Risk stratification of coronary artery disease using radionuclides. Current status of clinical practice

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Siemens Healthineers Introduction Coronary CT angiography (CCTA) plays an increasing role in the detection and risk stratification of patients with coronary artery disease (CAD). The Coronary Artery Disease – Reporting and Data System (CAD-RADS) allows for standardized classification of CCTA results and, thus, may improve patient management. Purpose Aim of this study was to assess the impact of CCTA in combination with CAD-RADS on patient management and to identify the impact of cardiovascular risk factors (CVRF) on CAD severity. Methods CCTA was performed on a third-generation dual-source CT scanner in patients, who were referred to a radiology centre by their attending physicians. In a total of 4801 patients, CVRF were derived from medical reports and anamnesis. Results The study population consisted of 4770 patients (62.0 (54.0-69.0) years, 2841 males) with CAD (CAD-RADS 1-5), while 31 patients showed no CAD and were excluded from further analyses. Age, male gender and the number of CVRF were associated with more severe CAD stages (all p < 0.001). 3040 patients (63.7 %) showed minimal or mild CAD requiring optimization of CVRF i.e. medical therapy but no further assessment at his time. A group of 266 patients (5.6 %) had a severe CAD defined as CAD-RADS 4B/5. In the multivariate regression analysis, age, male gender, history of smoking, diabetes mellitus and hyperlipidaemia were significant predictors for severe CAD, whereas arterial hypertension and family history of CAD did not reach significance. Of note, a subgroup of 28 patients (10.5 %) with a severe CAD (68.5 (65.5-70.0) years, 26 males, both p = n.s.) had no CVRF. Conclusions CCTA in combination with the CAD-RADS allowed for effective risk stratification of CAD patients. The majority of the patients showed non-obstructive CAD and, thus, could be treated conservatively without the need for further CAD assessment. CVRF out of arterial hypertension and family history had an impact on CAD severity reflected in higher CAD-RADs gradings. Of note, a relevant fraction of patients with CAD did not have any CVRF and, thus, may not be covered by risk stratification models. CAD-RADS n Age (years) Males (%) 1 1453 56.0 (50.0-62.0) 623 (42.9 %) 2 1587 62.0 (55.0-69.0) 918 (57.8 %) 3 1067 66.0 (59.0-71.0) 749 (70.2 %) 4A 397 66.0 (59.0-72.0) 317 (79.8 %) 4B 162 67.0 (61.0-74.0) 139 (85.8 %) 5 104 66.0 (58.5.0-77.0) 95 (91.3 %)

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Implications of cardiac markers in risk-stratification and management for COVID-19 patients

Critical Care ◽

10.1186/s13054-021-03555-z ◽

2021 ◽

Vol 25 (1) ◽

Author(s):

Pengping Li ◽

Wei Wu ◽

Tingting Zhang ◽

Ziyu Wang ◽

Jie Li ◽

...

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Risk Stratification ◽

Rna Sequencing ◽

High Mortality ◽

Admission Rate ◽

Serum Levels ◽

Receptor Expression ◽

Cardiac Markers ◽

Artery Disease

Abstract Background COVID-19 has resulted in high mortality worldwide. Information regarding cardiac markers for precise risk-stratification is limited. We aim to discover sensitive and reliable early-warning biomarkers for optimizing management and improving the prognosis of COVID-19 patients. Methods A total of 2954 consecutive COVID-19 patients who were receiving treatment from the Wuhan Huoshenshan Hospital in China from February 4 to April 10 were included in this retrospective cohort. Serum levels of cardiac markers were collected after admission. Coronary artery disease diagnosis and survival status were recorded. Single-cell RNA-sequencing and bulk RNA-sequencing from different cohorts of non-COVID-19 were performed to analyze SARS-CoV-2 receptor expression. Results Among 2954 COVID-19 patients in the analysis, the median age was 60 years (50–68 years), 1461 (49.5%) were female, and 1515 (51.3%) were severe/critical. Compared to mild/moderate (1439, 48.7%) patients, severe/critical patients showed significantly higher levels of cardiac markers within the first week after admission. In severe/critical COVID-19 patients, those with abnormal serum levels of BNP (42 [24.6%] vs 7 [1.1%]), hs-TNI (38 [48.1%] vs 6 [1.0%]), α- HBDH (55 [10.4%] vs 2 [0.2%]), CK-MB (45 [36.3%] vs 12 [0.9%]), and LDH (56 [12.5%] vs 1 [0.1%]) had a significantly higher mortality rate compared to patients with normal levels. The same trend was observed in the ICU admission rate. Severe/critical COVID-19 patients with pre-existing coronary artery disease (165/1,155 [10.9%]) had more cases of BNP (52 [46.5%] vs 119 [16.5%]), hs-TNI (24 [26.7%] vs 9.6 [%], α- HBDH (86 [55.5%] vs 443 [34.4%]), CK-MB (27 [17.4%] vs 97 [7.5%]), and LDH (65 [41.9%] vs 382 [29.7%]), when compared with those without coronary artery disease. There was enhanced SARS-CoV-2 receptor expression in coronary artery disease compared with healthy controls. From regression analysis, patients with five elevated cardiac markers were at a higher risk of death (hazards ratio 3.4 [95% CI 2.4–4.8]). Conclusions COVID-19 patients with pre-existing coronary artery disease represented a higher abnormal percentage of cardiac markers, accompanied by high mortality and ICU admission rate. BNP together with hs-TNI, α- HBDH, CK-MB and LDH act as a prognostic biomarker in COVID-19 patients with or without pre-existing coronary artery disease.

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Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications

Diagnostics ◽

10.3390/diagnostics11030551 ◽

2021 ◽

Vol 11 (3) ◽

pp. 551

Author(s):

Chris Boyd ◽

Greg Brown ◽

Timothy Kleinig ◽

Joseph Dawson ◽

Mark D. McDonnell ◽

...

Keyword(s):

Machine Learning ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Model Comparison ◽

Disease Risk ◽

Imaging Modalities ◽

Current Status ◽

Advantages And Disadvantages ◽

Body Regions ◽

Artery Disease

Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient imaging datasets, as well as a lack of transparency in specific methods, are obstacles to further development. This paper reviews the current status of quantitative vascular ML, identifying advantages and disadvantages common to all imaging modalities. Literature from the past 8 years was systematically collected from MEDLINE® and Scopus database searches in January 2021. Papers satisfying all search criteria, including a minimum of 50 patients, were further analysed and extracted of relevant data, for a total of 47 publications. Current ML image segmentation, disease risk prediction, and pathology quantitation methods have shown sensitivities and specificities over 70%, compared to expert manual analysis or invasive quantitation. Despite this, inconsistencies in methodology and the reporting of results have prevented inter-model comparison, impeding the identification of approaches with the greatest potential. The clinical potential of this technology has been well demonstrated in Computed Tomography of coronary artery disease, but remains practically limited in other modalities and body regions, particularly due to a lack of routine invasive reference measurements and patient datasets.

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