Cardiac CT assessment of left ventricular mass in mid-diastasis and its prognostic value

Quantifying anatomically-based in-silico electrocardiogram variability for cardiac digital twins

Human cardiac Cardiac digital twins (CDTs) are digital replicas of patient hearts, designed to match clinical observations precisely. The electro-cardiogram (ECG), as the most common non-invasive electrophysiology (EP) measurement, has been recently successfully employed for calibrating CDT. However, ECG-based calibration methods often fail to account for the inherent uncertainties in clinical data acquisition and CDT anatomical generation workflows. As a result, discrepancies inevitably arise between the actual physical and simulated patient EP and ECG. In this study, we aim to qualitatively and quantitatively analyze the impact of these uncertainties on ECG morphology and diagnostic markers, and therefore to assess the reliability of ECG-based CDT calibration. We analyze residual beat-to-beat variability in ECG recordings obtained from three datasets, including healthy subjects and patients treated for ventricular tachycardia and atrial fibrillation. Using a biophysically detailed and anatomically accurate computational model of whole-heart EP combined with a detailed torso model calibrated to closely replicate measured ECG signals, we vary anatomical factors (heart location, orientation, size), heterogeneity in electrical conductivities in the heart and torso, and electrode placements across ECG leads to assess their qualitative impact on ECG morphology. Our study demonstrates that diagnostically relevant ECG features and overall morphology remain close to the ground through ECG independently of the investigated uncertainties. This resilience is consistent with the narrow distribution of ECG due to residual beat-to-beat variability observed in both healthy subjects and patients. Overall, our results suggest that observation uncertainties do not impede an accurate calibration of the CDT.

Value of Advanced Cardiac CTA in Clinical Assessment of Hypertrophic Cardiomyopathy: A Literature Review and Practical Implications

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiac anomaly with a potentially unfavorable clinical outcome. The essential role of multimodality imaging in HCM is well recognized by major professional cardiac imaging societies and has been incorporated into the HCM clinical practice guidelines. Appropriate utilization of cardiac imaging tools is cardinal for accurate diagnosis and appropriate management for HCM patients to mitigate their lifelong risk of adverse events. Echocardiography is the imaging modality of choice for clinical diagnosis of HCM. Cardiac magnetic resonance (CMR) and coronary computed tomography angiogram (CCTA) offer complementary practical information for an inclusive evaluation in such patients. CCTA provides a thorough analysis of the cardiac anatomy and function that is paramount in HCM clinical decision-making. This review summarizes the utility of CCTA in the clinical assessment of patients with HCM. It outlines the multi-role of CCTA in HCM, including the quantification of cardiac parameters, myocardial tissue characterization, left ventricular (LV) functional analysis, the definition of cardiac and coronary arteries (CA) anatomy, and the provision of a roadmap for septal reduction therapies (SRT), mitral valve (MV) intervention, and atrial fibrillation (AF) ablation.

Clinical utility of systolic left ventricular ejection fraction in atrial fibrillation: Role of prospective ECG-triggered cardiac CT

BACKGROUND

The assessment of left ventricular (LV) systolic function and quantification of LV ejection fraction (LVEF) in patients with atrial fibrillation (AF) can be difficult. We previously demonstrated that LV volume changes over the 100 ms of systole (LVEF100ms) can be used as a measure of LV systolic function.

OBJECTIVE

We sought to evaluate the applicability of LVEF100ms in patients with AF.

METHODS

We screened patients with AF who underwent prospective systolic electrocardiogram-triggered cardiac computed tomography from January 2015 to June 2023. The correlation between LVEF100ms and echocardiography-derived LVEF was assessed. Patients were categorized into 3 groups on the basis of echocardiographic LVEF (≤40%, 40%-55%, and ≥55%), and LVEF100ms was compared among these groups. Receiver operating characteristic curve analysis and Cox proportional hazards models were used to determine the optimal LVEF100ms cutoff for predicting LVEF ≤ 40% and major adverse cardiovascular events (MACE), defined as a composite of cardiac death, myocardial infarction, heart failure hospitalization, and stroke.

RESULTS

Of the total 123 patients, 62 (50.4%) had an LVEF of ≥55%, 40 (32.5%) had an LVEF of 40%-50%, and 21 (17.1%) had an LVEF of ≤40%. LVEF100ms correlated with echocardiography-derived LVEF (P < .001) and differed significantly among groups (P < .001). LVEF100ms ≤ 3.3% predicted LVEF ≤ 40% (area under the curve 0.809; sensitivity 87%; specificity 67%). Patients with an LVEF100ms of ≤3.3% had a higher rate of MACE than did those without (P = .030), and LVEF100ms ≤ 3.3% was an independent predictor of MACE.

