Agreement of ejection fraction measured by coronary computed tomography (CT) and cardiac ultrasound in evaluating patients with chronic heart failure: an observational comparative study.

Background: Cardiac ultrasound is one of the most important examinations in cardiovascular medicine, but the technical requirements for the operator are relatively high, which to some extent affects the scope of its use. This study was dedicated to investigating the agreement of ejection fraction between coronary computed tomography (CT) and cardiac ultrasound and diagnostic performance in evaluating the clinical diagnosis of patients with chronic heart failure. Methods: We conducted a single-center-based retrospective study including 343 consecutive patients enrolled between January 2019 to April 2020, all of whom presented with suspected symptoms of heart failure within one month. All enrolled cases performed cardiac ultrasound and coronary CT scans. The CT images were analyzed using accurate left ventricle (AccuLV) artificial intelligence (AI) software to calculate the ejection fraction-computed tomography (EF-CT) and it was compared with the ejection fraction (EF) obtained based on ultrasound. Cardiac insufficiency was determined if the EF measured by ultrasound was below 50%. Diagnostic performance analysis, correlation analysis and Bland-Altman plot were used to compare agreement between EF-CT and CT. Results: Of the 319 successfully performed patients, 220 (69%) were identified as cardiac insufficiency. Quantitative consistency analysis showed a good correlation between EF-CT and EF values in all cases (R square =0.704, r=0.837). Bland-Altman analysis showed mean bias of 6.6%, mean percentage error of 27.5% and 95% limit of agreement of -17% to 30% between EF and EF-CT. The results of the qualitative diagnostic study showed that the sensitivity and specificity of EF measured by coronary CT reached a high level of 91% [95% confidence interval (CI): 86-94%], and the positive diagnostic value was up to 96% (95% CI: 92-98%). Conclusions: The EF-CT and EF have excellent agreement, and AccuLV-based AI left ventricular function analysis software perhaps can be used as a clinical diagnostic reference.

Differences and Correlations of Morphological and Hemodynamic Parameters between Anterior Circulation Bifurcation and Side-wall Aneurysms.

OBJECTIVE: The objective of this research was to explore the difference and correlation of the morphological and hemodynamic features between sidewall and bifurcation aneurysms in anterior circulation arteries, utilizing computational fluid dynamics as a tool for analysis. METHODS: In line with the designated inclusion criteria, this study covered 160 aneurysms identified in 131 patients who received treatment at Union Hospital of Tongji Medical College, Huazhong University of Science and Technology, China, from January 2021 to September 2022. Utilizing follow-up digital subtraction angiography (DSA) data, these cases were classified into two distinct groups: the sidewall aneurysm group and the bifurcation aneurysm group. Morphological and hemodynamic parameters in the immediate preoperative period were meticulously calculated and examined in both groups using a three-dimensional DSA reconstruction model. RESULTS: No significant differences were found in the morphological or hemodynamic parameters of bifurcation aneurysms at varied locations within the anterior circulation. However, pronounced differences were identified between sidewall and bifurcation aneurysms in terms of morphological parameters such as the diameter of the parent vessel (Dvessel), inflow angle (θF), and size ratio (SR), as well as the hemodynamic parameter of inflow concentration index (ICI) (P<0.001). Notably, only the SR exhibited a significant correlation with multiple hemodynamic parameters (P<0.001), while the ICI was closely related to several morphological parameters (R>0.5, P<0.001). CONCLUSIONS: The significant differences in certain morphological and hemodynamic parameters between sidewall and bifurcation aneurysms emphasize the importance to contemplate variances in threshold values for these parameters when evaluating the risk of rupture in anterior circulation aneurysms. Whether it is a bifurcation or sidewall aneurysm, these disparities should be considered. The morphological parameter SR has the potential to be a valuable clinical tool for promptly distinguishing the distinct rupture risks associated with sidewall and bifurcation aneurysms.

Classification and hemodynamic characteristics of delayed intracerebral hemorrhage following stent-assisted coil embolism in unruptured intracranial aneurysms.

