ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2020
Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- Predicting cardiac resynchronization therapy response: development and validation of a single photon emission computed tomography-based nomogram.Quantitative imaging in medicine and surgery · 2025Article
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- Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities.American heart journal plus : cardiology research and practice · 2024Article
- Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis.EJNMMI research · 2024Review
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice.Healthcare (Basel, Switzerland) · 2024Review
- Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Scientific reports · 2024Article
- Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging.European heart journal. Imaging methods and practice · 2024Article
- Hybridizing machine learning in survival analysis of cardiac PET/CT imaging.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.Reviews in cardiovascular medicine · 2023Review
- A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support.European journal of nuclear medicine and molecular imaging · 2023Article
- Preparing for the Artificial Intelligence Revolution in Nuclear Cardiology.Nuclear medicine and molecular imaging · 2023Review
- Development and validation of ischemia risk scores.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Developing a framework for evaluating and comparing risk models.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- A highly predictive cardiac positron emission tomography (PET) risk score for 90-day and one-year major adverse cardiac events and revascularization.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Machine learning to predict abnormal myocardial perfusion from pre-test features.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022Article
- The effectiveness of post-professional physical therapist training in the treatment of chronic low back pain using a propensity score approach with machine learning.Musculoskeletal care · 2022Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundA significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR).
methods1,234 patients referred to Nitrogen-13 ammonia PET were analyzed. Demographic (4), clinical (8), and functional variables (9) were retrieved and input into a cross-validated ML workflow consisting of feature selection and modeling. Two PET-defined outcome variables were operationalized: (1) any myocardial ischemia (regional MPR < 2.0) and (2) an elevated risk of MACE (global MPR < 2.0). ROC curves were used to evaluate ML performance.
results16 features were included for boosted ensemble ML. ML achieved an AUC of 0.72 and 0.71 in identifying patients with myocardial ischemia and with an elevated risk of MACE, respectively. ML performance was superior to logistic regression when the latter used the ESC guidelines risk models variables for both PET-defined labels (P < .001 and P = .01, respectively).
conclusionsML is feasible and applicable in the evaluation and utilization of simple and accessible predictors for the identification of patients who will present myocardial ischemia and an elevated risk of MACE in quantitative PET imaging.
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