CONCLUSION

LVEF100ms can provide a useful indicator of LV dysfunction in patients with AF undergoing prospective electrocardiogram-triggered cardiac computed tomography.

Prognostic value of left ventricular mass measured on coronary computed tomography angiography

BACKGROUND

Left ventricular (LV) mass is a well-established prognostic indicator for cardiovascular risk. Measurement of LV mass on coronary computed tomography angiography (CCTA) is considered optional. We aimed to assess for associations between LV mass measured on CCTA with all-cause mortality (ACM) risk and to determine age- and sex-specific distributions.

METHODS

We evaluated patients without known coronary artery disease (CAD) who underwent CCTA at a single center. We assessed age- and sex-specific distributions (10th, 25th, 50th, 75th, and 90th percentiles) of LV mass index. ACM, the primary endpoint, was recorded over a median period of 5.1 [interquartile range: 1.4-8.4] years. The association between LV mass and mortality risk was assessed using multivariable Cox models adjusted for age, sex, medical history, coronary artery calcium (CAC) score and CCTA stenosis.

RESULTS

4187 patients (mean age: 61.9 ​± ​11.7, 63 ​% male) were included. Male sex, African American ethnicity, Hypertension, CAC>400, and smoking were independent predictors of increased LV mass index. During the median 5.1 years of study follow, 265 (6.3 ​%) deaths occurred. Increased LV mass index percentiles were associated with increased risk of ACM. The addition of LV mass index percentiles improved discrimination and reclassification for mortality prediction over a model with age, sex, conventional risk factors, CAC score and CCTA stenosis severity (X2 improvement: 22.68, NRI: 28 ​%, both p ​< ​0.001).

CONCLUSION

In a large sample of patients without known CAD who underwent CCTA, increased LV mass index provided independent and incremental prognostic value for all-cause mortality. Assessment of LV mass by CCTA, considering age and gender distribution, can be utilized clinically to identify patients with high myocardial mass.

Prognostic value of systolic left ventricular ejection fraction using prospective ECG-triggered cardiac CT

BACKGROUND

Prospective ECG-triggered cardiac computed tomography (CT) imaging limits the ability to assess left ventricular (LV) ejection fraction (EF). We previously developed a new index derived from LV volume changes over 100 ​ms during systole (LVEF100msec) as a surrogate of LV function in patients undergoing prospective ECG-triggered cardiac CT. We sought to evaluate the prognostic value of LVEF100msec.

METHODS

Patients undergoing prospective systolic ECG-triggered cardiac CT were enrolled between January 2015 and September 2022. Each CT was analyzed for LVEF100msec. Area under the curve analysis and Cox proportional hazards models were used to define the best LVEF100msec cut-off and to predict major adverse cardiovascular events (MACE), defined as a composite of all-cause death, cardiac death/arrest, non-fatal myocardial infarction, and stroke.

RESULTS

The study enrolled 313 patients (median age ​= ​58 years, male ​= ​52.4 ​%). During a median follow-up of 924 (660-1365) days, 24 (7.7 ​%) patients had MACE. LVEF100msec was significantly lower in the MACE group compared to the non-MACE group (4.8 ​% vs. 8.3 ​%, p ​= ​0.002). Optimal LVEF100msec cut-off for predicting MACE was 6.3 ​%. MACE-free survival rate was significantly lower in patients with LVEF100msec ≤6.3 ​% than those with >6.3 ​% (p ​< ​0.001). LVEF100msec ≤6.3 ​% was an independent predictor of MACE, with an adjusted hazard ratio of 3.758 (95 ​% CI, 1.543-9.148; p ​= ​0.004). The prognostic value of LVEF100msec was consistent across the various severities of coronary artery disease.

CONCLUSION

LVEF100msec was an independent predictor of adverse events. The implementation of LVEF100msec may improve the prognostic value of prospective ECG-triggered cardiac CT.

AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

Background

Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR).

Objectives

The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR.

Methods

We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR.

Results

A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR.

Conclusions

The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR.

Hypertension and left ventricular hypertrophy

In the initial stage, left ventricular hypertrophy (LVH) is adaptive, but in time, it transforms to maladaptive LVH which is specific for the development of various phenotypes that cause heart failure, initially with preserved, but later with reduced left ventricular ejection fraction. Pathophysiological mechanisms, which are characteristic for remodeling procedure, are numerous and extremely complex, and should be subjected to further research with the aim of making a comprehensive overview of hypertensive heart disease (HHD) and discovering new options for preventing and treating HHD. The contemporary methods, such as cardiac magnetic resonance (CMR) and computed tomography (CT) provide very accurate morphological and functional information on HHD. The objective of this review article is to summarize the available scientific information in terms of prevalence, pathophysiology, diagnostics, prevention, contemporary therapeutic options, as well as to present potential therapeutic solutions based on the research of pathological mechanisms which are at the core of HHD.

Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms

Aims

Cardiac magnetic resonance imaging (MRI) is the gold standard in the assessment of left ventricle (LV) mass and wall thickness. In recent years, cardiac computed tomography angiography (CCTA) has gained widespread usage as an imaging modality. Despite this, limited previous investigations have specifically addressed the potential of CCTA as an alternative modality for quantitative LV assessment. The aim of this study was to compare CCTA derived LV mass and wall thickness with cardiac MRI utilizing machine learning algorithms.

Methods and results

Fifty-seven participants who underwent both CCTA and cardiac MRI were identified. LV mass and wall thickness was calculated using LV contours which were automatically placed using in-house developed machine learning models. Pearson's correlation coefficients were calculated along with Bland-Altman plots to assess the agreement between the LV mass and wall thickness per region on CCTA and cardiac MRI. Inter-observer correlations were tested using Pearson's correlation coefficient. Average LV mass and wall thickness for CCTA and cardiac MRI were 127 g, 128 g, 7, and 8 mm, respectively. Bland-Altman plots demonstrated mean differences and corresponding 95% limits of agreement of -1.26 (25.06; -27.58) and -0.57 (1.78; -2.92), for LV mass and average LV wall thickness, respectively. Mean differences and corresponding 95% limits of agreement for wall thickness per region were -0.75 (1.34; -2.83), -0.58 (2.14; -3.30), and -0.29 (3.21; -3.79) for the basal, mid, and apical regions, respectively. Inter-observer correlations were excellent.

Conclusion

Quantitative assessment of LV mass and wall thickness on CCTA using machine learning algorithms seems feasible and shows good agreement with cardiac MRI.

Association Between Major Adverse Cardiovascular Events and Left Ventricular Mass Index in Patients Who Have Undergone Coronary Computed Tomography Angiography: From the FU-CCTA Registry

Background

Left ventricular mass (LVM) is a predictor of future cardiovascular risk. We determined the association between LVM measured by coronary computed tomography angiography (CCTA) and the prognosis in patients who have undergone CCTA for screening of coronary artery disease (CAD).

Methods

We performed a prospective cohort study. Five hundred twenty consecutive patients who underwent CCTA at Fukuoka University Hospital (FU-CCTA registry) were enrolled. They were clinically suspected of having CAD or had at least one cardiovascular risk factor, and were a follow-up of up to 5 years. Equal to more than 50% of coronary stenosis as assessed by CCTA was diagnosed as CAD. Using CCTA, LVM index (LVMI), LV ejection fraction (LVEF), LV end-diastolic volume (LVEDV) and LV end-systolic volume were measured. The primary endpoint was major adverse cardiovascular events (MACEs: including all causes of death, ischemic stroke, acute myocardial infarction and coronary revascularization). The patients were divided into non-MACEs and MACEs groups.

Results

The non-MACEs and MACEs groups consisted of 478 and 42 patients, respectively. Percent of CAD in the MACEs group was significantly higher than that in the non-MACEs group. The MACEs group showed significantly higher LVMI and tended to have a lower LVEF and LVEDV than the non-MACEs group. Although LVMI was not associated with MACEs in all patients, LVMI was independently associated with MACEs in males (odd ratio: 1.018, 95% confidence interval: 1.002 - 1.035, P = 0.030), but not females.

Conclusions

Evaluation of LVMI by CCTA may be useful for predicting MACEs in males.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.

Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI

Background

Non-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI.

Methods

We enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI. The median interval between coronary CTA and MRI was 22 days (IQR: 3-76). We utilized an nn-Unet AI model that automatically segmented non-contrast CT structures. AI measurement of LV mass was compared to contrast CTA and MRI.

Results

A total of 316 patients (Age: 57.1±16.7, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r=0.84, p<0.001), with no significant differences (136.5±55.3 vs. 139.6±56.9 g, p=0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to MRI, measured LV mass was higher with AI (136.5±55.3 vs. 127.1±53.1 g, p<0.001). There was an excellent correlation between AI and MRI (r=0.85, p<0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and MRI, or AI and MRI.

Conclusions

The AI-based automated estimation of LV mass from non-contrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and MRI.

The estimation of left ventricular function using prospective ECG-triggered coronary CT angiography

BACKGROUND

Coronary computed tomography angiography (CCTA) is vital for diagnosing coronary artery disease; however, prospective ECG-triggered acquisition, minimizing radiation exposure, limits left ventricular (LV) ejection fraction (EF) evaluation. We aimed to assess the feasibility and utility of LVEF100msec, a new index for estimating LV function using volumetric changes during 100 msec within systole.