Background and objective: Stent-assisted coil (SAC) embolization is a commonly used endovascular treatment for unruptured intracranial aneurysms (UIAs) but can be associated with symptomatic delayed intracerebral hemorrhage (DICH). Our study aimed to investigate the hemodynamic risk factors contributing to DICH following SAC embolization and to establish a classification for DICH predicated on hemodynamic profiles. Methods: This retrospective study included patients with UIAs located in the internal carotid artery (ICA) treated with SAC embolization at our institution from January 2021 to January 2022. We focused on eight patients who developed postoperative DICH and matched them with sixteen control patients without DICH. Using computational fluid dynamics, we evaluated the hemodynamic changes in distal arteries [terminal ICA, the anterior cerebral artery (ACA), and middle cerebral artery (MCA)] pre-and post-embolization. We distinguished DICH-related arteries from unrelated ones (ACA or MCA) and compared their hemodynamic alterations. An imbalance index, quantifying the differential in flow velocity changes between ACA and MCA post-embolization, was employed to gauge the flow distribution in distal arteries was used to assess distal arterial flow distribution. Results: We identified two types of DICH based on postoperative flow alterations. In type 1, there was a significant lower in the mean velocity increase rate of the DICH-related artery compared to the unrelated artery (-47.25 ± 3.88% vs. 42.85 ± 3.03%; p < 0.001), whereas, in type 2, there was a notable higher (110.58 ± 9.42% vs. 17.60 ± 4.69%; p < 0.001). Both DICH types demonstrated a higher imbalance index than the control group, suggesting an association between altered distal arterial blood flow distribution and DICH occurrence. Conclusion: DICH in SAC-treated UIAs can manifest as either a lower (type 1) or higher (type 2) in the rate of velocity in DICH-related arteries. An imbalance in distal arterial blood flow distribution appears to be a significant factor in DICH development.

Nomograms for assessing the rupture risk of anterior choroid artery aneurysms based on clinical, morphological, and hemodynamic features.

Background and purpose: A notable prevalence of subarachnoid hemorrhage is evident among patients with anterior choroidal artery aneurysms in clinical practice. To evaluate the risk of rupture in unruptured anterior choroidal artery aneurysms, we conducted a comprehensive analysis of risk factors and subsequently developed two nomograms. Methods: A total of 120 cases of anterior choroidal artery aneurysms (66 unruptured and 54 ruptured) from 4 medical institutions were assessed utilizing computational fluid dynamics (CFD) and digital subtraction angiography (DSA). The training set, consisting of 98 aneurysms from 3 hospitals, was established, with an additional 22 cases from the fourth hospital forming the external validation set. Statistical differences between the two data sets were thoroughly compared. The significance of 9 clinical baseline characteristics, 11 aneurysm morphology parameters, and 4 hemodynamic parameters concerning aneurysm rupture was evaluated within the training set. Candidate selection for constructing the nomogram models involved regression analysis and variance inflation factors. Discrimination, calibration, and clinical utility of the models in both training and validation sets were assessed using area under curves (AUC), calibration plots, and decision curve analysis (DCA). The DeLong test, net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed to compare the effectiveness of classification across models. Results: Two nomogram models were ultimately constructed: model 1, incorporating clinical, morphological, and hemodynamic parameters (C + M + H), and model 2, relying primarily on clinical and morphological parameters (C + M). Multivariate analysis identified smoking, size ratio (SR), normalized wall shear stress (NWSS), and average oscillatory shear index (OSIave) as optimal candidates for model development. In the training set, model 1 (C + M + H) achieved an AUC of 0.795 (95% CI: 0.706 ~ 0.884), demonstrating a sensitivity of 95.6% and a specificity of 54.7%. Model 2 (C + M) had an AUC of 0.706 (95% CI: 0.604 ~ 0.808), with corresponding sensitivity and specificity of 82.4 and 50.3%, respectively. Similarly, AUCs for models 1 and 2 in the external validation set were calculated to be 0.709 and 0.674, respectively. Calibration plots illustrated a consistent correlation between model evaluations and real-world observations in both sets. DCA demonstrated that the model incorporating hemodynamic parameters offered higher clinical benefits. In the training set, NRI (0.224, p = 0.007), IDI (0.585, p = 0.002), and DeLong test (change = 0.089, p = 0.008) were all significant. In the external validation set, NRI, IDI, and DeLong test statistics were 0.624 (p = 0.063), 0.572 (p = 0.044), and 0.035 (p = 0.047), respectively. Conclusion: Multidimensional nomograms have the potential to enhance risk assessment and patient-specific treatment of anterior choroidal artery aneurysms. Validated by an external cohort, the model incorporating clinical, morphological, and hemodynamic features may provide improved classification of rupture states.

Hemodynamic assessment for intracranial atherosclerosis from angiographic images: a clinical validation study.

