High pericoronary adipose tissue attenuation on computed tomography angiography predicts cardiovascular events in patients with type 2 diabetes mellitus: post-hoc analysis from a prospective cohort study

Ichikawa, Keishi; Miyoshi, Toru; Osawa, Kazuhiro; Nakashima, Mitsuyoshi; Miki, Takashi; Nishihara, Takahiro; Toda, H.; Yoshida, Midori; Ito, Hiroshi

doi:10.1186/s12933-022-01478-9

Cited by 31 publications

(24 citation statements)

References 41 publications

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“…This study is the first to combine multiple factors to diagnose the occurrence of CAD in patients with T2DM. While previous studies have focused on the future occurrence of late cardiovascular events in patients with T2DM [ 25 – 27 , 45 ], we have focused on CAD with the aim of detecting the onset of CAD before an irreversible endpoint event occurs in patients with T2DM. We have developed an optimal model nomogram to help patients proactively monitor the progression of their disease and the probability of CAD based on their condition and examination findings to proactively prevent and treat CAD in a timely manner.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, an increasing number of studies have examined patients with T2DM, and a higher LAD-PCATA [ 25 ], plaque volume and mild calcification [ 26 ], and increased epicardial adipose tissue volume [ 27 ] have been found to improve the prediction of future cardiovascular events and cardiogenic death in patients with T2DM. A higher CACS has also been shown to be an independent predictor of CAD in patients with T2DM [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study

Dong

Zhu

et al. 2023

Cardiovasc Diabetol

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Background Patients with type 2 diabetes mellitus (T2DM) are highly susceptible to cardiovascular disease, and coronary artery disease (CAD) is their leading cause of death. We aimed to assess whether computed tomography (CT) based imaging parameters and radiomic features of pericoronary adipose tissue (PCAT) can improve the diagnostic efficacy of whether patients with T2DM have developed CAD. Methods We retrospectively recruited 229 patients with T2DM but no CAD history (146 were diagnosed with CAD at this visit and 83 were not). We collected clinical information and extracted imaging manifestations from CT images and 93 radiomic features of PCAT from all patients. All patients were randomly divided into training and test groups at a ratio of 7:3. Four models were constructed, encapsulating clinical factors (Model 1), clinical factors and imaging indices (Model 2), clinical factors and Radscore (Model 3), and all together (Model 4), to identify patients with CAD. Receiver operating characteristic curves and decision curve analysis were plotted to evaluate the model performance and pairwise model comparisons were performed via the DeLong test to demonstrate the additive value of different factors. Results In the test set, the areas under the curve (AUCs) of Model 2 and Model 4 were 0.930 and 0.929, respectively, with higher recognition effectiveness compared to the other two models (each p < 0.001). Of these models, Model 2 had higher diagnostic efficacy for CAD than Model 1 (p < 0.001, 95% CI [0.129–0.350]). However, Model 4 did not improve the effectiveness of the identification of CAD compared to Model 2 (p = 0.776); similarly, the AUC did not significantly differ between Model 3 (AUC = 0.693) and Model 1 (AUC = 0.691, p = 0.382). Overall, Model 2 was rated better for the diagnosis of CAD in patients with T2DM. Conclusions A comprehensive diagnostic model combining patient clinical risk factors with CT-based imaging parameters has superior efficacy in diagnosing the occurrence of CAD in patients with T2DM.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis of coronary artery disease in patients with type 2 diabetes mellitus based on computed tomography and pericoronary adipose tissue radiomics: a retrospective cross-sectional study

Dong

Zhu

et al. 2023

Cardiovasc Diabetol

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“…When FAI was added to the CTA stenosis estimation the AUC of such model was comparable to the one achieved with the invasive assessment of stenosis severity. The value of PCAT has been now also assessed as a predictive tool in patients with diabetes ( 57 ). Adding PCAT along with findings of CTA improved the model fit for predicting cardiovascular events in type 2 diabetes patients and helped to identify patients at a higher risk independently of the plaque characteristics.…”

Section: The Role Of Coronary Ct In the Management Of Coronary Artery...mentioning

confidence: 99%

Multi-modality cardiac imaging in the management of diabetic heart disease

Wamil

Gonçalves²,

Rutherford

et al. 2022

Front. Cardiovasc. Med.

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Diabetic heart disease is a major healthcare problem. Patients with diabetes show an excess of death from cardiovascular causes, twice as high as the general population and those with diabetes type 1 and longer duration of the disease present with more severe cardiovascular complications. Premature coronary artery disease and heart failure are leading causes of morbidity and reduced life expectancy. Multimodality cardiac imaging, including echocardiography, cardiac computed tomography, nuclear medicine, and cardiac magnetic resonance play crucial role in the diagnosis and management of different pathologies included in the definition of diabetic heart disease. In this review we summarise the utility of multi-modality cardiac imaging in characterising ischaemic and non-ischaemic causes of diabetic heart disease and give an overview of the current clinical practice. We also describe emerging imaging techniques enabling early detection of coronary artery inflammation and the non-invasive characterisation of the atherosclerotic plaque disease. Furthermore, we discuss the role of MRI-derived techniques in studying altered myocardial metabolism linking diabetes with the development of diabetic cardiomyopathy. Finally, we discuss recent data regarding the use of artificial intelligence applied to large imaging databases and how those efforts can be utilised in the future in screening of patients with diabetes for early signs of disease.

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“…A high PCAT attenuation also predicted CV events among 333 patients with type 2 diabetes undergoing clinically indicated CCTA (median follow-up: 4 years), 10 as well as future myocardial infarction among 1697 participants with suspected stable angina due to CAD undergoing CCTA (median follow-up: 4.7 years) in a post-hoc analysis of the multicenter Scottish Computed Tomography of the Heart (SCOT-HEART) trial. 11 These findings highlight the potential use of PCAT attenuation to re-classify CV risk, diagnose sub-clinical atherosclerosis, and identify high-risk patients, thus enabling the implementation of early prevention strategies.…”

mentioning

confidence: 96%

“…8 Similarly, pericoronary fat attenuation index (FAI) (assessed in 280 coronary vessels) was shown to predict lesion-specific ischemia, as did coronary stenosis severity (≥50% stenosis was considered obstructive) and atherosclerotic plaque characteristics (such as low-attenuation plaque and aggregate plaque volume). 9 A high PCAT attenuation also predicted CV events among 333 patients with type 2 diabetes undergoing clinically indicated CCTA (median follow-up: 4 years), 10 as well as future myocardial infarction among 1697 participants with suspected stable angina due to CAD undergoing CCTA (median follow-up: 4.7 years) in a post-hoc analysis of the multicenter Scottish Computed Tomography of the Heart (SCOT-HEART) trial. 11 These findings highlight the potential use of PCAT attenuation to re-classify CV risk, diagnose sub-clinical atherosclerosis, and identify high-risk patients, thus enabling the implementation of early prevention strategies.…”

mentioning

confidence: 99%