Complete Revascularization and Angina-Related Health Status in the ISCHEMIA Trial

Mavromatis, Kreton; Jones, Philip G.; Ali, Ziad A.; Stone, Gregg W.; Rhodes, Goodrich B.; Bangalore, Sripal; O’Brien, Sean M.; Généreux, Philippe; Horst, Jennifer; Dressler, Ovidiu; Goodman, Shaun G.; Alexander, Karen P.; Mathew, Anoop; Chen, Jiyan; Bhargava, Balram; Uxa, Amar; Boden, William E.; Mark, Daniel B.; Reynolds, Harmony R.; Maron, David J.; Hochman, Judith S.; Spertus, John A.

doi:10.1016/j.jacc.2023.05.025

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…The risk scoring model was presented in a colorful nomogram. Frontiers in Pharmacology frontiersin.org complete revascularization achievement in patients with chronic coronary artery disease (Mavromatis et al, 2023). These robust findings provide substantial support for our own results.…”

Section: Figuresupporting

confidence: 84%

“…It is frequently employed as an outcome evaluation tool for assessing treatment effectiveness ( Ford et al, 2020 ). Notably, researchers have discovered a close association between Anginal stability score in the SAQ and post-coronary artery bypass grafting angina occurrence ( Hattler et al, 2019 ), as well as its correlation with complete revascularization achievement in patients with chronic coronary artery disease ( Mavromatis et al, 2023 ). These robust findings provide substantial support for our own results.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients

Wang,

Sun,

et al. 2024

Front. Pharmacol.

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Objective: To develop a risk score model for the occurrence of composite cardiovascular events (CVE) in patients with stable angina pectoris (SA) combined with coronary heart disease (CHD) by comparing the modeling effects of various machine learning (ML) algorithms.Methods: In this prospective study, 690 patients with SA combined with CHD attending the Department of Integrative Cardiology, China-Japan Friendship Hospital, from October 2020 to October 2021 were included. The data set was randomly divided into a training group and a testing group in a 7:3 ratio in the per-protocol set (PPS). Model variables were screened using the least absolute shrinkage selection operator (LASSO) regression, univariate analysis, and multifactor logistic regression. Then, nine ML algorithms are integrated to build the model and compare the model effects. Individualized risk assessment was performed using the SHapley Additive exPlanation (SHAP) and nomograms, respectively. The model discrimination was evaluated by receiver operating characteristic curve (ROC), the calibration ability of the model was evaluated by calibration plot, and the clinical applicability of the model was evaluated by decision curve analysis (DCA). This study was approved by the Clinical Research Ethics Committee of China-Japan Friendship Hospital (2020-114-K73).Results: 690 patients were eligible to finish the complete follow-up in the PPS. After LASSO screening and multifactorial logistic regression analysis, physical activity level, taking antiplatelets, Traditional Chinese medicine treatment, Gensini score, Seattle Angina Questionnaire (SAQ)-exercise capacity score, and SAQ-anginal stability score were found to be predictors of the occurrence of CVE. The above predictors are modeled, and a comprehensive comparison of the modeling effectiveness of multiple ML algorithms is performed. The results show that the Light Gradient Boosting Machine (LightGBM) model is the best model, with an area under the curve (AUC) of 0.95 (95% CI = 0.91–1.00) for the test set, Accuracy: 0.90, Sensitivity: 0.87, and Specificity: 0.96. Interpretation of the model using SHAP highlighted the Gensini score as the most important predictor. Based on the multifactorial logistic regression modeling, a nomogram, and online calculators have been developed for clinical applications.Conclusion: We developed the LightGBM optimization model and the multifactor logistic regression model, respectively. The model is interpreted using SHAP and nomogram. This provides an option for early prediction of CVE in patients with SA combined with CHD.

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Section: Figuresupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients

Wang,

Sun,

et al. 2024

Front. Pharmacol.

