The international journal of cardiovascular imaging最新文献

It is observed that some atrial fibrillation (AF) patients develop right heart enlargement and severe right heart failure, which affect the prognosis. An abnormal right atrioventricular coupling index (RACI) indicates that AF has led to significant right atrial and right ventricular dysfunction. However, right atrial (RA) remodeling in AF patients is rarely assessed and data on the prognosis of RA is limited. This study aims to determine if the RACI can be used to predict the risk of long-term adverse outcomes in a cohort of patients with non-valvular AF after catheter ablation (CA). A total of 123 non-valvular AF patients who underwent CA in our center were enrolled in this retrospective study. Conventional and speckle tracking echocardiography (STE) were performed for AF patients before CA. Patients were followed up with until April 2025. The adverse outcomes were the composite of atrial tachyarrhythmia recurrence (ATa), stroke or transient ischemic attack (TIA), AF-associated hospitalization, pacemaker insertion and cardiovascular death. Logistic regression analysis was used to determine the optimal cutoff value of correlates for predicting adverse events. The median follow-up time was 5.3(1.6-5.5) years. Among the 123 AF patients, 69 experienced adverse outcomes. Multivariable logistic regression analysis showed that RACI and left atrial ejection fraction (LAEF) were the independent predictors of adverse events (95% CI, 0.643-0.806; P < 0.0001 and 95% CI, 0.628-0.795; P < 0.0001; respectively). The optimal cutoff values of RACI and LAEF were 70.97% and 35%, respectively. Spearman's correlation analysis showed that RACI was negatively correlated with RA reservoir strain and RA boost strain (r=-0.583, P < 0.001; r=-0.572, P < 0.001, respectively), and LAEF was positively correlated with LA reservoir strain and LA boost strain (r = 0.859, P < 0.001; r = 0.765, P < 0.001, respectively). Increased RACI and impaired LAEF are strongly associated with long-term outcomes, with RACI showing better independent predictive value.

This study evaluated the predictive value of combining clinical characteristics, serum biomarkers, and radiomic features for poor prognosis in patients with acute pulmonary embolism (APE). Clinical data, serum biomarker data (e.g., inflammatory and coagulation biomarkers), and computed tomography pulmonary angiography (CTPA) image from patients with APE were retrospectively collected from the First Affiliated Hospital of Hebei Northern University, First Hospital of Zhangjiakou, and Second Affiliated Hospital of Hebei Northern University. Patients were divided into good and poor prognosis groups. Data from the first two hospitals were randomly split into a training cohort (166) and internal validation cohort (72) using a 7:3 ratio; data from the third hospital formed the external validation cohort (37). Using 3D Slicer software, thrombus regions were outlined to extract radiomic features were constructed using R software. Model performance was assessed via receiver operating characteristic (ROC) curve, calibration, and decision curves analysis, and the Delong test. Six statistically significant radiomic features and independent clinical risk factors, including white blood cell count, neutrophile percentage, lymphocyte percentage, respiratory rate, pulse, syncope, smoking history, and RV / LV ratio, were identified (P < 0.05). The combined model outperformed individual models in the training (AUC = 0.85) (P < 0.05), internal validation (AUC = 0.86), and external validation cohorts (AUC = 0.88) (P < 0.05), demonstrating high clinical utility. The combined model effectively predicts early poor prognosis in APE, offering a robust tool for clinical evaluation and intervention planning.

Aortic atherosclerosis (AA) involves the accumulation of plaque within the vessel wall and predisposes toward cardiovascular events. Therefore, early diagnosis and treatment are important, but early lesions are difficult to identify. Recently, 4D flow magnetic resonance imaging (MRI) has become an established method of evaluating blood flow. We hypothesized that the total aortic (TAV) and stagnation zone (ST) volumes would correlate with the severity of AA and aimed to identify diagnostic cutoff values for these parameters. We studied 181 patients who underwent cardiac contrast-enhanced MRI. They were allocated to two groups according to their cardio-ankle vascular index (CAVI; <0.8, low; ≥0.8, high) (high CAVI: 78 men/24 women; low CAVI: 49 men/30 women) and 40 participants per group were propensity score matched. We measured the TAV and ST volume of the participants and used receiver operating characteristic analysis to identify the most appropriate cutoff values for a diagnosis of AA. The patients in the high CAVI group had larger TAVs (113.7 ± 28.8 vs. 80.4 ± 29.4 cm3, P < 0.0001) and mean and maximum ST volumes (P < 0.0001 for all the stagnation definitions used) than those in the low CAVI group. TAV could differentiate patients in the two groups using a cutoff of < 83.1 cm3, but the mean and maximum ST volume more effectively differentiated them, using cutoffs of 10.6-65.3 and 21.3-81.0 cm3, respectively. Patients with high CAVI scores have larger TAVs and ST volumes than those with low scores, and the ST volume can be used to differentiate these groups.