METHODS

This retrospective study analyzed patients who underwent prospective ECG-triggered CCTA with systolic acquisition between January 2015 and June 2022. The LVEF100msec was calculated using the maximum and minimum LV volumes among the three phases (300, 350, and 400 msec post-QRS) and expressed as a percentage. Patients were classified into normal, mild-moderately reduced, or severely reduced LV function categories based on the reference test. The LVEF100msec was compared among groups, and the optimal cutoff value of LVEF100msec for predicting severe LV dysfunction was investigated.

RESULTS

The study included 271 patients (median age ​= ​58 years, 52% male). LVEF was normal in 188 (69.4%), mild-moderately reduced in 57 (21.0%), and severely reduced in 26 (9.6%) patients. Median LVEF100msec value was 9.0 (6.7-12.6) for normal LV function, 4.7 (3.1-8.8) for mild-moderately reduced, and 2.9 (1.5-3.8) for severely reduced LV function. LVEF100msec values significantly differed among categories (p ​< ​0.001). The optimal LVEF100msec cutoff for severe LV dysfunction was 4.3%, with an AUC of 0.924, sensitivity of 88%, and specificity of 89%.

CONCLUSION

The LVEF100msec may serve as a valuable indicator of severe LV dysfunction.

Cardiac Imaging Biomarkers in Chronic Kidney Disease

Uremic cardiomyopathy (UC), the peculiar cardiac remodeling secondary to the systemic effects of renal dysfunction, is characterized by left ventricular (LV) diffuse fibrosis with hypertrophy (LVH) and stiffness and the development of heart failure and increased rates of cardiovascular mortality. Several imaging modalities can be used to obtain a non-invasive assessment of UC by different imaging biomarkers, which is the focus of the present review. Echocardiography has been largely employed in recent decades, especially for the determination of LVH by 2-dimensional imaging and diastolic dysfunction by pulsed-wave and tissue Doppler, where it retains a robust prognostic value; more recent techniques include parametric assessment of cardiac deformation by speckle tracking echocardiography and the use of 3D-imaging. Cardiac magnetic resonance (CMR) imaging allows a more accurate assessment of cardiac dimensions, including the right heart, and deformation by feature-tracking imaging; however, the most evident added value of CMR remains tissue characterization. T1 mapping demonstrated diffuse fibrosis in CKD patients, increasing with the worsening of renal disease and evident even in early stages of the disease, with few, but emerging, prognostic data. Some studies using T2 mapping highlighted the presence of subtle, diffuse myocardial edema. Finally, computed tomography, though rarely used to specifically assess UC, might provide incidental findings carrying prognostic relevance, including information on cardiac and vascular calcification. In summary, non-invasive cardiovascular imaging provides a wealth of imaging biomarkers for the characterization and risk-stratification of UC; integrating results from different imaging techniques can aid a better understanding of the physiopathology of UC and improve the clinical management of patients with CKD.

Aortic regurgitation: A multimodality approach

Aortic regurgitation (AR) is a common valvular pathology. Multimodality noninvasive cardiovascular imaging is routinely used to assess the mechanism of AR, degree, and its hemodynamic impact on the cardiovascular system. Collecting this information is crucial in establishing the prognosis and in guiding patient management and follow-up. While echocardiography remains the primary test to assess AR, a comprehensive assessment of this valvulopathy can be obtained by combining the information from different techniques. This state-of-the-art review is intended to provide an update ed overview of the applications, strengths, and limits of transthoracic echocardiography, cardiac magnetic resonance, and cardiac computed tomography in patients with AR.

Current Progress of Studies of Coronary CT for Risk Prediction of Major Adverse Cardiovascular Event (MACE)

Cardiovascular disease is a serious threat to human health, and early risk prediction of major adverse cardiovascular event in people suspected of coronary heart disease can help guide prevention and clinical decisions. Coronary computed tomography (CT) is a useful imaging tool for evaluation of coronary heart disease, and its ability to reflect coronary atherosclerosis shows potential value for risk prediction. In recent years, various new techniques and studies of coronary CT have emerged for risk prediction of major adverse cardiovascular event in people suspected of coronary heart disease. We will review the background and current study advances of using coronary artery calcium score, coronary CT angiography, and artificial intelligence in this field.

Artificial intelligence in cardiovascular CT: Current status and future implications

Artificial intelligence (AI) refers to the use of computational techniques to mimic human thought processes and learning capacity. The past decade has seen a rapid proliferation of AI developments for cardiovascular computed tomography (CT). These algorithms aim to increase efficiency, objectivity, and performance in clinical tasks such as image quality improvement, structure segmentation, quantitative measurements, and outcome prediction. By doing so, AI has the potential to streamline clinical workflow, increase interpretative speed and accuracy, and inform subsequent clinical pathways. This review covers state-of-the-art AI techniques in cardiovascular CT and the future role of AI as a clinical support tool.

