首页|Beijing Anzhen Hospital of Capital Medical University Reports Findings in Machine Learning (Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive ...)
Beijing Anzhen Hospital of Capital Medical University Reports Findings in Machine Learning (Machine learning-based coronary artery calcium score predicted from clinical variables as a prognostic indicator in patients referred for invasive ...)
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New research on Machine Learning is the subject of a report. According to news reporting out of Beijing, People’s Republic of China, by NewsRx editors, research stated, “Utilising readily available clinical variables, we aimed to develop and validate a novel machine learning (ML) model to predict severe coronary calcification, and further assessed its prognostic significance. This retrospective study enrolled patients who underwent coronary CT angiography and subsequent invasive coronary angiography.” Our news journalists obtained a quote from the research from the Beijing Anzhen Hospital of Capital Medical University, “Multiple ML algorithms were used to train the models for predicting severe coronary calcification (cardiac CT-measured coronary artery calcium [CT-CAC] score 400). The ML-based CAC (MLCAC) score derived from the ML predictive probability was stratified into quartiles for prognostic analysis. The primary endpoint was a composite of all-cause death, nonfatal myocardial infarction, or nonfatal stroke. Overall, 5785 patients were divided into training (80%) and test sets (20%). For clinical practicability, we selected the nine-feature support vector machine model with good and satisfactory performance regarding both discrimination and calibration based on five repetitions of the 10-fold cross-validation in the training set (mean AUC = 0.715, Brier score = 0.202), and based on the test in the test set (AUC = 0.753, Brier score = 0.191). In the test set cohort (n = 1137), the primary endpoint was observed in 50 (4.4%) patients during a median 2.8 years’ follow-up. The ML-CAC system was significantly associated with an increased risk of the primary endpoint (adjusted hazard ratio for trend 2.26, 95% CI 1.35-3.79, p = 0.002). There was no significant difference in the prognostic value between the ML-CAC and CT-CAC systems (C-index, 0.67 vs. 0.69; p = 0.618). ML-CAC score predicted from clinical variables can serve as a novel prognostic indicator in patients referred for invasive coronary angiography.”
BeijingPeople’s Republic of ChinaAsiaAngiographyCardiologyCardiovascular Diagnostic TechniquesCoronary ArteryCyborgsEmerging TechnologiesHealth and MedicineMachine Learning
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