Acute Coronary Syndrome

Harnessing the gut-heart axis for cardiovascular drug innovation: microbiome, metabolites, and personalized treatment strategies.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide despite major advances in pharmacotherapy. Emerging evidence reveals a pivotal role for the gut-heart axis, wherein gut microbiota are and their metabolites influence CV physiology, pathology, and drug responsiveness. Dysbiosis in conditions such as hypertension, atherosclerosis, and heart failure has been associated wit...

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast and fragmented biomedical literature, which now exceeds 38 million publications indexed in PubMed. To address the central challenge of navigating and synthesizing a huge fragmented biomedical literature base, we applied our validated machine learning-b...

Adversarial Debiasing for Equitable and Fair Detection of Acute Coronary Syndrome Using 12-Lead ECG.

OBJECTIVE: Acute coronary syndrome (ACS) is a life-threatening condition requiring accurate diagnosis for better outcomes. However, variability in sig...

Integrated approaches to preventing macrovascular complications in type 2 diabetes mellitus: Advances in secondary prevention.

BACKGROUND: Type 2 diabetes mellitus (T2DM) substantially increases the risk of macrovascular complications, including coronary artery disease, cerebr...

A fine-grained transformer combined with multimodal data for predicting hospital length of stay in acute coronary syndrome.

The prediction of hospital length of stay (LOS) is of great significance for hospitals to rationally allocate medical resources and provide timely tre...

Longitudinal Strain by Artificial Intelligence-Driven Automated Strain Analysis for Left Ventricular Function Evaluation and Infarct Region Estimation.

PURPOSE: Accurate evaluation of left ventricular (LV) dysfunction and infarct localization in acute myocardial infarction (AMI) remains challenging du...

Explainable machine learning framework based on blood biomarkers and routine health information for assistive diagnosis and risk stratification of acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) presents a critical clinical challenge due to its rapid progression and high mortality, compounded by di...

[Development of a deep learning model for predicting adverse cardiovascular events in patients with acute coronary syndrome based on retinal fundus images].

Objective: To develop and validate a deep learning model based on retinal fundus images for predicting the risk of long-term adverse cardiovascular ev...

High Sensitivity Cardiac Troponin I Detection via MP-Locked Aptamer and Multimeric DNAzyme-Coupled Hyperbranched Hybridization Chain Reaction.

Timely and sensitive detection of cardiac troponin I (cTnI) is critical for early diagnosis of myocardial infarction, particularly at the point-of-car...

Evaluation of the impact of NOAC underdosing and exploration of bleeding risk factors in elderly patients with atrial fibrillation: artificial intelligence-based approach.

OBJECTIVE: Atrial fibrillation in elderly patients increases the risk of thromboembolism, necessitating long-term anticoagulation. While non-vitamin K...

First-line risk stratification with machine learning models facilitates rapid triage for non-ST-elevation myocardial infarction.

Timely diagnosis of non-ST-elevation myocardial infarction (NSTEMI) remains challenging, as current protocols rely on serial high-sensitivity cardiac ...

Multicentre development and validation of a risk model integrating immunotherapy and coagulation biomarkers for thrombosis in autoimmune neurological disorders.

PURPOSE: Patients with Autoimmune Neurological Disorders (ANDs) require routine immunomodulatory therapy, which inherently increases thrombosis risk. ...

Neoadjuvant Radiation is Causally Linked to Increased Operative Time and Perioperative Blood Transfusion in Pancreatic Ductal Adenocarcinoma.

INTRODUCTION: Blood transfusion in patients undergoing surgical resection for pancreatic ductal adenocarcinoma (PDAC) is associated with worse outcome...

Platelets as immune sensors: monitoring immune dynamics and diagnosing disease states across multiple disorders.

BACKGROUND: Beyond their classical roles in haemostasis and thrombosis, platelets have been recognised as active regulators of immune responses. Howev...

Assessment of Predictive Factors That Shorten Duration of Treatment in Patients With Multiple Myeloma Using AI: Real-World Longitudinal Study Using Data From Medical Data Vision Claims Database.

BACKGROUND: With the availability of newer therapies, the duration of therapy (DoT) shortens with each increasing line of treatment in Japanese patien...

Explainable machine learning for risk prediction of acute cardiac tamponade during atrial fibrillation ablation.

Cardiac tamponade is a rare yet catastrophic complication during atrial fibrillation (AF) catheter ablation. Influenced by multiple procedural and pat...

Prediction model for deep vein thrombosis stability based on multiple machine learning methods.

BackgroundThis study aimed to develop multiple machine learning (ML) models to predict DVT stability based on clinical and computed tomography (CT) te...

Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis.

Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification re...

Interpretable multimodal machine learning model for predicting health risks of patients with heart failure.

Heart failure (HF) is one of the major causes of morbidity and mortality globally, necessitating accurate tools for health outcome prediction and risk...

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may ste...