Imaging of the brain-heart axis: prognostic value in a European setting.

BACKGROUND AND AIMS: Increasing data suggest that stress-related neural activity (SNA) is associated with subsequent major adverse cardiovascular events (MACE) and may represent a therapeutic target. Current evidence is exclusively based on populations from the U.S. and Asia where limited information about cardiovascular disease risk was available. This study sought to investigate whether SNA imaging has clinical value in a well-characterized cohort of cardiovascular patients in Europe. METHODS: In this single-centre study, a total of 963 patients (mean age 58.4 ± 16.1 years, 40.7% female) with known cardiovascular status, ranging from 'at-risk' to manifest disease, and without active cancer underwent 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography between 1 January 2005 and 31 August 2019. Stress-related neural activity was assessed with validated methods and relations between SNA and MACE (non-fatal stroke, non-fatal myocardial infarction, coronary revascularization, and cardiovascular death) or all-cause mortality by time-to-event analysis. RESULTS: Over a maximum follow-up of 17 years, 118 individuals (12.3%) experienced MACE, and 270 (28.0%) died. In univariate analyses, SNA significantly correlated with an increased risk of MACE (sub-distribution hazard ratio 1.52, 95% CI 1.05-2.19; P = .026) or death (hazard ratio 2.49, 95% CI 1.96-3.17; P < .001). In multivariable analyses, the association between SNA imaging and MACE was lost when details of the cardiovascular status were added to the models. Conversely, the relationship between SNA imaging and all-cause mortality persisted after multivariable adjustments. CONCLUSIONS: In a European patient cohort where cardiovascular status is known, SNA imaging is a robust and independent predictor of all-cause mortality, but its prognostic value for MACE is less evident. Further studies should define specific patient populations that might profit from SNA imaging.

Combination of computed tomography angiography with coronary artery calcium score for improved diagnosis of coronary artery disease: a collaborative meta-analysis of stable chest pain patients referred for invasive coronary angiography.

OBJECTIVES: Coronary computed tomography angiography (CCTA) has higher diagnostic accuracy than coronary artery calcium (CAC) score for detecting obstructive coronary artery disease (CAD) in patients with stable chest pain, while the added diagnostic value of combining CCTA with CAC is unknown. We investigated whether combining coronary CCTA with CAC score can improve the diagnosis of obstructive CAD compared with CCTA alone. METHODS: A total of 2315 patients (858 women, 37%) aged 61.1 ± 10.2 from 29 original studies were included to build two CAD prediction models based on either CCTA alone or CCTA combined with the CAC score. CAD was defined as at least 50% coronary diameter stenosis on invasive coronary angiography. Models were built by using generalized linear mixed-effects models with a random intercept set for the original study. The two CAD prediction models were compared by the likelihood ratio test, while their diagnostic performance was compared using the area under the receiver-operating-characteristic curve (AUC). Net benefit (benefit of true positive versus harm of false positive) was assessed by decision curve analysis. RESULTS: CAD prevalence was 43.5% (1007/2315). Combining CCTA with CAC improved CAD diagnosis compared with CCTA alone (AUC: 87% [95% CI: 86 to 89%] vs. 80% [95% CI: 78 to 82%]; p < 0.001), likelihood ratio test 236.3, df: 1, p < 0.001, showing a higher net benefit across almost all threshold probabilities. CONCLUSION: Adding the CAC score to CCTA findings in patients with stable chest pain improves the diagnostic performance in detecting CAD and the net benefit compared with CCTA alone. CLINICAL RELEVANCE STATEMENT: CAC scoring CT performed before coronary CTA and included in the diagnostic model can improve obstructive CAD diagnosis, especially when CCTA is non-diagnostic. KEY POINTS: • The combination of coronary artery calcium with coronary computed tomography angiography showed significantly higher AUC (87%, 95% confidence interval [CI]: 86 to 89%) for diagnosis of coronary artery disease compared to coronary computed tomography angiography alone (80%, 95% CI: 78 to 82%, p < 0.001). • Diagnostic improvement was mostly seen in patients with non-diagnostic C. • The improvement in diagnostic performance and the net benefit was consistent across age groups, chest pain types, and genders.

