AIMC Topic: Percutaneous Coronary Intervention

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Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study.

Computational and mathematical methods in medicine Jul 5, 2021
Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricem...
Hyperuricemia Heart Disease Risk Factors Female Aged Male Middle Aged China Computational Biology Logistic Models Area Under Curve ST Elevation Myocardial Infarction Percutaneous Coronary Intervention Humans Machine Learning Algorithms Prognosis Retrospective Studies
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Risk stratification of ST-segment elevation myocardial infarction (STEMI) patients using machine learning based on lipid profiles.

Lipids in health and disease May 6, 2021
BACKGROUND: Numerous studies have revealed the relationship between lipid expression and increased cardiovascular risk in ST-segment elevation myocardial infarction (STEMI) patients. Nevertheless, few investigations have focused on the risk stratific...
Middle Aged Risk Assessment Unsupervised Machine Learning Principal Component Analysis ST Elevation Myocardial Infarction Humans Prognosis Female Aged Male Patient Readmission Cholesterol, HDL Triglycerides Kaplan-Meier Estimate Apolipoprotein B-100 Apolipoprotein A-I Lipoprotein(a) Percutaneous Coronary Intervention Cholesterol, LDL Lipid Metabolism Patient Selection
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Is Deep Learning Coming to Guide Your Coronary Intervention?

JACC. Cardiovascular interventions Apr 14, 2021
Humans Deep Learning Treatment Outcome Coronary Angiography Percutaneous Coronary Intervention
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Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 8, 2021
Recurrent angina (RA) after percutaneous coronary intervention (PCI) has few known risk factors, hampering the identification of high-risk populations. In this multicenter study, plasma samples are collected from patients with stable angina after PCI...
Metabolome Percutaneous Coronary Intervention Tandem Mass Spectrometry Risk Factors Angina, Stable Male Humans Recurrence Middle Aged Machine Learning Risk Assessment Prospective Studies Chromatography, Liquid Biomarkers Female
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Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.

BMC cardiovascular disorders Jan 18, 2021
BACKGROUND: Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of f...
Retrospective Studies Female Risk Factors Aged Male Humans Machine Learning Decision Support Techniques Coronary Artery Disease Iran Stents Cerebrovascular Disorders Heart Diseases Proportional Hazards Models Middle Aged Risk Assessment Time Factors Treatment Outcome Percutaneous Coronary Intervention
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Artificial neural network-based prediction of prolonged length of stay and need for post-acute care in acute coronary syndrome patients undergoing percutaneous coronary intervention.

European journal of clinical investigation Nov 29, 2020
BACKGROUND: Prolonged length of stay (LOS) and post-acute care after percutaneous coronary intervention (PCI) is common and costly. Risk models for predicting prolonged LOS and post-acute care have limited accuracy. Our goal was to develop and valida...
Transitional Care Skilled Nursing Facilities Subacute Care Hospices Hospitals, Rehabilitation Male Middle Aged Risk Assessment Length of Stay Humans Non-ST Elevated Myocardial Infarction Neural Networks, Computer Acute Coronary Syndrome Female ST Elevation Myocardial Infarction Nursing Homes Percutaneous Coronary Intervention Angina, Unstable Aged Patient Discharge
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A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling.

Journal of healthcare engineering Jul 30, 2020
Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with tra...
Ventricular Remodeling Middle Aged Ventricular Dysfunction, Left ROC Curve Imaging, Three-Dimensional Models, Statistical Aged Area Under Curve Predictive Value of Tests Adult Humans Coronary Angiography Sensitivity and Specificity Machine Learning Percutaneous Coronary Intervention Male Female Myocardial Infarction Echocardiography Reproducibility of Results Heart Ventricles Risk Factors
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Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

European radiology Mar 12, 2020
OBJECTIVE: To determine the potential impact of on-site CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of coronary CT angiography (CCTA) in patients with obstructive coronary artery disease (CAD) on CCTA.
Retrospective Studies Tomography, X-Ray Computed Female Aged Male Middle Aged Hemodynamics Cohort Studies Coronary Angiography Percutaneous Coronary Intervention Humans Machine Learning Fractional Flow Reserve, Myocardial Coronary Artery Disease Disease Management Cardiac Catheterization Computed Tomography Angiography Coronary Stenosis
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Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.

The Canadian journal of cardiology Feb 7, 2020
BACKGROUND: Machine learning (ML) has arrived in medicine to deliver individually adapted medical care. This study sought to use ML to discriminate stent restenosis (SR) compared with existing predictive scores of SR. To develop an easily applicable ...
Long Term Adverse Effects Drug-Eluting Stents Demography Humans Male Machine Learning Middle Aged Risk Assessment Coronary Angiography Percutaneous Coronary Intervention Coronary Restenosis Coronary Artery Disease Spain Prognosis Female Risk Factors
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Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Medical image analysis Jan 11, 2020
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using n...
Radiographic Image Interpretation, Computer-Assisted Fluoroscopy Percutaneous Coronary Intervention Motion Pattern Recognition, Automated Humans Deep Learning Bayes Theorem Radiography, Interventional Respiratory Mechanics Respiratory-Gated Imaging Techniques
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