Coronary Computed Tomography Angiography Analysis of Calcium Content to Identify Non-culprit Vulnerable Plaques in Patients With Acute Coronary Syndrome

Risk factors and the CCTA application in patients with vulnerable coronary plaque in type 2 diabetes: a retrospective study

BACKGROUND

Diabetes is an independent risk factor for cardiovascular disease. The purpose of this study was to identify the risk factors for vulnerable coronary plaques (VCPs), which are associated with adverse cardiovascular events, and to determine the value of coronary CT angiography (CCTA) in patients with type 2 diabetes mellitus (T2DM) and VCPs.

METHODS

Ninety-eight T2DM patients who underwent CCTA and intravascular ultrasound (IVUS) were retrospectively included and analyzed. The patients were grouped and analyzed according to the presence or absence of VCPs.

RESULTS

Among the patients with T2DM, time in range [TIR {the percentage of time blood glucose levels were in the target range}] (OR = 0.93, 95% CI = 0.89-0.96; P < 0.001) and the high-density lipoprotein-cholesterol (HDL-C) concentration (OR = 0.24, 95% CI = 0.09-0.63; P = 0.04) were correlated with a lower risk of VCP, but the triglycerides (TG) concentration was correlated with a higher risk of VCP (OR = 1.79, 95% CI = 1.01-3.18; P = 0.045). The area under the receiver operator characteristic curve (AUC) of TIR, and HDL-C and TG concentrations were 0.76, 0.73, and 0.65, respectively. The combined predicted AUC of TIR, and HDL-C and TG concentrations was 0.83 (P < 0.05). The CCTA sensitivity, specificity, false-negative, and false-positive values for the diagnosis of VCP were 95.74%, 94.12%, 4.26%, and 5.88%, respectively. The identification of VCP by CCTA was positively correlated with IVUS (intraclass correlation coefficient [ICC] = 0.90).

CONCLUSIONS

The TIR and HDL-C concentration are related with lower risk of VCP and the TG concentration was related with higher risk of VCP in patients with T2DM. In clinical practice, TIR, HDL-C and TG need special attention in patients with T2DM. The ability of CCTA to identify VCP is highly related to IVUS findings.

Artificial intelligence evaluation of coronary computed tomography angiography for coronary stenosis classification and diagnosis

BACKGROUND

Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time-consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows.

METHODS

In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model.

RESULTS

The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient: .79 vs. .39 and .77 vs. .40, p < .0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index = .350; Z = 4.194; p < .001).

CONCLUSIONS

The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows.

Left atrioventricular coupling index assessed using cardiac CT as a prognostic marker of cardiovascular death

PURPOSE

The purpose of this study was to investigate the prognostic value of left atrioventricular coupling index (LACI) assessed by cardiac computed tomography (CT), to predict cardiovascular death in consecutive patients referred for cardiac CT with coronary analysis.

MATERIALS AND METHODS

Between 2010 and 2020, we conducted a single-centre study with all consecutive patients without known cardiovascular disease referred for cardiac CT. LACI was defined as the ratio of left atrial to left ventricle end-diastolic volumes. The primary outcome was cardiovascular death. Cox regressions were used to evaluate the association between LACI and primary outcome after adjustment for traditional risk factors and cardiac CT angiography findings.

RESULTS

In 1,444 patients (mean age, 70 ± 12 [standard deviation] years; 43% men), 67 (4.3%) patients experienced cardiovascular death after a median follow-up of 6.8 (Q1, Q3: 5.9, 9.1) years. After adjustment, LACI was positively associated with the occurrence of cardiovascular death (adjusted hazard ratio [HR], 1.07 [95% CI: 1.05-1.09] per 1% increment; P < 0.001), and all-cause death (adjusted HR, 1.05 [95% CI: 1.03-1.07] per 1% increment; P <0.001). After adjustment, a LACI ≥ 25% showed the best improvement in model discrimination and reclassification for predicting cardiovascular death above traditional risk factors and cardiac CT findings (C-statistic improvement: 0.27; Nnet reclassification improvement = 0.826; Integrative discrimination index =0.209, all P < 0.001; likelihood-ratio-test, P < 0.001).

CONCLUSION

LACI measured by cardiac CT is independently associated with cardiovascular death and all-cause death in patients without known cardiovascular disease referred for cardiac CT, with an incremental prognostic value over traditional risk factors and cardiac CT findings.

Additional prognostic value of stress cardiovascular magnetic resonance for cardiovascular risk stratification after a cryptogenic ischemic stroke

Background

One-third of ischemic strokes are “cryptogenic” without clearly identified etiology. Although coronary artery disease (CAD) is the main cause of death after stroke, the interest in CAD screening in patients with cryptogenic stroke is still debated.

Aim

The aim of the study was to assess the incremental prognostic value of stress cardiovascular magnetic resonance (CMR) beyond traditional risk factors for predicting cardiovascular events in patients with a prior cryptogenic ischemic stroke.

Materials and methods

Between 2008 and 2021, consecutive patients with prior cryptogenic strokes referred for stress CMR were included and followed for the occurrence of major adverse cardiovascular events (MACEs), defined by cardiovascular death or non-fatal myocardial infarction (MI). Univariable and multivariable Cox regressions were performed to determine the prognostic value of unrecognized MI and silent ischemia.

Results

Of 542 patients (55.2% male, mean age 71.4 ± 8.8 years) who completed the follow-up (median 5.9 years), 66 (12.2%) experienced MACE. Silent ischemia and unrecognized MI were detected in 18 and 17% of patients, respectively. Using Kaplan–Meier analysis, silent ischemia and unrecognized MI were associated with the occurrence of MACE [hazard ratio, HR: 8.43 (95% CI: 5.11–13.9); HR: 7.87 (95% CI: 4.80–12.9), respectively, p < 0.001]. In multivariable analysis, silent ischemia and unrecognized MI were independent predictors of MACE [HR: 8.08 (95% CI: 4.21–15.5); HR: 6.65 (95% CI: 3.49–12.7), respectively, p < 0.001]. After adjustment, stress CMR findings showed the best improvement in model discrimination and reclassification above traditional risk factors (C-statistic improvement: 0.13; NRI = 0.428; IDI = 0.048).

Conclusion

In patients with prior cryptogenic stroke, stress CMR findings have an incremental prognostic value to predict MACE over traditional risk factors.