Anesthesiology

Risk prediction modelling of 30-day all-cause mortality following percutaneous coronary intervention in an Australian population: leveraging machine learning.

BACKGROUND: Preprocedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and benchmarking hospital performance. This study aimed to identify preprocedural factors to predict the risk of 30-day all-cause mortality post-PCI using machine learning (ML) approaches. METHODS: The study analysed 93 055 consecutive PCI procedur...

Incidence rate of and risk factors for anorectal diseases in primiparae in the first 6 weeks following delivery.

OBJECTIVES: We aimed to explore the incidence rate of and risk factors for anorectal diseases in primiparae in the first 6 weeks following delivery, and to establish a risk evaluation model for the early identification of high-risk groups and precise intervention. METHODS: Subjects (350 in total) in this prospective observational cohort study were recruited from eligible primiparae who delivered b...

From innovation to implementation: Addressing the AI adoption gap in cardiac surgery.

BACKGROUND: While artificial intelligence (AI) is advancing rapidly across cardiovascular medicine, its translation into cardiac surgery remains limit...

Machine learning-based prognostic analysis of patients with status epilepticus in the neurological intensive care unit.

OBJECTIVE: This study aimed to develop and validate machine learning (ML) models for predicting the prognosis of status epilepticus (SE) patients with...

Machine learning prediction model for delirium after heart valve replacement with cardiopulmonary bypass: A large-scale cohort study.

OBJECTIVE: Postoperative delirium (POD) is a common and severe complication following heart valve replacement (HVR) with cardiopulmonary bypass (CPB),...

Updated Policy on the Use of Artificial Intelligence for the Journal of Neurosurgical Anesthesiology.

Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using machine learning.

To develop and evaluate machine learning (ML) models that infer preoperative cognitive function from intraoperative electroencephalography (EEG). This...

Early Prediction of Standing at Discharge in Moderate-to-Severe TBI: A Clinical Machine Learning Model Integrating Modifiable and Nonmodifiable Factors.

OBJECTIVE: To develop and internally validate a machine learning model to predict favorable standing ability at hospital discharge in patients with mo...

Fully automated, deep learning, cardiac CT-based multimodal network for cardiovascular risk stratification in high-risk perioperative patients.

AIMS: Major adverse cardiac events (MACE) significantly impact perioperative morbidity and mortality. We aimed to develop a fully automated multimodal...

Artificial Intelligence-Enabled Electrocardiographic Detection of Severe Aortic Stenosis Leading to Transcatheter Aortic Valve Replacement.

BACKGROUND: EchoNext is an artificial intelligence (artificial intelligence)-enabled electrocardiographic (ECG) model validated to detect unrecognized...

Accuracy, quality, and readability analyses of responses from large language models to questions on pediatric dental sedation.

Determinants of labor epidural analgesia uptake and associations with maternal-neonatal outcomes: a stratified cohort study with risk prediction modeling.

BACKGROUND: Labor pain is a major physiological and psychological stressor for women during childbirth. Epidural analgesia is widely recognized as the...

ARIMA-based forecasting of cerebral physiologic signals in acute traumatic brain injury: a CAnadian high-resolution TBI (CAHR-TBI) cohort study.

BACKGROUND: Traumatic brain injury (TBI) remains a major global health issue, with limited progress in reducing morbidity and mortality for TBI patien...

Interpretable machine learning model for predicting the risk of dysphagia in patients after cardiac surgery.

BACKGROUND: Dysphagia is recognized as one of the most common severe complications following cardiac surgery, with the potential to result in adverse ...

Artificial Intelligence for Predicting Clinical Outcomes in Interventional Pain Medicine for Spine Disorders: A Systematic Review.

OBJECTIVES: Artificial intelligence (AI) applications are being increasingly explored in pain medicine due to AI's ability to handle multidimensional ...

External Validation of an AI-Based Colonoscopy Quality Assessment Tool in Japanese Clinical Practice: A Pilot Study.

BACKGROUND: While AI-assisted polyp detection has advanced significantly, comprehensive AI-based colonoscopy quality assessment tools remain limited. ...

Stratified Causal Inference for Intensive Care Unit Risk Prediction: Informatics-Based Modeling of Anesthetic Drug Combinations.

BACKGROUND: Postoperative intensive care unit (ICU) admission affects 15% to 20% of surgical patients and represents a major source of morbidity and h...

Artificial intelligence assistance (ASReview Lab) reduces screening workload but its practical impact remains uncertain: a post hoc analysis of a regional anesthesia systematic review.

Machine learning in trauma anesthesia - from promise to practice.

Hierarchical Channeled Graphitized Nanoarchitecture as a Diagnostic Platform for Maternal Fever Warning.

The precision and effectiveness of nano-diagnostic platforms rely on the deliberate design of advanced nanomaterials, aiming to address the clinical c...