Neurological prognosis in surgically treated acute aortic dissection with brain computed tomography perfusion.

OBJECTIVES: The aim of this study was to explore the prognostic value of brain computed tomography perfusion (CTP) for postoperative new stroke in acute type A aortic dissection (ATAAD) patients. METHODS: Patients with ATAAD and suspected cerebral malperfusion who underwent brain CTP and surgical repair were retrospectively analysed. Brain perfusion was quantified mainly with the averaged cerebral blood flow. Significant clinical and imaging findings were identified through univariable and multivariable regression analysis. Furthermore, the added prognostic benefit of perfusion parameters was confirmed with the receiver operating characteristic curves in the entire cohort and subgroup analysis. RESULTS: The incidence of postoperative new stroke was 30.8% (44/143). The independent adjusted predictors of postoperative new stroke included an impaired averaged cerebral blood flow (ml/100 ml/min) (odds ratio: 0.889; P < 0.001), severe stenosis (odds ratio: 5.218; P = 0.011) or occlusion (odds ratio: 14.697; P = 0.048) of the true lumen in common carotid artery (CCA), hypotension on admission (odds ratio: 9.644; P = 0.016) and a longer surgery time (odds ratio: 1.593; P = 0.021). The area under the receiver operating characteristic curves significantly improved after adding perfusion parameters to clinical and computed tomography angiography characteristics (P = 0.048). This benefit was more pronounced in patients with severe stenosis or occlusion in CCA true lumen (P = 0.004). CONCLUSIONS: Brain CTP could be a useful prognostic tool for surgically treated ATAAD patients and especially beneficial in patients with severe stenosis or occlusion of the CCA true lumen.

Prognostic value of coronary CT angiography and CT myocardial perfusion imaging among patients with and without Diabetes.

OBJECTIVES: Whether stress CT myocardial perfusion imaging (CT-MPI) improves risk assessment in patients with diabetes mellitus (DM) remains unexplored. We aimed to evaluate the prognostic value of coronary CT angiography (CCTA) and stress CT-MPI in suspected coronary artery disease (CAD) patients with and without DM. METHODS: A total of 334 patients with suspected CAD who underwent CCTA and stress CT-MPI from May 2020 to July 2021 were retrospectively analyzed. The endpoint was major adverse cardiovascular events (MACEs). Multivariable Cox regression analysis was used to evaluate the risk factors for MACEs, including clinical risk factors, CCTA characteristics and CT-MPI characteristics. RESULTS: After a median follow-up of 21 months,15 patients of the DM group and 16 patients of the non-DM group experienced MACEs. Multivariate Cox stepwise regression analysis showed that abnormal perfusion myocardial segments ratio was associated with MACEs after adjusting for clinical risk factors and CCTA characteristics in all patients (HR:1.023, p < 0.001), DM group (HR:1.024, p = 0.008) and non-DM group (HR:1.028, p = 0.003). By adding CT-MPI characteristics to CCTA characteristics and clinical risk factors, the global chi-square for predicting MACEs increased from 62.24 to 78.84 in all patients (p < 0.001), from 19.18 to 27.30 in DM group (p = 0.004) and from 39.51 to 48.65 in non-DM group (p = 0.003); the increment of C-index in all patients, DM group and non-DM group were 0.018, 0.054 and 0.019, respectively. CONCLUSION: In all patients and those with and without DM, CT-MPI has incremental prognostic value over clinical risk factors alone or combined with CCTA characteristics in predicting MACEs.

Lack of Incremental Prognostic Value of Pericoronary Adipose Tissue Computed Tomography Attenuation Beyond Coronary Artery Disease Reporting and Data System for Major Adverse Cardiovascular Events in Patients With Acute Chest Pain.

BACKGROUND: Pericoronary adipose tissue (PCAT) and Coronary Artery Disease Reporting and Data System (CAD-RADS) category had prognostic values for major adverse cardiovascular events (MACEs). However, little is known about the difference between CAD-RADS and PCAT computed tomography (CT) attenuation for predicting MACEs. This study was to compare the prognostic value of PCAT and CAD-RADS for MACEs in patients with acute chest pain. METHODS: Between January 2010 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary computed tomography angiography were enrolled in this retrospective study. MACEs included unstable angina requiring hospitalization, coronary revascularization, nonfatal myocardial infarction, and all-cause death. Patients' clinical characteristics, CAD-RADS, and PCAT CT attenuation were used to evaluate risk factors of MACEs using multivariable Cox regression analysis. RESULTS: A total of 1313 patients were evaluated (mean age, 57.13±12.57 years; 782 men). During a median follow-up of 38 months, 142 of the 1313 patients (10.81%) experienced MACEs. Multivariable Cox regression analysis showed that CAD-RADS categories 2, 3, 4, 5 (hazard ratio range, 2.286-8.325; all P<0.005) and right coronary artery PCAT CT attenuation (hazard ratio, 1.033; P=0.006) were independent predictors of MACEs after adjusting for clinical risk factors. The C statistics revealed that CAD-RADS improved risk stratification compared with PCAT CT alone (C-index, 0.760 versus 0.712; P=0.036). However, the benefit of right coronary artery PCAT CT attenuation combined with CAD-RADS was not significant compared with CAD-RADS alone (0.777 versus 0.760; P=0.129). CONCLUSIONS: Right coronary artery PCAT CT attenuation and CAD-RADS were independent predictors of MACEs. However, no incremental prognostic value of right coronary artery PCAT CT attenuation beyond CAD-RADS was detected for MACEs in patients with acute chest pain.