BACKGROUND: Intracranial atherosclerotic stenosis (ICAS) is one of the leading causes of ischemic stroke. Conventional anatomical analysis by CT angiography, MRI, or digital subtraction angiography can provide valuable information on the anatomical changes of stenosis; however, they are not sufficient to accurately evaluate the hemodynamic severity of ICAS. The goal of this study was to assess the diagnostic performance of the pressure ratio across intracranial stenoses (termed as fractional flow (FF)) derived from cerebral angiography for the diagnosis of hemodynamically significant ICAS defined by pressure wire-derived FF. METHODS: This retrospective study represents a feasible and reliable method for calculating the FF from cerebral angiography (AccuFFicas). Patients (n=121) who had undergone wire-based measurement of FF and cerebral angiography were recruited. The accuracy of the computed pressure ratio was evaluated using wire-based FF as the reference standard. RESULTS: The mean value of wire-based FF was 0.78±0.19, while the computed AccuFFicas had an average value of 0.79±0.18. Good correlation (Pearson's correlation coefficient r=0.92, P<0.001) between AccuFFicas and FF was observed. Bland-Altman analysis showed that the mean difference between AccuFFicas and FF was -0.01±0.07, indicating good agreement. The area under the curve (AUC) of AccuFFicas in predicting FF≤0.70, FF≤0.75, and FF≤0.80 was 0.984, 0.986, and 0.962, respectively. CONCLUSION: Angiography-based FF computed from cerebral angiographic images could be an effective computational tool for evaluating the hemodynamic significance of ICAS.

Hemodynamic alterations of flow diverters on aneurysms at the fetal posterior communicating artery: A simulation study using CFD to compare the surpass streamline, pipeline flex, and tubridge devices.

PURPOSE: Traditional flow diverters (FDs) for treating aneurysms at the fetal posterior communicating artery are unsatisfactory. Surpass Streamline is a novel FD with different mesh characteristics; however, the outcomes for such aneurysms remain unclear. This study aimed to compare hemodynamic alterations induced by Surpass Streamline, Pipeline Flex, and Tubridge devices and explore possible strategies for aneurysms at the fetal posterior communicating artery. METHODS: Two simulated aneurysms (Case 1, Case 2) were constructed from digital subtraction angiography (DSA). The three FDs were virtually deployed, and hemodynamic analysis based on computational fluid dynamics was performed. Hemodynamic parameters, including the sac-averaged velocity magnitude (Velocity), high-flow volume (HFV), and wall shear stress (WSS), were compared between each FD and the untreated model (control). Surpass Streamline was performed in real life for two aneurysms and the clinical outcomes were collected for analysis. RESULTS: Compared to the control, the Surpass resulted in the most significant reduction in flow. In Case 1, the Velocity, HFV, and WSS were reduced by 51.6%, 78.1%, and 64.3%, respectively. In Case 2, the Velocity, HFV, and WSS were reduced by 48.0%, 81.1%, and 65.3%, respectively. Tubridge showed slightly larger changes in hemodynamic parameters than Pipeline. In addition, our analysis suggested that metal coverage was correlated with the WSS, Velocity, and HFV. The postoperative DSA showed that the aneurysm was nearly occluded in Case 1 and decreased in Case 2. CONCLUSION: Compared to that with the Pipeline and Tubridge, the Surpass resulted in the greatest reduction in hemodynamic parameters and might be effective for aneurysms at the fetal posterior communicating artery. Virtual FD deployment and computational fluid dynamics analysis may be used to predict the treatment outcomes.

Automated ASPECTS calculation may equal the performance of experienced clinicians: a machine learning study based on a large cohort.

OBJECTIVES: The Alberta Stroke Program Early CT Score (ASPECTS) is a semi-quantitative method to evaluate the severity of early ischemic change on non-contrast computed tomography (NCCT) in patients with acute ischemic stroke (AIS). In this work, we propose an automated ASPECTS method based on large cohort of data and machine learning. METHODS: For this study, we collected 3626 NCCT cases from multiple centers and annotated directly on this dataset by neurologists. Based on image analysis and machine learning methods, we constructed a two-stage machine learning model. The validity and reliability of this automated ASPECTS method were tested on an independent external validation set of 300 cases. Statistical analyses on the total ASPECTS, dichotomized ASPECTS, and region-level ASPECTS were presented. RESULTS: On an independent external validation set of 300 cases, for the total ASPECTS results, the intraclass correlation coefficient between automated ASPECTS and expert-rated was 0.842. The agreement between ASPECTS threshold of ≥ 6 versus < 6 using a dichotomized method was moderate (κ = 0.438, 0.391-0.477), and the detection rate (sensitivity) was 86.5% for patients with ASPECTS threshold of ≥ 6. Compared with the results of previous studies, our method achieved a slight lead in sensitivity (67.8%) and AUC (0.845), with comparable accuracy (78.9%) and specificity (81.2%). CONCLUSION: The proposed automated ASPECTS method driven by a large cohort of NCCT images performed equally well compared with expert-rated ASPECTS. This work further demonstrates the validity and reliability of automated ASPECTS evaluation method. CLINICAL RELEVANCE STATEMENT: The automated ASPECTS method proposed by this study may help AIS patients to receive rapid intervention, but should not be used as a stand-alone diagnostic basis. KEY POINTS: NCCT-based manual ASPECTS scores were poorly consistent. Machine learning can automate the ASPECTS scoring process. Machine learning model design based on large cohort data can effectively improve the consistency of ASPECTS scores.

