Acute Coronary Syndrome

Hidden Preoperative Lower-Extremity Venous Thrombosis in Pelvic Organ Prolapse Surgery: Prevalence and Development of a Clinical Prediction Model.

INTRODUCTION AND HYPOTHESIS: Preoperative lower-extremity venous thrombosis (LEVT) is frequently overlooked in women undergoing pelvic organ prolapse (POP) surgery, despite its potential impact on perioperative outcomes. Conventional risk tools, including the Caprini score and D-dimer, show limited performance in this population, and the true prevalence of occult preoperative LEVT remains uncertai...

Integrated Diagnosis of Submassive Pulmonary Embolism Using Computed Tomography Angiography, Echocardiography, and Artificial Intelligence.

OBJECTIVES: Early intervention in submassive pulmonary embolism (SMPE) has been shown to improve long-term cardiopulmonary outcomes compared to anticoagulation alone. SMPEs are diagnosed by documentation of right-to-left ventricular (RV/LV) ratio > 0.9, indicative of right heart strain (RHS), and is associated with adverse clinical outcomes. Although often used interchangeably to guide treatment o...

Interpretable machine learning using accumulated local effects to characterise predictors of subclinical leaflet thrombosis after self-expanding transcatheter aortic valve implantation.

OBJECTIVES: Subclinical leaflet thrombosis is an early form of bioprosthetic valve dysfunction after transcatheter aortic valve implantation. Predicti...

Deep learning electrocardiogram model for risk stratification of coronary revascularization need in the emergency department.

BACKGROUND AND AIMS: Identification of patients with acute coronary syndrome requiring coronary revascularization can be challenging due to inconclusi...

Identification and validation of cholesterol homeostasis and NK cell-associated predictive and therapeutic models for Coronary atherosclerotic heart disease patients with ST-elevated myocardial infarction: Insights from artificial intelligence and multi-omics.

OBJECTIVE: Dysregulation of Cholesterol homeostasis(CH) and NK cells proportion can increase risk of ST-Elevated Myocardial Infarction(STEMI) for Coro...

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis (MPN-SVT).

Machine Learning Prediction for 6-Month Outcomes in Traumatic Cerebral Venous Sinus Thrombosis.

This study developed and externally validated a multicenter machine learning framework to predict 6-month poor functional outcome (modified Rankin Sca...

Unveiling the Microvasculature: The Index of Microcirculatory Resistance and Its Expanding Role in Cardiovascular Care.

Index of microcirculatory resistance (IMR) is a cutting-edge, wire-based tool that advances the capability assessment of coronary microvascular functi...

Development of Models for Predicting the 7-Month Risk of Venous Thromboembolism and Clinically Relevant Bleeding in Ambulatory Patients with Cancer: Analysis from the AVERT Trial.

BACKGROUND: Patients with cancer are at elevated risk of venous thromboembolism (VTE). While primary thromboprophylaxis reduces VTE incidence, it also...

Consistency analysis of AI cell recognition results between non anticoagulant and anticoagulant marrow smear after Wright's staining.

The integration of artificial intelligence with bone marrow cytology represents a significant trend in the application of AI image recognition technol...

Machine learning-based model for predicting recanalization in isolated distal deep vein thrombosis and analysis of predictors.

BACKGROUND: Isolated distal deep vein thrombosis (IDDVT) is common, yet tools for predicting poor recanalization remain limited. We aimed to develop a...

Thrombosis at the crossroads of inflammation, coagulation, and vascular biology: Recent advances and emerging therapeutic strategies.

Thrombosis is a multifaceted pathological process involving intravascular clot formation that drives a range of cardiovascular and cerebrovascular dis...

Machine learning in bleeding risk assessment for low-molecular-weight heparin or fondaparinux: a predictive model study.

BACKGROUD:  No universally accepted model exists for predicting bleeding risk in patients receiving low-molecular-weight heparin or fondaparinux. OBJE...

A large-scale 12-lead electrocardiogram dataset for acute coronary syndrome prediction containing 19,955 ECGs.

Acute coronary syndrome(ACS) is a common cardiovascular disease and a severe type of coronary heart disease. Electrocardiograms(ECGs) are the initial ...

An integrated microneedle device for skin interstitial fluid multiplexed detection and deep learning-based analysis of acute myocardial infarction biomarkers.

Acute myocardial infarction (AMI) remains difficult to diagnose rapidly outside hospital settings because current evaluation still relies mainly on el...

Prehospital Risk Stratification Using Unsupervised Machine Learning in STEMI.

BACKGROUND: ST-elevation myocardial infarction (STEMI) exhibits substantial clinical heterogeneity complicating prehospital risk stratification. Tradi...

Evaluating Biomedical Feature Fusion on Machine Learning's Predictability and Interpretability of COVID-19 Severity Types: Model Development, Interpretation, and Validation.

BACKGROUND: Accurately differentiating severe from nonsevere COVID-19 clinical types is critical for the health care system to optimize workflow. Curr...

[Machine learning models to predict intestinal necrosis in acute mesenteric venous thrombosis].

Objective: To develop and validate machine learning-based models for predicting the risk of transmural irreversible intestinal necrosis (ITIN) in pati...

Decoding Kounis syndrome with artificial intelligence: from diagnosis to precision medicine.

Kounis syndrome-acute coronary events triggered by allergic or hypersensitivity reactions-remains underrecognized across emergency and cardiology sett...

External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.

An ECG-based artificial intelligence (AI) model was previously developed to generate ten digital biomarkers for emergency and cardiac assessment and i...