The relationship between systemic inflammation and increased left ventricular mass is partly mediated by noncalcified coronary artery disease burden in psoriasis

Objective

Increased left ventricular (LV) mass is an important precursor to heart failure. Inflammation plays an important role in increasing LV mass. However, the contribution of subclinical coronary artery disease (CAD) to the inflammation-LV mass relationship is unknown. In subjects with psoriasis, a chronic inflammatory skin disease, we evaluated if systemic inflammation assessed by plasma glycoprotein A (GlycA) associated with LV mass measured on coronary CT angiography (CCTA). Additionally, we analyzed whether this relationship was mediated by early CAD assessed as noncalcified coronary burden (NCB).

Methods

We performed an observational longitudinal study of 213 subjects with psoriasis free of known cardiovascular disease, 189 of whom were followed over one year. All participants had GlycA measurements by nuclear magnetic resonance spectroscopy and LV mass and NCB quantified by CCTA.

Results

The cohort had a mean age of 50.3 (±12.9) years and 59% were male. There was moderate psoriasis severity and low cardiovascular risk. LV mass increased by GlycA tertiles [1st tertile:24.6 g/m^2.7(3.8), 2nd tertile:25.5 g/m^2.7(3.8), 3rd tertile:27.7 g/m^2.7(5.5), p<0.001]. Both GlycA (β=0.24, p = 0.001) and NCB (β=0.50, p<0.001) associated with LV mass in models adjusted for age, sex, hypertension, hypertension therapy, lipid therapy, biologic therapy for psoriasis, waist:hip ratio, psoriasis disease duration and severity. In multivariable-adjusted mediation analyses, NCB accounted for 32% of the GlycA-LV mass relationship. Finally, over one year, change in NCB independently associated with change in LV mass (β=0.25, p = 0.002).

Conclusions

Both systemic inflammation and coronary artery NCB were associated with LV mass beyond cardiovascular risk factors in psoriasis. Furthermore, a substantial proportion of the inflammatory-LV mass relationship was mediated by NCB. These findings underscore the possible contribution of early coronary artery disease to the relationship between systemic inflammation and LV mass.

Early effect of percutaneous coronary intervention of non-left anterior descending artery on coronary flow velocity reserve of left anterior descending artery assessed by transthoracic Doppler echocardiography

Background

Limited data are available regarding the influence of percutaneous coronary intervention (PCI) of non-totally occluded lesions (non-CTO) on the coronary flow of non-target vessels. We sought to investigate the short-term impact of the non-left anterior descending artery (non-LAD) PCI on the coronary flow physiology of LAD using transthoracic Doppler echocardiography (TDE).

Methods and results

We consecutively studied 50 patients who underwent successful PCI of non-LAD and non-CTO lesions and a coronary flow velocity assessment of LAD at rest and maximal hyperemia before and at 2 days after the procedure by TDE. Coronary flow velocity reserve (CFVR) was calculated as the ratio of hyperemic to resting diastolic peak velocity (hDPV/bDPV). We evaluated the changes in LAD coronary flow characteristics after PCI of non-LAD and explored the determinants of the change in LAD-CFVR. The median fractional flow reserve (FFR) of the culprit lesion and the LAD quantitative flow ratio (QFR) were 0.67 and 0.88, respectively. After non-LAD PCI, LAD-CFVR was decreased in 33 patients (66.0%). LAD-CFVR significantly decreased (pre-PCI: 2.41, post-PCI: 2.03, p = 0.001) due to a significant decrease in LAD-hDPV (P = 0.007). The prevalence of impaired LAD-CFVR (≤2.0) significantly increased (pre: 30%, post: 48%, P = 0.027). Multivariable linear regression analysis showed that pre-PCI LAD-CFVR was independent predictor of the change in LAD-CFVR after PCI.

Conclusions

LAD-CFVR significantly decreased after successful non-LAD PCI due to the postprocedural reduction of coronary flow assessed by LAD-hDPV.

Prognostic implications of cardiac damage classification based on computed tomography in severe aortic stenosis

AIMS

An echocardiographic staging system of severe aortic stenosis (AS) based on additional extra-valvular cardiac damage has been associated with prognosis after transcatheter aortic valve implantation (TAVI). Multidetector row computed tomography (MDCT) is key in the evaluation of AS patients undergoing TAVI and can potentially detect extra-valvular cardiac damage. This study aimed at evaluating the prognostic implications of an MDCT staging system of severe AS in patients undergoing TAVI.