To compare the diagnostic performance of coronary CT-derived fractional flow reserve (CT-FFR) calculated from conventional polychromatic CT images with 70-keV monoenergetic reconstructions derived from spectral CT data. This retrospective study enrolled patients who underwent spectral coronary CT angiography followed by invasive FFR measurements. The CT-FFR values were computed from conventional polychromatic and 70-keV monoenergetic images. Image quality was assessed using the contrast-to-noise ratio (CNR). Comparison of the CT-FFR with invasive FFR provided mean absolute error (MAE), Pearson's correlation, Bland-Altman analysis, and receiver operating characteristic (ROC) curve analysis. Hemodynamically significant stenosis was defined as an invasive FFR < 0.80. Thirty-two patients (21 men, 11 women; mean age, 68.5 ± 8.4 years) with 47 coronary artery lesions were included in this study. The 70-keV images demonstrated significantly higher CNR than conventional images (36.4 ± 15.2 vs. 25.3 ± 7.4; p < 0.01). CT-FFR derived from 70-keV images yielded lower MAE (0.07 ± 0.05 vs. 0.12 ± 0.08; p < 0.01) and showed stronger correlation with invasive FFR (R = 0.769 vs. R = 0.439). Bland-Altman analysis revealed narrower limits of agreement for the 70-keV CT-FFR. In 17 lesions with hemodynamically significant stenosis, the area under the ROC curve of 70-keV CT-FFR was significantly higher than that of conventional CT-FFR (0.929 vs. 0.807; p = 0.01). CT-FFR derived from 70-keV monoenergetic spectral CT images demonstrated promising agreement with invasive FFR and may offer exploratory insight into improving functional assessment of coronary stenosis.

To evaluate the feasibility of using synthetic extracellular volume (sECV) fraction-calculated from synthetic hematocrit (sHCT) derived via blood CT attenuation-as an alternative for conventional ECV in differentiation heart failure (HF). This study prospectively included 120 patients undergoing calcium scoring (CS), coronary CT angiography (CCTA), and late enhancement (LE) imaging (5 min after CCTA), with HCT measured within 24 h. Conventional ECV (cECV) was calculated using serum HCT. An additional retrospective cohort of 207 patients with CS and HCT was analyzed. Linear regression was used to derive sHCT from blood CT attenuation in the left ventricle (LV), right ventricle (RV), and ascending aorta (AO), enabling calculation of sHCT fractions (sECV_LV, sECV_RV, sECV_AO). Among 64 HF and 56 nonHF patients, cECV was significantly higher in HF cases (36.83 ± 5.75 vs. 30.34 ± 4.88, P < 0.001; AUC = 0.82). The sECV_LV, sECV_RV and sECV_AO showed strong correlations with cECV (R²=0.96, 0.97, and 0.93). HF patients had higher sECV_LV (36.79 ± 6.11% vs. 30.42 ± 5.09%, P < 0.001), sECV_RV (36.49 ± 6.10% vs. 30.19 ± 4.99%, P < 0.001), and sECV_AO (36.87 ± 6.37% vs. 30.23 ± 5.31%, P < 0.001) than nonHF patients. The AUCs for detecting HF using sECV_LV, sECV_RV and sECV_AO were 0.80, 0.79, and 0.80, respectively. Delong test indicated no significant differences among cECV and sECV fractions (all P > 0.05). All ECV fractions showed moderate negative correlations with left ventricular ejection fraction (all P < 0.05). Synthetic ECV fractions derived from cardiac CT are feasible and demonstrate diagnostic performance comparable to conventional ECV fraction in identifying HF.