AneuGuide™ software-assisted vs. manual measurements in sizing for pipeline embolization device: An agreement study.

Sizing of flow diverters (FDs) is a challenging task in the treatment of intracranial aneurysms due to their foreshortening behavior. The purpose of this study is to evaluate the difference between the sizing results from the AneuGuide™ software and from conventional 2D measurement. Ninety-eight consecutive patients undergoing pipeline embolization device (PED) treatment between October 2018 and April 2023 in the First Medical Center of Chinese PLA General Hospital (Beijing, China) were retrospectively analyzed. For all cases, the optimal PED dimensions were both manually determined through 2D measurements on pre-treatment 3D-DSA and computed by AneuGuide™ software. The inter-rater reliability between the two sets of sizing results for each methodology was analyzed using intraclass correlation coefficient (ICC). The degree of agreement between manual sizing and software sizing were analyzed with the Bland-Altman plot and Pearson's test. Differences between two methodologies were analyzed with Wilcoxon signed rank test. Statistical significance was defined as p < 0.05. There was better inter-rater reliability between AneuGuide™ measurements both for diameter (ICC 0.92, 95%CI 0.88-0.95) and length (ICC 0.93, 95%CI 0.89-0.96). Bland-Altman plots showed a good agreement for diameter selection between two methodologies. However, the median length proposed by software group was significantly shorter (16 mm versus 20 mm, p < 0.001). No difference was found for median diameter (4.25 mm versus 4.25 mm). We demonstrated that the AneuGuide™ software provides highly reliable results of PED sizing compared with manual measurement, with a shorter stent length. AneuGuide™ may aid neurointerventionalists in selecting optimal dimensions for FD treatment.

Diagnostic accuracy of a novel optical coherence tomography-based fractional flow reserve algorithm for assessment of coronary stenosis significance.

BACKGROUND: This study aimed to introduce a novel optical coherence tomography-derived fractional flow reserve (FFR) computational approach and assess the diagnostic performance of the algorithm for assessing physiological function. METHODS: The fusion of coronary optical coherence tomography and angiography was used to generate a novel FFR algorithm (AccuFFRoct) to evaluate functional ischemia of coronary stenosis. In the current study, a total of 34 consecutive patients were included, and AccuFFRoct was used to calculate the FFR for these patients. With the wire-measured FFR as the reference standard, we evaluated the performance of our approach by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: Per vessel accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRoct in identifying hemodynamically significant coronary stenosis were 93.8%, 94.7%, 92.3%, 94.7%, and 92.3%, respectively, were found. Good correlation (Pearson's correlation coefficient r = 0.80, p < 0.001) between AccuFFRoct and FFR was observed. The Bland-Altman analysis showed a mean difference value of -0.037 (limits of agreement: -0.189 to 0.115). The area under the receiver-operating characteristic curve (AUC) of AccuFFRoct in identifying physiologically significant stenosis was 0.94, which was higher than the minimum lumen area (MLA, AUC = 0.91) and significantly higher than the diameter stenosis (%DS, AUC = 0.78). CONCLUSIONS: This clinical study shows the efficiency and accuracy of AccuFFRoct for clinical implementation when using invasive FFR measurement as a reference. It could provide important insights into coronary imaging superior to current methods based on the degree of coronary artery stenosis.

Comparison of deep learning-based image segmentation methods for intravascular ultrasound on retrospective and large image cohort study.

OBJECTIVES: The aim of this study was to investigate the generalization performance of deep learning segmentation models on a large cohort intravascular ultrasound (IVUS) image dataset over the lumen and external elastic membrane (EEM), and to assess the consistency and accuracy of automated IVUS quantitative measurement parameters. METHODS: A total of 11,070 IVUS images from 113 patients and pullbacks were collected and annotated by cardiologists to train and test deep learning segmentation models. A comparison of five state of the art medical image segmentation models was performed by evaluating the segmentation of the lumen and EEM. Dice similarity coefficient (DSC), intersection over union (IoU) and Hausdorff distance (HD) were calculated for the overall and for subsets of different IVUS image categories. Further, the agreement between the IVUS quantitative measurement parameters calculated by automatic segmentation and those calculated by manual segmentation was evaluated. Finally, the segmentation performance of our model was also compared with previous studies. RESULTS: CENet achieved the best performance in DSC (0.958 for lumen, 0.921 for EEM) and IoU (0.975 for lumen, 0.951 for EEM) among all models, while Res-UNet was the best performer in HD (0.219 for lumen, 0.178 for EEM). The mean intraclass correlation coefficient (ICC) and Bland-Altman plot demonstrated the extremely strong agreement (0.855, 95% CI 0.822-0.887) between model's automatic prediction and manual measurements. CONCLUSIONS: Deep learning models based on large cohort image datasets were capable of achieving state of the art (SOTA) results in lumen and EEM segmentation. It can be used for IVUS clinical evaluation and achieve excellent agreement with clinicians on quantitative parameter measurements.