Impact of sex and gender on post-COVID-19 syndrome, Switzerland, 2020.

BackgroundWomen are overrepresented among individuals with post-acute sequelae of SARS-CoV-2 infection (PASC). Biological (sex) as well as sociocultural (gender) differences between women and men might account for this imbalance, yet their impact on PASC is unknown.AimWe assessed the impact of sex and gender on PASC in a Swiss population.MethodOur multicentre prospective cohort study included 2,856 (46% women, mean age 44.2 ± 16.8 years) outpatients and hospitalised patients with PCR-confirmed SARS-CoV-2 infection.ResultsAmong those who remained outpatients during their first infection, women reported persisting symptoms more often than men (40.5% vs 25.5% of men; p < 0.001). This sex difference was absent in hospitalised patients. In a crude analysis, both female biological sex (RR = 1.59; 95% CI: 1.41-1.79; p < 0.001) and a score summarising gendered sociocultural variables (RR = 1.05; 95% CI: 1.03-1.07; p < 0.001) were significantly associated with PASC. Following multivariable adjustment, biological female sex (RR = 0.96; 95% CI: 0.74-1.25; p = 0.763) was outperformed by feminine gender-related factors such as a higher stress level (RR = 1.04; 95% CI: 1.01-1.06; p = 0.003), lower education (RR = 1.16; 95% CI: 1.03-1.30; p = 0.011), being female and living alone (RR = 1.91; 95% CI: 1.29-2.83; p = 0.001) or being male and earning the highest income in the household (RR = 0.76; 95% CI: 0.60-0.97; p = 0.030).ConclusionSpecific sociocultural parameters that differ in prevalence between women and men, or imply a unique risk for women, are predictors of PASC and may explain, at least in part, the higher incidence of PASC in women. Once patients are hospitalised during acute infection, sex differences in PASC are no longer evident.

Polygenic Risk Scores for Prediction of Subclinical Coronary Artery Disease in Persons With Human Immunodeficiency Virus (HIV): The Swiss HIV Cohort Study.

BACKGROUND: In people with human immunodeficiency virus (HIV) (PWH), individual polygenic risk scores (PRSs) are associated with coronary artery disease (CAD) events. Whether PRSs are associated with subclinical CAD is unknown. METHODS: In Swiss HIV Cohort Study participants of European descent, we defined subclinical CAD as presence of soft, mixed, or high-risk plaque (SMHRP) on coronary computed tomography (CT) angiography, or as participants in the top tertile of the study population's coronary artery calcium (CAC) score, using noncontrast CT. We obtained univariable and multivariable odds ratios (ORs) for subclinical CAD endpoints based on nongenetic risk factors, and validated genome-wide PRSs built from single nucleotide polymorphisms associated with CAD, carotid intima-media thickness (IMT), or longevity in the general population. RESULTS: We included 345 genotyped participants (median age, 53 years; 89% male; 96% suppressed HIV RNA); 172 and 127 participants had SMHRP and CAC, respectively. CAD-associated PRS and IMT-associated PRS were associated with SMHRP and CAC (all P < .01), but longevity PRS was not. Participants with unfavorable CAD-PRS (top quintile) had an adjusted SMHRP OR = 2.58 (95% confidence interval [CI], 1.18-5.67), and a CAC OR = 3.95 (95% CI, 1.45-10.77) vs. bottom quintile. Unfavorable nongenetic risk (top vs. bottom quintile) was associated with adjusted SMHRP OR = 24.01 (95% CI, 9.75-59.11), and a CAC-OR = 65.07 (95% CI, 18.48-229.15). Area under the receiver operating characteristic curve increased when we added CAD-PRS to nongenetic risk factors (SMHRP: 0.75 and 0.78, respectively; CAC: 0.80 and 0.83, respectively). CONCLUSIONS: In Swiss PWH, subclinical CAD is independently associated with an individual CAD-associated PRS. Combining nongenetic and genetic cardiovascular risk factors provided the most powerful subclinical CAD prediction.

Coronary microvascular function in male physicians with burnout and job stress: an observational study.