The accurate evaluation of pulmonary vascular resistance (PVR) is critical for evaluating operability and guiding management in adults with unrepaired atrial septal defect (ASD). Non-invasive echocardiographic markers, such as the echocardiographic Pulmonary to Left Atrial Ratio (ePLAR), calculated by dividing the peak tricuspid regurgitation velocity (TR Vmax) by the ratio of the transmitral E-wave to mitral annular e' velocity, may facilitate early risk stratification and reduce reliance on invasive procedures. Eighty-nine adult patients with unrepaired ASD underwent comprehensive echocardiography and right heart catheterization. ePLAR and additional echocardiographic parameters were measured, and their ability to identify elevated PVR (≥5 Wood Units, WU) was assessed through correlation and receiver operating characteristic (ROC) analysis. PVR ≥ 5 group showed elevated ePLAR (0.41 vs. 0.29, p < 0.0001. ePLAR showed moderate ability to predict elevated PVR (AUC 0.754). The TR Vmax/ right ventricular outflow tract velocity-time integral (RVOT VTI) and TR Vmax²/RVOT VTI ratio demonstrated stronger discrimination (AUC 0.907 and 0.929). DeLong's test confirmed TR Vmax/RVOT VTI superiority (p = 0.0147). TRVmax/ right ventricular global longitudinal strain (RV GLS) shown to be an ineffective measure (AUC 0.265, p > 0.05). ePLAR is a practical, novel non-invasive echocardiographic parameter for identifying elevated pulmonary vascular resistance in adults with unrepaired ASD. With high sensitivity and independent predictive value, ePLAR is suitable for initial screening and risk stratification, potentially reducing unnecessary invasive testing and improving clinical decision-making.

To investigate whether non-calcified coronary plaque (NCP) volume differs between symptomatic versus asymptomatic individuals across strata of coronary artery calcium (CAC) score, and whether symptom status adds incremental value beyond CAC. In this retrospective single-center study, we analyzed 1,835 adults (14% symptomatic, 86% asymptomatic) who underwent coronary CT angiography (CCTA) with AI-based plaque quantification. Participants were stratified into five CAC categories: 0, 1-99, 100-299, 300-1000, and > 1000 Agatston units. Total NCP volume was compared between symptomatic and asymptomatic patients within each stratum using non-parametric tests. Median NCP volumes were similar in both groups for CAC 0 (17.7 vs. 18.6 mm³, p = 0.96) and CAC 1-99 (50.8 vs. 56.3 mm³, p = 0.37). A significant difference emerged in the CAC 100-299 category: symptomatic patients had higher NCP (152.3 vs. 112.3 mm³, p = 0.035). No significant differences were seen in CAC 300-1000 or > 1000 (p = 0.12 and p = 0.066, respectively). Symptom status may be associated with higher non-calcified plaque burden particularly in symptomatic patients with CAC 100-299. Prospective outcome studies are needed to determine whether AI-quantified NCP volume can guide preventive therapy independent of CAC.

Aortic diseases, particularly acute aortic syndromes (AAS) and aortic aneurysms (AA), represent critical cardiovascular conditions with high mortality rates requiring precise imaging for diagnosis and management. This review provides a comprehensive analysis of current imaging diagnostic techniques, focusing specifically on acquired thoracic and abdominal aortic pathologies. We first evaluate the comparative efficacy of Computed Tomography Angiography (CTA) and Magnetic Resonance Imaging (MRI) in the diagnosis of AAS (including aortic dissection, intramural hematoma, and penetrating atherosclerotic ulcer), highlighting the role of artificial intelligence in optimizing segmentation and detection. Subsequently, we discuss aortic aneurysms, emphasizing the shift from simple diameter-based assessment to functional risk stratification incorporating calcification scoring, inflammatory imaging, and hemodynamic parameters. Furthermore, the review addresses postoperative imaging surveillance, particularly for endoleak detection following endovascular aneurysm repair (EVAR). We conclude that while CTA remains the gold standard for emergency diagnosis due to its speed and spatial resolution, MRI offers superior value in functional assessment and radiation-free long-term follow-up. The integration of multimodal imaging and AI-driven analysis is essential for achieving precision medicine in the management of acquired aortic diseases.