How does the recurrence-related morphology characteristics of the Pcom aneurysms correlated with hemodynamics?

Introduction: Posterior communicating artery (Pcom) aneurysm has unique morphological characteristics and a high recurrence risk after coil embolization. This study aimed to evaluate the relationship between the recurrence-related morphology characteristics and hemodynamics. Method: A total of 20 patients with 22 Pcom aneurysms from 2019 to 2022 were retrospectively enrolled. The recurrence-related morphology parameters were measured. The hemodynamic parameters were simulated based on finite element analysis and computational fluid dynamics. The hemodynamic differences before and after treatment caused by different morphological features and the correlation between these parameters were analyzed. Result: Significant greater postoperative inflow rate at the neck (Qinflow), relative Qinflow, inflow concentration index (ICI), and residual flow volume (RFV) were reported in the aneurysms with wide neck (>4 mm). Significant greater postoperative RFV were reported in the aneurysms with large size (>7 mm). Significant greater postoperative Qinflow, relative Qinflow, and ICI were reported in the aneurysms located on the larteral side of the curve. The bending angle of the internal carotid artery at the initiation of Pcom (αICA@PCOM) and neck diameter had moderate positive correlations with Qinflow, relative Qinflow, ICI, and RFV. Conclusion: The morphological factors, including aneurysm size, neck diameter, and αICA@PCOM, are correlated with the recurrence-inducing hemodynamic characteristics even after fully packing. This provides a theoretical basis for evaluating the risk of aneurysm recurrence and a reference for selecting a surgical plan.

Computed tomography perfusion software pipelines to assess parameter maps and ischemic volumes: A comparative study.

BACKGROUND AND PURPOSE: This study was dedicated to investigating the agreement of the calculated results of two CT perfusion (CTP) postprocessing software packages, including parameter maps and ischemic volume, focusing on the infarct core volume (ICV) and penumbra volume (PV). METHODS: A retrospective collection of 235 patients with acute ischemic stroke who underwent CTP examination were enrolled. All images had been analyzed with two software pipelines, RAPID CTP and AccuCTP, and the comparative analysis was based on ICV and PV results calculated by both software packages. The agreement of parameter maps was evaluated by root mean square error and Bland-Altman analysis. The ICV and PV agreement was evaluated by intraclass correlation coefficient (ICC) and Bland-Altman analysis. The accuracy of ICV and PV based on multiple thresholds was also analyzed. RESULTS: The ICV and PV of AccuCTP and RAPID CTP show excellent agreement. The relative differences of the parameter maps were all within 10% and the Bland-Altman analysis also showed a strong agreement. From ordinary least squares fitting results, both ICV and PV had a remarkably high goodness of fit (ICV, R2 = 0.975 [p<.001]; PV, R2 = 0.964 [p<.001]). For the ICC analysis, both had high ICC scores (ICV ICC 0.984, 95% CI [confidence interval] 0.973-0.989; PV ICC 0.955, 95% CI 0.947-0.964). Furthermore, multi-threshold analysis on the basis of ICV and PV also achieved reliable analytical accuracy. CONCLUSIONS: The image analysis results of AccuCTP are in excellent agreement with RAPID CTP and can be used as an alternative analysis tool to RAPID CTP software in stroke clinical practice.

Calculation of left ventricular ejection fraction using an 8-layer residual U-Net with deep supervision based on cardiac CT angiography images versus echocardiography: a comparative study.

