AIMC Topic: Heart Failure
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The diagnostic value investigation of programmed cell death genes in heart failure.
BMC cardiovascular disorders
Nov 22, 2024
BACKGROUND: We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF).
Transcriptome
Case-Control Studies
Protein Interaction Maps
Gene Regulatory Networks
Nomograms
Humans
Machine Learning
Reproducibility of Results
Gene Expression Profiling
Heart Failure
Plasminogen Activator Inhibitor 1
Databases, Genetic
Gene Expression Regulation
Apoptosis Regulatory Proteins
Apoptosis
Predictive Value of Tests
Genetic Markers
Machine Learning-Based Prediction of Death and Hospitalization in Patients With Implantable Cardioverter Defibrillators.
Journal of the American College of Cardiology
Nov 20, 2024
BACKGROUND: Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional stati...
Machine learning-based prediction of elevated N terminal pro brain natriuretic peptide among US general population.
ESC heart failure
Nov 19, 2024
AIMS: Natriuretic peptide-based pre-heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre...
Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial.
European journal of clinical investigation
Nov 18, 2024
BACKGROUND: The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging. Our aim was to evaluate the performance of machine learning (ML) in identif...
Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical Trials.
Circulation. Heart failure
Nov 16, 2024
BACKGROUND: Medical record review by a physician clinical events committee is the gold standard for identifying cardiovascular outcomes in clinical trials, but is labor-intensive and poorly reproducible. Automated outcome adjudication by artificial i...
Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.
Cardiovascular diabetology
Nov 15, 2024
BACKGROUND: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop ...
Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission.
International journal of medical informatics
Nov 14, 2024
BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction of readmission risk is essential for optimizing healthcare resources and enhancing patient outcomes...
Predictive modeling of preoperative acute heart failure in older adults with hypertension: a dual perspective of SHAP values and interaction analysis.
BMC medical informatics and decision making
Nov 6, 2024
BACKGROUND: In older adults with hypertension, hip fractures accompanied by preoperative acute heart failure significantly elevate surgical risks and adverse outcomes, necessitating timely identification and management to improve patient outcomes.
Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure.
JAMA network open
Nov 4, 2024
IMPORTANCE: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials.
AI derived ECG global longitudinal strain compared to echocardiographic measurements.
Scientific reports
Nov 2, 2024
Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial intelligence (AI)-generated electrocardiography score for LVGLS estimation (ECG-GLS score) to diagn...
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