BACKGROUND: As a professional group, physicians are at increased risk of burnout and job stress, both of which are associated with an increased risk of coronary heart disease that is at least as high as that of other professionals. This study aimed to examine the association of burnout and job stress with coronary microvascular function, a predictor of major adverse cardiovascular events. METHODS: Thirty male physicians with clinical burnout and 30 controls without burnout were included. Burnout was assessed with the Maslach Burnout Inventory and job stress with the effort-reward imbalance and overcommitment questionnaire. All participants underwent myocardial perfusion positron emission tomography to quantify endothelium-dependent (cold pressor test) and endothelium-independent (adenosine challenge) coronary microvascular function. Burnout and job stress were regressed on coronary flow reserve (primary outcome) and two additional measures of coronary microvascular function in the same model while adjusting for age and body mass index. RESULTS: Burnout and job stress were significantly and independently associated with endothelium-dependent microvascular function. Burnout was positively associated with coronary flow reserve, myocardial blood flow response, and hyperemic myocardial blood flow (r partial = 0.28 to 0.35; p-value = 0.008 to 0.035). Effort-reward ratio (r partial = - 0.32 to - 0.38; p-value = 0.004 to 0.015) and overcommitment (r partial = - 0.30 to - 0.37; p-value = 0.005 to 0.022) showed inverse associations with these measures. CONCLUSIONS: In male physicians, burnout and high job stress showed opposite associations with coronary microvascular endothelial function. Longitudinal studies are needed to show potential clinical implications and temporal relationships between work-related variables and coronary microvascular function. Future studies should include burnout and job stress for a more nuanced understanding of their potential role in cardiovascular health.

Long-term impact of myocardial inflammation on quantitative myocardial perfusion-a descriptive PET/MR myocarditis study.

PURPOSE: Whether myocardial inflammation causes long-term sequelae potentially affecting myocardial blood flow (MBF) is unknown. We aimed to assess the effect of myocardial inflammation on quantitative MBF parameters, as assessed by 13N-ammonia positron emission tomography myocardial perfusion imaging (PET-MPI) late after myocarditis. METHODS: Fifty patients with a history of myocarditis underwent cardiac magnetic resonance (CMR) imaging at diagnosis and PET/MR imaging at follow-up at least 6 months later. Segmental MBF, myocardial flow reserve (MFR), and 13N-ammonia washout were obtained from PET, and segments with reduced 13N-ammonia retention, resembling scar, were recorded. Based on CMR, segments were classified as remote (n = 469), healed (inflammation at baseline but no late gadolinium enhancement [LGE] at follow-up, n = 118), and scarred (LGE at follow-up, n = 72). Additionally, apparently healed segments but with scar at PET were classified as PET discordant (n = 18). RESULTS: Compared to remote segments, healed segments showed higher stress MBF (2.71 mL*min-1*g-1 [IQR 2.18-3.08] vs. 2.20 mL*min-1*g-1 [1.75-2.68], p < 0.0001), MFR (3.78 [2.83-4.79] vs. 3.36 [2.60-4.03], p < 0.0001), and washout (rest 0.24/min [0.18-0.31] and stress 0.53/min [0.40-0.67] vs. 0.22/min [0.16-0.27] and 0.46/min [0.32-0.63], p = 0.010 and p = 0.021, respectively). While PET discordant segments did not differ from healed segments regarding MBF and MFR, washout was higher by ~ 30% (p < 0.014). Finally, 10 (20%) patients were diagnosed by PET-MPI as presenting with a myocardial scar but without a corresponding LGE. CONCLUSION: In patients with a history of myocarditis, quantitative measurements of myocardial perfusion as obtained from PET-MPI remain altered in areas initially affected by inflammation. CMR = cardiac magnetic resonance; PET = positron emission tomography; LGE = late gadolinium enhancement.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179). RESULTS: Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both). CONCLUSION: Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.

PURPOSE: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS: From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS: Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS: Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification.

BACKGROUND: Deep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP. METHODS: One hundred patients were consecutively enrolled. Image quality-associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1-99 Agatston score (mildly increased risk), Agatston 100-299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk). RESULTS: In comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%. CONCLUSION: DLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment. KEY POINTS: • In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise. • Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score. • Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.