Background: Accurate segmentation of the left ventricle (LV) is an important step in assessing cardiac function. Cardiac CT angiography (CCTA) has become an important means of clinical diagnosis of cardiovascular diseases (CVDs) because of its advantages of non-invasive, short examination time and low cost. In order to obtain the segmentation of LV in CCTA scans, we propose a deep learning method based on 8-layer residual U-Net with deep supervision. In this study we compared the left ventricular ejection fraction (LVEF) calculated by deep learning (DL) method (AccuLV) from CCTA to LVEF by conventional two-dimensional echocardiography (EC). Methods: This was a retrospective cross-sectional study, and consecutive patients who had undergone CCTA and EC in our hospital from February 2021 to May 2021 were recruited. The current study included 180 patients who had undergone CCTA and EC. To obtain LVEF, we used an 8-layer residual U-Net with deep supervision to segment LV contours from CCTA images and compute LVEF (DL-LVEF). The EC and DL-LVEF measurements were compared. A 50% EC-LVEF cut-off value was used as a reference standard to assess the diagnostic performance of AccuLV in assessing LV function. Results: The overall mean EC-LVEF and DL-LVEF values were 64.0% (52.3%, 69.0%) and 73.0% (52.3%, 77.0%), respectively. Three patient groups were studied: (I) hypertensive patients, (II) postmenopausal women, and (III) diabetes. EC-LVEF and DL-LVEF were found to be positively correlated for all of the included patients (r=0.82, P<0.001), with the detailed results for the three groups as follows: hypertensive patients (r=0.77, P<0.001), postmenopausal women (r=0.92, P<0.001) and diabetes (r=0.88, P<0.001). The diagnostic accuracy, sensitivity, and specificity of the DL method in predicting EC-LVEF <50% for all patients were 93.9%, 92.3%, and 94.3%, and for hypertensive patients were 95.4%, 93.8%, and 95.8%, for postmenopausal women were 87.0%, 100%, and 84.2%, for diabetes were 97.4%, 100%, and 96.6%. Conclusions: In comparison to echocardiography, which is commonly used in clinical setting, AccuLV may be a promising, fully automated tool for rapid and accurate quantification of LV function and thus for making reliable clinical decisions.

Are baseline conditions of coronary arteries sufficient for calculating angio-based index of microcirculatory resistance and fractional flow reserve?

Background: Angio-based index of microcirculatory resistance (IMR) and fractional flow reserve (FFR) have been developed, however, the differences between baseline and hyperemic data and their effects on their computation have not yet been discussed. This study aimed to compare the diagnostic performance of a novel method for calculating IMR and FFR from coronary angiography under baseline and hyperemic conditions. Methods: We performed a retrospective study to investigate the diagnostic performance of angiography-derived IMR (AccuIMR) and FFR (AccuFFRangio) computed from the hyperemic condition (AccuIMRhyp, AccuFFRangiohyp) and baseline condition (AccuIMRbase, AccuFFRangiobase) in 101 consecutive patients with chronic coronary syndrome (CCS) who underwent measurements of IMR and FFR at a single center, using wire-based IMR and FFR as the reference standard. Results: AccuIMRhyp showed much better correlation with IMR than AccuIMRbase (r=0.77 vs. 0.47, P<0.001). The diagnostic accuracy and area under the curve (AUC) for identifying significant microvascular dysfunction was higher for AccuIMRhyp than AccuIMRbase [92.1% (95% CI: 85.0-96.5%) vs. 83.2% (95% CI: 74.4-89.9%), P=0.012; 0.942 (95% CI: 0.877-0.979) vs. 0.815 (95% CI: 0.726-0.886), P=0.003]. The computed AccuFFRangio showed good correlations with FFR and good diagnostic performance under both hyperemic and baseline conditions [r=0.68 vs. 0.68, P>0.99; diagnostic accuracy =95.9% (95% CI: 89.8-98.9%) vs. 94.9% (95% CI: 88.4-98.3%), P=0.728; AUC =0.989 (95% CI: 0.942-1.000) vs. 0.973 (95% CI: 0.919-0.995), P=0.381]. The net reclassification index (NRI) demonstrated that hyperemic group had improved reclassification ability compared to the baseline group in identification of IMR >25 (NRI =0.20, P<0.001) and FFR ≤0.8 (NRI =0.11, P<0.001). Conclusions: By comparing the calculated angio-derived IMR and FFR under the baseline and hyperemic conditions, this study demonstrates that AccuIMR calculation is more accurate using the hyperemic condition, while AccuFFRangio calculation is accurate under both conditions.

Intravascular Ultrasound-Based Fractional Flow Reserve for Predicting Prognosis after Percutaneous Coronary Intervention.