METHODS AND RESULTS

A total of 405 patients (80 ± 7 years, 52% men) who underwent full-beat MDCT prior to TAVI were included. The extent of cardiac damage was assessed by MDCT and classified in five categories; Stage 0 (no cardiac damage), Stage 1 (left ventricular damage), Stage 2 (left atrium and mitral valve damage), Stage 3 (right atrial damage), and Stage 4 (right ventricular damage). Twenty-seven (7%) patients were stratified as Stage 0, 96 (24%) as Stage 1, 152 (38%) as Stage 2, 78 (19%) as Stage 3, and 52 (13%) as Stage 4. During a median follow-up of 3.7 (IQR 1.7-5.5) years, 150 (37%) died. On multivariable Cox regression analysis, cardiac damage Stage 3 (HR vs. Stage 0: 4.496, P = 0.039) and Stage 4 (HR vs. Stage 0: 5.565, P = 0.020) were independently associated with all-cause mortality.

CONCLUSION

The MDCT-based staging system of cardiac damage in severe AS effectively identifies the patients who are at higher risk of death after TAVI.

Linking statistical shape models and simulated function in the healthy adult human heart

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.

Prognostic value of pericoronary inflammation and unsupervised machine-learning-defined phenotypic clustering of CT angiographic findings

BACKGROUND

Pericoronary adipose tissue attenuation expressed by fat attenuation index (FAI) on coronary CT angiography (CCTA) reflects pericoronary inflammation and is associated with cardiac mortality.

OBJECTIVE

The aim of this study was to define the sub-phenotypes of coronary CCTA-defined plaque and whole vessel quantification by unsupervised machine learning (ML) and its prognostic impact when combined with pericoronary inflammation.

METHODS

A total of 220 left anterior descending arteries (LAD) with intermediate stenosis who underwent fractional flow reserve (FFR) measurement and CCTA were studied. After removal of outcome and FAI data, the phenotype heterogeneity of CCTA-defined plaque and whole vessel quantification was investigated by unsupervised hierarchical clustering analysis based on Ward's method. Detailed features of CCTA findings were assessed according to the clusters (CS1 and CS2). Major adverse cardiac events (MACE)-free survivals were assessed according to the stratifications by FAI and the clusters.

RESULTS

Compared with CS2 (n = 119), CS1 (n = 101) were characterized by greater vessel size, increased plaque volume, and high-risk plaque features. FAI was significantly higher in CS1. ROC analyses revealed that best cut-off value of FAI to predict MACE was -73.1. Kaplan-Meier analysis revealed that lesions with FAI ≥ -73.1 had a significantly higher risk of MACE. Multivariate Cox proportional hazards regression analysis revealed that age, FAI ≥ -73.1, and the clusters were independent predictors of MACE.

CONCLUSION

Unsupervised hierarchical clustering analysis revealed two distinct CCTA-defined subgroups and discriminated by high-risk plaque features and increased FAI. The risk of MACE differs significantly according to the increased FAI and ML-defined clusters.

Micro-CT scan with virtual dissection of left ventricle is a non-destructive, reproducible alternative to dissection and weighing for left ventricular size

Micro-CT scan images enhanced by iodine staining provide high-resolution visualisation of soft tissues in laboratory mice. We have compared Micro-CT scan-derived left ventricular (LV) mass with dissection and weighing. Ex-vivo micro-CT scan images of the mouse hearts were obtained following staining by iodine. The LV was segmented and its volume was assessed using a semi-automated method by Drishti software. The left ventricle was then dissected in the laboratory and its actual weight was measured and compared against the estimated results. LV mass was calculated multiplying its estimated volume and myocardial specific gravity. Thirty-five iodine-stained post-natal mouse hearts were studied. Mice were of either sex and 68 to 352 days old (median age 202 days with interquartile range 103 to 245 days) at the time of sacrifice. Samples were from 20 genetically diverse strains. Median mouse body weight was 29 g with interquartile range 24 to 34 g. Left Ventricular weights ranged from 40.0 to 116.7 mg. The segmented LV mass estimated from micro-CT scan and directly measured dissected LV mass were strongly correlated (R² = 0. 97). Segmented LV mass derived from Micro-CT images was very similar to the physically dissected LV mass (mean difference = 0.09 mg; 95% confidence interval − 3.29 mg to 3.1 mg). Micro-CT scanning provides a non-destructive, efficient and accurate visualisation tool for anatomical analysis of animal heart models of human cardiovascular conditions. Iodine-stained soft tissue imaging empowers researchers to perform qualitative and quantitative assessment of the cardiac structures with preservation of the samples for future histological analysis.