Coronary microvascular disease in hypertrophic and infiltrative cardiomyopathies.

Pathologic hypertrophy of the cardiac muscle is a commonly encountered phenotype in clinical practice, associated with a variety of structural and non-structural diseases. Coronary microvascular disease is considered to play an important role in the natural history of this pathological phenotype. Non-invasive imaging modalities, most prominently positron emission tomography and cardiac magnetic resonance, have provided insights into the pathophysiological mechanisms of the interplay between hypertrophy and the coronary microvasculature. This article summarizes the current knowledge on coronary microvascular dysfunction in the most frequently encountered forms of pathologic hypertrophy.

Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography.

AIM: The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR). METHODS: Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR. RESULTS: A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion. CONCLUSION: A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.

Left ventricular function and volumes from gated [13N]-ammonia positron emission tomography myocardial perfusion imaging: A prospective head-to-head comparison against CMR using a hybrid PET/MR device.

BACKGROUND: Positron emission tomography (PET) myocardial perfusion imaging (MPI) can be used to evaluate left ventricular (LV) volumes and function. We performed a head-to-head comparison of LV function and volumes obtained simultaneously using [13N]-ammonia-PET and cardiac magnetic resonance (CMR), with the latter serving as the reference standard. METHODS AND RESULTS: In this prospective study, 51 patients underwent [13N]-ammonia-PET MPI and CMR using a hybrid PET/MR device. Left ventricular end-systolic volumes (LVESV), end-diastolic volumes (LVEDV), stroke volumes (LVSV), ejection fractions (LVEF), and segmental wall motion were analyzed for both methods and were compared using correlational and Bland-Altman (BA) analysis; segmental wall motion was compared using ANOVA. The agreement between [13N]-ammonia-PET and CMR for LVEF was good, with minimal bias (- .6%) and narrow BA limits of agreement (- 7.9% to 6.8%), but [13N]-ammonia-PET systematically underestimated LV volumes, with high bias in LVESV (- 11.2 ml), LVEDV (- 28.9 ml), and LVSV (- 17.5 ml). Mean segmental wall motion in [13N]-ammonia-PET differed significantly among the corresponding normokinetic (6.6 ± 2 mm), hypokinetic (5.1 ± 2 mm), and akinetic (3.3 ± 2 mm) segments in CMR (P < .01). CONCLUSION: LVEF and LV wall motion can be accurately assessed using [13N]-ammonia-PET MPI, although LV volumes are significantly underestimated compared to CMR.

Rest/stress myocardial perfusion imaging by positron emission tomography with 18F-Flurpiridaz: A feasibility study in mice.

BACKGROUND: Myocardial perfusion imaging by positron emission tomography (PET-MPI) is the current gold standard for quantification of myocardial blood flow. 18F-flurpiridaz was recently introduced as a valid alternative to currently used PET-MPI probes. Nonetheless, optimum scan duration and time interval for image analysis are currently unknown. Further, it is unclear whether rest/stress PET-MPI with 18F-flurpiridaz is feasible in mice. METHODS: Rest/stress PET-MPI was performed with 18F-flurpiridaz (0.6-3.0 MBq) in 27 mice aged 7-8 months. Regadenoson (0.1 µg/g) was used for induction of vasodilator stress. Kinetic modeling was performed using a metabolite-corrected arterial input function. Image-derived myocardial 18F-flurpiridaz uptake was assessed for different time intervals by placing a volume of interest in the left ventricular myocardium. RESULTS: Tracer kinetics were best described by a two-tissue compartment model. K1 ranged from 6.7 to 20.0 mL·cm-3·min-1, while myocardial volumes of distribution (VT) were between 34.6 and 83.6 mL·cm-3. Of note, myocardial 18F-flurpiridaz uptake (%ID/g) was significantly correlated with K1 at rest and following pharmacological vasodilation for all time intervals assessed. However, while Spearman's coefficients (rs) ranged between 0.478 and 0.681, R2 values were generally low. In contrast, an excellent correlation of myocardial 18F-flurpiridaz uptake with VT was obtained, particularly when employing the averaged myocardial uptake from 20 to 40 min post tracer injection (R2 ≥ 0.98). Notably, K1 and VT were similarly sensitive to pharmacological vasodilation induction. Further, mean stress-to-rest ratios of K1, VT, and %ID/g 18F-flurpiridaz were virtually identical, suggesting that %ID/g 18F-flurpiridaz can be used to estimate coronary flow reserve (CFR) in mice. CONCLUSION: Our findings suggest that a simplified assessment of relative myocardial perfusion and CFR, based on image-derived tracer uptake, is feasible with 18F-flurpiridaz in mice, enabling high-throughput mechanistic CFR studies in rodents.