AccuFFRivus is an alternative to fractional flow reserve (FFR) based on intravascular ultrasound (IVUS) images for functional assessment of coronary stenosis. However, its prognostic impact in patients undergoing percutaneous coronary intervention (PCI) is still unclear. This retrospective study aimed to investigate the capability of AccuFFRivus in predicting prognosis. AccuFFRivus was calculated based on postoperative angiographic and IVUS images. Vessel-oriented clinical events (VOCE) at 2 years were recorded and analyzed. A total of 131 participants with 131 vessels were included in the study. VOCE occurred in 15 patients during 2-year follow-up. AccuFFRivus after PCI (post-AccuFFRivus) was significantly higher in the non-VOCE group than in the VOCE group (0.95 ± 0.03 vs. 0.91 ± 0.02, p < 0.001). Multivariate Cox regression showed that AccuFFRivus ≤ 0.94 was a strong independent predictor of VOCE during 2-year follow-up (hazard ratio 23.76, 95% confidence interval: 3.04-185.81, p < 0.001). The left panel displays the Receiver operating characteristics (ROC) curves of postoperative parameters (post-AccuFFRivus and post-MLA) versus vessel-oriented clinical events (VOCE) occurrence within 2-year follow-up. The right panel demonstrates Kaplan-Meier curves of VOCE stratified by the optimal cut-off of post-AccuFFRivus.

Angiography-based index of microcirculatory resistance (AccuIMR) for the assessment of microvascular dysfunction in acute coronary syndrome and chronic coronary syndrome.

Background: To assess the diagnostic accuracy of AccuIMR, a newly proposed, pressure wire-free index, in identifying coronary microvascular dysfunction (CMD) among patients with acute coronary syndrome [including ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI)] and chronic coronary syndrome (CCS). Methods: A total of 163 consecutive patients (43 with STEMI, 59 with NSTEMI, and 61 with CCS), who underwent invasive coronary angiography (ICA) and for whom the index of microcirculatory resistance (IMR) was measured, were retrospectively enrolled at a single center. IMR measurements were made in 232 vessels. The AccuIMR based on computational fluid dynamics (CFD) was calculated from coronary angiography. The diagnostic performance of AccuIMR was assessed using wire-based IMR as a reference standard. Results: AccuIMR correlated well with IMR (overall r=0.76, P<0.001; STEMI r=0.78, P<0.001; NSTEMI r=0.78, P<0.001; CCS r=0.75, P<0.001) and had good diagnostic performance in detecting abnormal IMR [overall diagnostic accuracy, sensitivity, and specificity were 94.83% (91.14% to 97.30%), 92.11% (78.62% to 98.34%), and 95.36% (91.38% to 97.86%), respectively]. Using a cutoff value of IMR >40 U for AccuIMR in STEMI and IMR >25 U in NSTEMI and CCS, the area under the receiver operating characteristic (ROC) curve (AUC) of AccuIMR for predicting abnormal IMR value was 0.917 (0.874 to 0.949) in all patients, 1.000 (0.937 to 1.000) in patients with STEMI, 0.941 (0.867 to 0.980) in patients with NSTEMI, and 0.918 (0.841 to 0.966) in patients with CCS. Conclusions: The use of AccuIMR in the evaluation of microvascular diseases could provide valuable information and potentially increase the application of physiological assessment for microcirculation in patients with ischemic heart disease.

Wall enhancement predictive of abnormal hemodynamics and ischemia in vertebrobasilar non-saccular aneurysms: a pilot study.

Objective: To analyze how wall enhancement affects hemodynamics and cerebral ischemic risk factors in vertebrobasilar non-saccular intracranial aneurysms (VBNIAs). Materials and methods: Ten consecutive non-saccular aneurysms were collected, including three transitional vertebrobasilar dolichoectasia (TVBD). A wall enhancement model was quantitatively constructed to analyze how wall enhancement interacts with hemodynamics and cerebral ischemic factors. Results: Enhanced area revealed low wall shear stress (WSS) and wall shear stress gradient (WSSG), with high oscillatory shear index (OSI), relative residence time (RRT), and gradient oscillatory number (GON) while the vortex and slow flow region in fusiform aneurysms are similar to TVBD fusiform aneurysms. With low OSI, high RRT and similar GON in the dilated segment, the enhanced area still manifests low WSS and WSSG in the slow flow area with no vortex. In fusiform aneurysms, wall enhancement was negatively correlated with WSS (except for case 71, all p values < 0.05, r = -0.52 ~ -0.95), while wall enhancement was positively correlated with OSI (except for case 5, all p values < 0.05, r = 0.50 ~ 0.83). For the 10 fusiform aneurysms, wall enhancement is significantly positively correlated with OSI (p = 0.0002, r = 0.75) and slightly negatively correlated with WSS (p = 0.196, r = -0.30) throughout the dataset. Aneurysm length, width, low wall shear stress area (LSA), high OSI, low flow volume (LFV), RRT, and high aneurysm-to-pituitary stalk contrast ratio (CRstalk) area plus proportion may be predictive of cerebral ischemia. Conclusion: A wall enhancement quantitative model was established for vertebrobasilar non-saccular aneurysms. Low WSS was negatively correlated with wall enhancement, while high OSI was positively correlated with wall enhancement. Fusiform aneurysm hemodynamics in TVBD are similar to simple fusiform aneurysms. Cerebral ischemia risk appears to be correlated with large size, high OSI, LSA, and RRT, LFV, and wall enhancement.