Quantification of effects of mean blood pressure and left ventricular mass on noninvasive fast fractional flow reserve

While brachial mean blood pressure (MBP) and left ventricular mass (LVM) measured from CTCA are the two CFD simulation input parameters, their effects on noninvasive fractional flow reserve (FFRB) have not been systematically investigated. We demonstrate that inaccurate MBP and LVM inputs differing from patient-specific values could result in misclassification of borderline ischemic lesions. This is important in the clinical application of noninvasive FFR in coronary artery disease diagnosis.

Impact of pericoronary adipose tissue inflammation on left ventricular hypertrophy and regional physiological indices in stable coronary artery disease patients with preserved systolic function

Systemic low-grade inflammation has been shown to be associated with left ventricular hypertrophy (LVH). However, the relationship between pericoronary adipose tissue attenuation (PCATA) and both LVH and regional physiological indices remains unknown. This study aimed to evaluate the association of PCATA with LVH and regional physiological indices in stable coronary artery disease (CAD) patients with preserved systolic function. A total of 114 CAD patients who underwent coronary CT angiography (CTA) and invasive physiological tests showing ischemia due to a single de novo lesion were included in the study. On proximal 40-mm segments of all three major coronary vessels on CTA, PCATA was assessed by the crude analysis of the mean CT attenuation value [- 190 to - 30 Hounsfield units [HU)] and the culprit vessel PCATA was used for the analysis. Regional physiological indices were invasively obtained by pressure-temperature sensor-tipped wire. The patients were divided into three groups by culprit vessel PCATA tertiles, and clinical, CTA-derived, and physiological indices were compared. Univariable and multivariable analyses were further performed to determine the predictors of LVH. Angiographic stenosis severity, culprit lesion locations, culprit vessel fractional flow reserve, coronary flow reserve, index of microcirculatory resistance, total and target vessel coronary calcium score, and biomarkers including high-sensitivity C-reactive protein were not different among the groups. The left ventricular (LV) mass, LV mass index (LVMI), and LV mass at risk were all significantly different in the three groups with the greatest values in the highest tertile group (all, P < 0.05). On multivariable analysis, male gender, NT-proBNP, and PCATA were independent predictors of LVMI. Culprit vessel PCATA was significantly associated with LVMI, but not with regional physiology in CAD patients with functionally significant lesions and preserved systolic function. Our results may offer insight into the pathophysiological mechanisms linking pericoronary inflammation and LVH to worse prognosis.

Left Ventricular Mid-Diastolic Wall Thickness: Normal Values for Coronary CT Angiography

Purpose

To generate normal reference values for left ventricular mid-diastolic wall thickness (LV-MDWT) measured by using CT angiography.

Materials and Methods

LV-MDWT was measured in 2383 consecutive patients, without structural heart disease, undergoing prospective electrocardiographically (ECG) triggered mid-diastolic coronary CT angiography. LV-MDWT was manually measured on automatically segmented short-axis images according to the American Heart Association's 17-segment model. Commercially available automatic software was used to calculate the left ventricular (LV) mass.

Results

Among the 2383 patients, average LV-MDWT was 7.24 mm ± 1.86 (standard deviation [SD]), with the basal anteroseptal segment being the thickest wall (8.71 mm ± 2.19) and the apical inferior segment being the thinnest wall (5.9 mm ± 1.58; P < .001). Over all LV segments, the maximum upper limit, as defined as 2 SD above the mean, was 13.6 mm for men (LV1) and 11.2 mm for women. For men, only the basal anterior segment was above 13 mm. There was a significant difference in average LV-MDWT between women and men with 6.47 mm ± 1.07 and 7.90 mm ± 1.24, respectively (P < .001). Significant differences in LV-MDWT were found in the subgroups aged less than 65 years and greater than or equal to 65 years (P < .001). There was a strong correlation between LV-MDWT and LV mass (P < .001).

Conclusion

Normal sex- and age-specific reference ranges for LV-MDWT in prospective ECG-triggered mid-diastolic coronary CT angiography have been provided. These benchmarks may expand the diagnostic and prognostic roles of CT angiography, beyond its role in the identification of coronary artery disease.© RSNA, 2019.

Clinical determinants of plasma cardiac biomarkers in patients with stable chest pain

OBJECTIVE

Troponin and B-type natriuretic peptide (BNP) concentrations are associated with cardiovascular risk in stable patients. Understanding their determinants and identifying modifiable clinical targets may improve outcomes. We aimed to establish clinical and cardiac determinants of these biomarkers.

