Rhythm Labels.

Intraoperative cardiac arrhythmias present distinct characteristics compared to non-surgical environments, yet publicly available electrocardiogram (E...

Fuzzy logic for preanesthetic risk assessment in cataract surgery patients.

We aimed to build a fuzzy logic preanaesthetic risk score tailored to cataract surgery. By fusing systemic comorbidities with key patient attributes i...

Utilization and Feasibility of a Wearable Device in Patients With Sedative Effects of Drugs: Protocol for a Prospective Observational Study for the Advanced Respiratory Monitoring Events in Drug Toxicity (ARM-ED) Study.

BACKGROUND: Drug-related deaths worldwide are most commonly attributed to opioids. Opioids and other sedative drugs can cause respiratory depression a...

A Machine Learning-Based Clinical Tool for Predicting Inadequate Bowel Preparation: Development and Validation.

OBJECTIVES: Colonoscopy is an essential tool for diagnosing and treating colorectal diseases, with bowel preparation serving as a key determinant of i...

Representation learning for the prediction of length of stay in infants with congenital heart diseases undergoing cardiac surgery.

AIMS: Congenital heart disease is the most common birth defect and significantly impacts health, mortality, and healthcare costs. Accurately estimatin...

DeePedIMV: Deep learning model for early prediction of invasive mechanical ventilation in critically ill children.

BACKGROUND: Acute deterioration requiring invasive mechanical ventilation (IMV) is common in critically ill children and may arise from respiratory, c...

Minimally invasive spine surgery: current advantages, limitations, and future directions.

Minimally invasive spine surgery (MISS), supported by advancements in endoscopic systems, tubular retractors, lateral access corridors, image-guided n...

Accuracy of Diagnosis and Anticipation of Future Treatment in Pediatric Peripelvic Infections: The Role of Multisurgeon Review and Implications for Future Machine Learning Algorithms.

INTRODUCTION: MRI is commonly used to evaluate pelvic musculoskeletal infections. Limited "quick" MRI protocols enable timely imaging without intraven...

Artificial intelligence-based chest X-ray (AI-CXR) and hematology parameters to predict mortality and intubation events In COVID-19 patients at the second-referral hospital in Indonesia.

[Expert consensus on the clinical application of robotic-assisted bronchoscopy].

Robotic-assisted bronchoscopy (RAB) is an emerging diagnostic and interventional technology which integrates thin-slice CT-based virtual airway recons...

Perilesional neuromodulation replaces lost sensorimotor function in persons with spinal cord injury.

Spinal cord injury (SCI) results in permanent impairment of sensory, motor and autonomic function. Epidural electrical stimulation (EES) applied below...