Understanding the predictive value and methods of risk assessment based on coronary computed tomographic angiography in populations with coronary artery disease: a review

Li, Yiming; Jia, Kaiyu; Jia, Yixin; Yang, Yong; Yao, Yijun; Chen, Mao; Peng, Yong

doi:10.1093/pcmedi/pbab018

Cited by 2 publications

(2 citation statements)

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“…Intact fibrous cap ACS patients exhibit a unique inflammatory response and a lower MACE risk compared to rupture of the fibrous cap ACS patients [33]. By conducting a comprehensive evaluation based on CCTA, considering multiple aspects such as stenosis degree, plaque characteristics, and functional reserve, clinicians can obtain vital information for accurate prognosis stratification of patients [34]. Leveraging radiomics and machine learning techniques, CCTA images can be objectively and mathematically evaluated, providing enhanced precision analysis.…”

Section: Discussionmentioning

confidence: 99%

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Performance of the Risk Scores for Predicting In-Hospital Mortality in Patients with Acute Coronary Syndrome in a Chinese Cohort

Bai,

Li,

Yang

et al. 2023

Rev. Cardiovasc. Med.

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Background:The prognosis of patients with acute coronary syndrome (ACS) varies greatly, and risk assessment models can help clinicians to identify and manage high-risk patients. While the Global Registry of Acute Coronary Events (GRACE) model is widely used, the clinical pathways for acute coronary syndromes (CPACS), which was constructed based on the Chinese population, and acute coronary treatment and intervention outcomes network (ACTION) have not yet been validated in the Chinese population. Methods: Patients with ACS who underwent coronary angiography or percutaneous coronary intervention from 2011 to 2020, were retrospectively recruited and the appropriate corresponding clinical indicators was obtained. The primary endpoint was in-hospital mortality. The performance of the GRACE, GRACE 2.0, ACTION, thrombolysis in myocardial infarction (TIMI) and CPACS risk models was evaluated and compared. Results: A total of 19,237 patients with ACS were included. Overall, in-hospital mortality was 2.2%. ACTION showed the highest accuracy in predicting discriminated risk (c-index 0.945, 95% confidence interval [CI] 0.922-0.955), but the calibration was not satisfactory. GRACE and GRACE 2.0 did not significantly differ in discrimination (p = 0.1480). GRACE showed the most accurate calibration in all patients and in the subgroup analysis of all models. CPACS (c-index 0.841, 95% CI 0.821-0.861) and TIMI (c-index 0.811, 95% CI 0.787-0.835) did not outperform (c-index 0.926, 95% CI 0.911-0.940). Conclusions: In contemporary Chinese ACS patients, the ACTION risk model's calibration is not satisfactory, although outperformed the gold standard GRACE model in predicting hospital mortality. The CPACS model developed for Chinese patients did not show better predictive performance than the GRACE model.

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Section: Discussionmentioning

confidence: 99%

“…Leveraging radiomics and machine learning techniques, CCTA images can be objectively and mathematically evaluated, providing enhanced precision analysis. In the era of big data analysis and artificial intelligence, CCTA is poised to perform multi-dimensional risk stratification of patients with coronary heart disease [ 34 ].…”

Section: Discussionmentioning

confidence: 99%