Accuracy of intravascular ultrasound-derived virtual fractional flow reserve (FFR) and FFR derived from computed tomography for functional assessment of coronary artery disease.

BACKGROUND: Coronary computed tomography-derived fractional flow reserve (CT-FFR) and intravascular ultrasound-derived fractional flow reserve (IVUS-FFR) are two functional assessment methods for coronary stenoses. However, the calculation algorithms for these methods differ significantly. This study aimed to compare the diagnostic performance of CT-FFR and IVUS-FFR using invasive fractional flow reserve (FFR) as the reference standard. METHODS: Six hundred and seventy patients (698 lesions) with known or suspected coronary artery disease were screened for this retrospective analysis between January 2020 and July 2021. A total of 40 patients (41 lesions) underwent intravascular ultrasound (IVUS) and FFR evaluations within six months after completing coronary CT angiography were included. Two novel CFD-based models (AccuFFRct and AccuFFRivus) were used to compute the CT-FFR and IVUS-FFR values, respectively. The invasive FFR ≤ 0.80 was used as the reference standard for evaluating the diagnostic performance of CT-FFR and IVUS-FFR. RESULTS: Both AccuFFRivus and AccuFFRct demonstrated a strong correlation with invasive FFR (R = 0.7913, P < 0.0001; and R = 0.6296, P < 0.0001), and both methods showed good agreement with FFR. The area under the receiver operating characteristic curve was 0.960 (P < 0.001) for AccuFFRivus and 0.897 (P < 0.001) for AccuFFRct in predicting FFR ≤ 0.80. FFR ≤ 0.80 were predicted with high sensitivity (96.6%), specificity (85.7%), and the Youden index (0.823) using the same cutoff value of 0.80 for AccuFFRivus. A good diagnostic performance (sensitivity 89.7%, specificity 85.7%, and Youden index 0.754) was also demonstrated by AccuFFRct. CONCLUSIONS: AccuFFRivus, computed from IVUS images, exhibited a high diagnostic performance for detecting myocardial ischemia. It demonstrated better diagnostic power than AccuFFRct, and could serve as an accurate computational tool for ischemia diagnosis and assist in clinical decision-making.

Virtual simulation with AneuShape™ software for microcatheter shaping in intracranial aneurysm coiling: a validation study.

Background: The shaping of an accurate and stable microcatheter plays a vital role in the successful embolization of intracranial aneurysms. Our study aimed to investigate the application and the role of AneuShape™ software in microcatheter shaping for intracranial aneurysm embolization. Methods: From January 2021 to June 2022, 105 patients with single unruptured intracranial aneurysms were retrospectively analyzed with or without AneuShape™ software to assist in microcatheter shaping. The rates of microcatheter accessibility, accurate positioning, and stability for shaping were analyzed. During the operation, fluoroscopy duration, radiation dose, immediate postoperative angiography, and procedure-related complications were evaluated. Results: Compared to the manual group, aneurysm-coiling procedures involving the AneuShape™ software exhibited superior results. The use of the software resulted in a lower rate of reshaping microcatheters (21.82 vs. 44.00%, p = 0.015) and higher rates of accessibility (81.82 vs. 58.00%, p = 0.008), better positioning (85.45 vs. 64.00%, p = 0.011), and higher stability (83.64 vs. 62.00%, p = 0.012). The software group also required more coils for both small (<7 mm) and large (≥7 mm) aneurysms compared to the manual group (3.50 ± 0.19 vs. 2.78 ± 0.11, p = 0.008 and 8.22 ± 0.36 vs. 6.00 ± 1.00, p = 0.081, respectively). In addition, the software group achieved better complete or approximately complete aneurysm obliteration (87.27 vs. 66.00%, p = 0.010) and had a lower procedure-related complication rate (3.60 vs. 12.00%, p = 0.107). Without this software, the operation had a longer intervention duration (34.31 ± 6.51 vs. 23.87 ± 6.98 min, p < 0.001) and a higher radiation dose (750.50 ± 177.81 vs. 563.53 ± 195.46 mGy, p < 0.001). Conclusions: Software-based microcatheter shaping techniques can assist in the precise shaping of microcatheters, reduce operating time and radiation dose, improve embolization density, and facilitate more stable and efficient intracranial aneurysm embolization.