Acute Coronary Syndrome

Machine Learning-Based Risk Prediction of In-Hospital Mortality in Patients With Non-ST-Elevation Acute Coronary Syndrome at Various Stages of the Diagnostic Process: Observational Study.

BACKGROUND: Despite advances in understanding and treating non-ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non-ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantia...

Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.

PURPOSE: Peripherally inserted central catheter-related thrombosis (PICC-RT) is a common and serious complication in patients with hematological malignancies, leading to treatment interruptions and increased morbidity. Traditional risk assessment models are often static and lack hematology-specific predictors, resulting in suboptimal performance. We aimed to develop and validate a robust machine l...

Development and validation of a deep learning model to predict heparin response in critically ill patients with deep vein thrombosis receiving continuous intravenous heparin infusion.

Continuous intravenous heparin infusion is widely used for deep vein thrombosis (DVT) in the intensive care unit (ICU), but accurate prediction of act...

Machine Learning Models Integrating CYP2C19 Genotypes for Long-Term Risk Prediction and Personalized Antiplatelet Therapy Selection in Myocardial Infarction.

Machine learning (ML) models integrating genetic and clinical data show promise for personalizing antiplatelet therapy after myocardial infarction (MI...

Pharmacogenomic and Drug Interactions Risk in Cardio-Oncology: A precision medicine perspective for India.

Cardio-oncology patients may face complex treatment regimens due to the concurrent existence of cancer and cardiovascular disease, leading to a consid...

Energy-Decoupled Photoelectrochemical and Pressure Dual-Mode Biosensors for Machine-Learning-Assisted Immunoassay.

Reliable and ultrasensitive detection of cardiac troponin I (cTnI) remains challenging due to the signal interference and limited self-validation capa...

Adding Artificial Intelligence to the PERT Improves Time to Diagnosis and Treatment of Pulmonary Embolism.

OBJECTIVES: There is limited data demonstrating the benefit of artificial intelligence technology in the diagnosis and triage of pulmonary embolism. O...

American College of Surgeons NSQIP Hospital Benchmarking Using Bayesian Variational Inference to Adjust for Many CPT Codes.

BACKGROUND: Current ACS NSQIP benchmarking relies primarily on the principal Current Procedural Terminology (CPT) code for procedure-related risk adju...

Machine learning-derived clinical phenotypes of ascites in cirrhosis: A multi-center latent class analysis with external validation.

BACKGROUND: Ascites development in cirrhosis reduces five-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comp...

AI-Based Phenotyping of Atrial Fibrillation Through Generative Topographic Mapping: Prospective Murcia Atrial Fibrillation Project III Cohort Study.

BACKGROUND: The clinical heterogeneity of atrial fibrillation (AF) challenges current classifications and risk scores, limiting their real-world appli...

Does Maximum Troponin T Value Correlate With Acute Coronary Occlusion Among Patients Treated as NSTEMI?

BACKGROUND: Occlusion myocardial infarction (OMI) is increasingly recognized among NSTEMI patients, yet current diagnostic paradigms may fail to detec...

Seeing Beyond the Surgeon's Eye: Artificial Intelligence for Postoperative Monitoring of Free Vascularized Flaps: A Narrative Review.

BACKGROUND: Postoperative monitoring of free vascularized flaps (FVFs) is critical for early detection of ischemia and timely salvage. Although tradit...

AI-Assisted Electrocardiogram Interpretation Improves ST-Elevation Myocardial Infarction Diagnostic Accuracy Among Advanced Practice Providers: A Prospective Randomized Crossover Study.

INTRODUCTION: Timely and accurate diagnosis of ST-elevation myocardial infarction (STEMI) is critical in military operational environments where evacu...

From Caffeine to Aspirin: Everyday Molecules as Triggers for Genetically Encoded Proximity Systems.

Chemically induced proximity (CIP) enables programmable control of gene expression, protein activity, cellular signaling, and engineered cell function...

Deep learning electrocardiogram analysis for rapid rule-out of acute coronary syndrome in the emergency department: a multicenter diagnostic accuracy study.

BACKGROUND: Rapid and accurate exclusion of acute coronary syndrome (ACS) in patients presenting with chest pain remains a major clinical challenge. D...

Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study.

OBJECTIVE: To develop a predictive model for coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM) based on Artificial...

Compliance Control of a Robotic Breast Ultrasound System Based on Variable Admittance Control.

INTRODUCTION: In a robotic breast ultrasound (US) system (RBUS), ensuring patient safety, comfort, and high compliance is critical. METHOD: To improve...

Reconsidering Fibrinolysis in Selected Patients with Non-ST-Elevation Acute Coronary Syndrome: A Hypothesis-Generating, Research-Based Framework.

For decades, the use of fibrinolytic agents in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) has been contraindicated by major cli...

Artificial intelligence shows comparable or improved performance to traditional risk models in predicting atrial fibrillation after cryptogenic stroke.

BACKGROUND AND AIMS: Detecting subclinical atrial fibrillation (AF) and initiating anticoagulation therapy are critical for secondary stroke preventio...

Stepwise increasing input to machine learning models predicting 30-day AMI or death in emergency department chest-pain patients.

Introduction This study evaluated the performance of neural network (NN) models with stepwise increasing input for identifying acute myocardial infarc...