METHODS

This was a prespecified substudy from the randomised Scottish Computed Tomography of the Heart trial, which enrolled patients 18-75 years with suspected stable angina between 2010 and 2014 (NCT01149590). We included patients from six centres in whom high-sensitivity troponin I and BNP were measured (Singulex Erenna). Patients with troponin >99th centile upper reference limit (10.2 ng/L) or BNP ≥400 ng/L were excluded to avoid inclusion of patients with myocardial injury or heart failure. Multivariable linear regression models were constructed with troponin and BNP as dependent variables.

RESULTS

In total, 885 patients were included; 881 (99%) and 847 (96%) had troponin and BNP concentrations above the limit of detection, respectively. Participants had a slight male preponderance (n=513; 56.1%), and the median age was 59.0 (IQR 51.0-65.0) years. The median troponin and BNP concentrations were 1.4 (IQR 0.90-2.1) ng/L and 29.1 (IQR 14.0-54.0) ng/L, respectively. Age and atherosclerotic burden were independent predictors of both biomarkers. Male sex, left ventricular mass and systolic blood pressure were independent predictors of increased troponin. In contrast, female sex and left ventricular volume were independent predictors of increased BNP.

CONCLUSIONS

Troponin and BNP are associated with coronary atherosclerosis but have important sex differences and distinct and contrasting associations with CT-determined left ventricular mass and volume.

CLINICAL TRIAL REGISTRATION

NCT01149590; Post-results.

Technical feasibility of semiautomatic three-dimensional threshold-based cardiac computed tomography quantification of left ventricular mass

BACKGROUND

Semiautomatic three-dimensional (3-D) threshold-based cardiac computed tomography (CT) quantification has not been attempted for left ventricular mass.

OBJECTIVE

To evaluate the technical feasibility of semiautomatic 3-D threshold-based cardiac CT quantification of left ventricular mass in patients with various degrees of left ventricular hypertrophy.

MATERIALS AND METHODS

In 99 patients, cardiac CT was utilized to quantify ventricular volume and mass by using a semiautomatic 3-D threshold-based method. Left ventricular mass values were compared between the end-systole and the end-diastole. Volumetric parameters were compared among three left ventricular hypertrophy groups (definite, borderline, none). The reproducibility was assessed. The t-test, one-way analysis of variance and Pearson correlation were used.

RESULTS

There were no technical failures. The left ventricular mass between the two sessions exhibited a small mean difference of 2.3±1.1% (mean±standard deviation). The indexed mass values were significantly higher at the end-systole than at the end-diastole (71.4±42.9 g/m² vs. 65.9±43.3 g/m², P<0.001), with significant correlation (R=0.99, P<0.001). The definite group (83.5±41.3 g/m²) showed statistically significantly higher indexed mass values than the borderline and none groups (64.7±26.9 and 55.6±23.9 g/m², respectively; P<0.03), while demonstrating no statistically significant difference between the latter two groups (P>0.05). Left ventricular volume-mass and mass-volume ratios could be calculated in all three groups.

CONCLUSION

CT quantification of left ventricular mass using semiautomatic 3-D threshold-based segmentation is feasible with high reproducibility and the mass values and its ratios with ventricular volumes may be used in patients with various degrees of left ventricular hypertrophy.

Diagnostic Utility of High Sensitivity Troponins for Echocardiographic Markers of Structural Heart Disease

The conventional use of high-sensitivity troponins (hs-troponins) is for diagnosing myocardial infarction however they also have a role in chronic disease management. This pilot study assessed the relationship of hs-troponins with echocardiographic markers of left ventricular hypertrophy (LVH) and structural heart disease (SHD). Patients undergoing computer gomography (CT) coronary angiogram for low-intermediate risk chest pain and healthy volunteers were recruited. Hs-troponins Singulex I, Abbott I and Roche T and N-terminal pro-brain natriuretic peptide (NT-proBNP) were evaluated in relation to SHD parameters including left ventricular hypertrophy (LVH_Echo) and left atrial enlargement (LAE_Echo) on echocardiography. 78 subjects who underwent echocardiography were included in this study. C-statistics (95% confidence interval) of the four biomarkers for predicting LVH_Echo were 0.84 (0.72–0.92), 0.84 (0.73–0.92), 0.75 (0.63–0.85) and 0.62 (0.49–0.74); for LAE_Echo 0.74 (0.6–0.85), 0.78 (0.66–0.88), 0.55 (0.42–0.67) and 0.68 (0.62–0.85); and composite SHD 0.79 (0.66–0.88), 0.87 (0.75–0.94), 0.62 (0.49–0.73) and 0.74 (0.62–0.84) respectively. Optimal cut points for SHD were >1.2 ng/L, >1.6 ng/L, >8 ng/L and >18 pmol/L respectively. These results advocate the potential role of hs-troponins as screening tools for structural heart disease with theranostic implications.