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“…These results parallel and offer a mechanistic explanation for only 43% and 58% of revascularized patients achieving anatomic or functional complete revascularization respectively reported for the ISCHEMIA trial. 17 …”

Section: Discussionmentioning

confidence: 99%

Coronary flow capacity and survival prediction after revascularization: physiological basis and clinical implications

Gould,

Johnson,

Roby

et al. 2023

European Heart Journal

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Background and Aims Coronary flow capacity (CFC) is associated with an observed 10-year survival probability for individual patients before and after actual revascularization for comparison to virtual hypothetical ideal complete revascularization. Methods Stress myocardial perfusion (mL/min/g) and coronary flow reserve (CFR) per pixel were quantified in 6979 coronary artery disease (CAD) subjects using Rb-82 positron emission tomography (PET) for CFC maps of artery-specific size-severity abnormalities expressed as percent left ventricle with prospective follow-up to define survival probability per-decade as fraction of 1.0. Results Severely reduced CFC in 6979 subjects predicted low survival probability that improved by 42% after revascularization compared with no revascularization for comparable severity (P = .0015). For 283 pre-and-post-procedure PET pairs, severely reduced regional CFC-associated survival probability improved heterogeneously after revascularization (P < .001), more so after bypass surgery than percutaneous coronary interventions (P < .001) but normalized in only 5.7%; non-severe baseline CFC or survival probability did not improve compared with severe CFC (P = .00001). Observed CFC-associated survival probability after actual revascularization was lower than virtual ideal hypothetical complete post-revascularization survival probability due to residual CAD or failed revascularization (P < .001) unrelated to gender or microvascular dysfunction. Severely reduced CFC in 2552 post-revascularization subjects associated with low survival probability also improved after repeat revascularization compared with no repeat procedures (P = .025). Conclusions Severely reduced CFC and associated observed survival probability improved after first and repeat revascularization compared with no revascularization for comparable CFC severity. Non-severe CFC showed no benefit. Discordance between observed actual and virtual hypothetical post-revascularization survival probability revealed residual CAD or failed revascularization.

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“…All patients achieved anatomical complete revascularization ( 20 ), implying that coronary angiography suggests at least one posture, and revascularization was performed on blood vessels with diameters of >1.5 mm and stenosis of ≥50%. This is because complete revascularization can significantly reduce the frequency of angina pectoris and the incidence of adverse events in patients with coronary heart disease, and improve the quality of life ( 21 , 22 ). The instantaneous blood flow measurement technique was used to assess the quality of grafts anastomosis.…”

Section: Methodsmentioning

confidence: 99%

Prediction of left ventricular ejection fraction improvement in patients with ischemic cardiomyopathy after coronary artery bypass grafting based on cardiac magnetic resonance

Zhang,

Fu,

Dai

et al. 2023

Cardiovasc Diagn Ther

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Background To investigate the risk factors of left ventricular ejection fraction (LVEF) improvement in patients with ischemic cardiomyopathy (ICM) after coronary artery bypass grafting (CABG), and to construct a model that predicts LVEF improvement. Methods A retrospective analysis was performed on 106 ICM patients who received CABG and underwent cardiac magnetic resonance (CMR) at Beijing Anzhen Hospital, Capital Medical University from January 2017 to June 2022. Patients were divided into two groups with improved LVEF and no improved LVEF based on the results of postoperative 6-month transthoracic echocardiography. To analyze the risk factors affecting the LVEF non-improvement after CABG and establish a prediction model. Results There was LVEF non-improvement in 30.2% (32/106) of patients. Multivariate analysis showed that the number of transmural scar segments and left ventricular end-systolic volume index (LVESVI) were independent risk factors in LVEF non-improvement after CABG [odds ratio (OR) =2.398, 95% confidence interval (CI): 1.607–3.579, P<0.001; OR =1.036, 95% CI: 1.009–1.063, P=0.008]. The model is built and internally verified. ROC showed that the area under the curve (AUC) was 0.866 (95% CI: 0.792–0.940), calibration curve showed that the probability predicted by the model matched well with the clinical results, and decision curve analysis (DCA) showed that the model had good clinical applicability. During the mean follow-up time of 1.5 years, the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) in the LVEF non-improvement group was higher (5.4% vs. 25.0%, P=0.009), and the NYHA grading was higher (P=0.016), when compared to the LVEF improvement group. Conclusions The prediction model based on the number of transmural scar segments and LVESVI has good diagnostic efficacy. Our findings help to identify patients with improved LVEF and thus guide the selection of clinical treatment strategies.

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Complete Revascularization and Angina-Related Health Status in the ISCHEMIA Trial

Cited by 10 publications

References 21 publications

Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients

Machine learning-based prediction of composite risk of cardiovascular events in patients with stable angina pectoris combined with coronary heart disease: development and validation of a clinical prediction model for Chinese patients

Coronary flow capacity and survival prediction after revascularization: physiological basis and clinical implications

Prediction of left ventricular ejection fraction improvement in patients with ischemic cardiomyopathy after coronary artery bypass grafting based on cardiac magnetic resonance

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