Nonalcoholic fatty liver disease is associated with myocardial ischemia by CT myocardial perfusion imaging, independent of clinical and coronary CT angiography characteristics.

OBJECTIVE: The aim of this study was to evaluate whether patients with nonalcoholic fatty liver disease (NAFLD) have more myocardial malperfusion on CT myocardial perfusion imaging (CT-MPI), as well as to further assess if NAFLD is a predictor of myocardial ischemia independently. METHODS: A total of 310 consecutive patients were included for analysis. All patients were divided into two groups according to the presence or absence of NAFLD, which was diagnosed by noncontrast cardiac CT partially covered liver and spleen. Clinical characteristics as well as imaging features including coronary artery calcium score, CCTA, and CT-MPI findings were analyzed. Univariable and multivariable logistic regression analyses were used to find out the relationship between NAFLD and myocardial ischemia. RESULTS: NAFLD (unadjusted hazard ratio [HR]: 2.4, 95% confidence interval [CI]: 1.2 to 4.4, p = 0.008), male (HR: 2.6, 95% CI: 1.5 to 4.5, p = 0.001), obstructive CAD (HR: 2.3, 95% CI: 1.3 to 4.2, p = 0.004), and FAI ≥ -70.1 HU (HR: 3.1, 95% CI: 1.8 to 5.5, p < 0.001) were associated with myocardial ischemia in univariable analysis. After adjusting for traditional CAD risk factors and CT characteristics in the multivariable regression analysis, NAFLD (HR: 2.3, 95% CI: 1.2 to 4.4, p = 0.016) was an independent predictor of myocardial ischemia. CONCLUSION: Our data suggest that myocardial ischemia was more prevalent in patients with NAFLD, and NAFLD is a predictor of myocardial ischemia independent of traditional cardiovascular risk factors and CCTA characteristics. KEY POINTS: • NAFLD patients had higher calcium score, incidence of obstructive coronary artery disease, grade of CAD-RADS, quantitative plaque characteristics, and incidence of fat attenuation index ≥ -70.1 HU. • NAFLD patients had a higher incidence of myocardial ischemia, myocardial hypoperfusion, and hypoperfusion myocardial segments ratio. • NAFLD was a predictor of myocardial ischemia, independent of traditional cardiovascular risk factors, and CCTA characteristics.

The Construction of a Risk Prediction Model Based on Neural Network for Pre-operative Acute Ischemic Stroke in Acute Type A Aortic Dissection Patients.

Objective: To establish a pre-operative acute ischemic stroke risk (AIS) prediction model using the deep neural network in patients with acute type A aortic dissection (ATAAD). Methods: Between January 2015 and February 2019, 300 ATAAD patients diagnosed by aorta CTA were analyzed retrospectively. Patients were divided into two groups according to the presence or absence of pre-operative AIS. Pre-operative AIS risk prediction models based on different machine learning algorithm was established with clinical, transthoracic echocardiography (TTE) and CTA imaging characteristics as input. The performance of the difference models was evaluated using the receiver operating characteristic (ROC), precision-recall curve (PRC) and decision curve analysis (DCA). Results: Pre-operative AIS was detected in 86 of 300 patients with ATAAD (28.7%). The cohort was split into a training (211, 70% patients) and validation cohort (89, 30% patients) according to stratified sampling strategy. The constructed deep neural network model had the best performance on the discrimination of AIS group compare with other machine learning model, with an accuracy of 0.934 (95% CI: 0.891-0.963), 0.921 (95% CI: 0.845-0.968), sensitivity of 0.934, 0.960, specificity of 0.933, 0.906, and AUC of 0.982 (95% CI: 0.967-0.997), 0.964 (95% CI: 0.932-0.997) in the training and validation cohort, respectively. Conclusion: The established risk prediction model based on the deep neural network method may have the big potential to evaluate the risk of pre-operative AIS in patients with ATAAD.