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

Prognostic Significance of Nonobstructive Left Main Coronary Artery Disease in Patients With and Without Diabetes: Long-Term Outcomes From the CONFIRM Registry.

BACKGROUND: Prognostic significance of non-obstructive left main (LM) disease was recently reported. However, the influence of diabetes mellitus (DM) on event rates in patients with and without non-obstructive LM disease is not well-known. METHODS: We evaluated 27,252 patients undergoing coronary computed tomographic angiography from the COroNary CT Angiography Evaluation For Clinical Outcomes: An InteRnational Multicenter (CONFIRM) Registry. Cumulative long-term incidence of all-cause mortality (ACM) was assessed between DM and non-DM patients by normal or non-obstructive LM disease (1-49% stenosis). RESULTS: The mean age of the study population was 57.6±12.6 years. Of the 27,252 patients, 4,434 (16%) patients had DM. A total of 899 (3%) deaths occurred during the follow-up of 3.6±1.9. years. Compared to patients with normal LM, those with non-obstructive LM had more pronounced overall coronary atherosclerosis and more cardiovascular risk factors. After clinical risk factors, segment involvement score, and stenosis severity adjustment, compared to patients without DM and normal LM, patients with DM were associated with increased ACM regardless of normal (HR 1.48, 95% CI 1.22-1.78, p<0.001) or non-obstructive LM (HR 1.46, 95% CI 1.04-2.04, p=0.029), while nonobstructive LM disease was not associated with increased ACM in patients without DM (HR 0.85, 95% CI 0.67-1.07, p=0.165) and there was no significant interaction between DM and LM status (HR 1.03, 95% CI 0.69-1.54, p=0.879). CONCLUSION: From the CONFIRM registry, we demonstrated that DM was associated with increased ACM. However, the presence of non-obstructive LM was not an independent risk marker of ACM, and there was no significant interaction between DM and non-obstructive LM disease for ACM.

NEMA NU 2-2018 performance evaluation of a new generation 30-cm axial field-of-view Discovery MI PET/CT.

PURPOSE: The DMI PET/CT is a modular silicon photomultiplier-based scanner with an axial field-of-view (FOV) between 15 and 25 cm depending on ring configuration (3, 4, or 5 rings). A new generation of the system includes a reengineered detector module, featuring improved electronics and an additional 6th ring, extending the axial FOV to 30 cm. We report on the performance evaluation of the 6-ring upgraded Generation 2 (Gen2) system while values are also reported for the 5-ring configuration of the very same system prior to the upgrade. METHODS: PET performance was evaluated using the NEMA NU 2-2018 standard for spatial resolution, sensitivity, image quality, count rate performance, timing resolution, and image co-registration accuracy. Patient images were used to assess image quality. RESULTS: The average system sensitivity was measured at 32.76 cps/kBq (~ 47% increase to 5 rings at 22.29 cps/kBq) while noise equivalent count rate peaked at 434.3 kcps corresponding to 23.6 kBq/mL (~ 60% increase to Generation 1 (Gen1) and 39% to Gen2 5 rings). Contrast recovery ranged between 54.5 and 85.8% similar to 5 rings, while the 6 rings provided lower background variability (2.3-8.5% for 5 rings vs 1.9-6.8% for 6 rings) and lower lung error (4.0% for the 5 rings and 3.16% for the 6 rings). Transverse/axial full width at half-maximum (FWHM) at 1 cm (3.79/4.26 mm) and 10 cm (4.29/4.55 mm), scatter fraction (40.2%), energy resolution (9.63%), and time-of-flight (TOF) resolution (389.6 ps at 0 kBq/mL) were in line to previously reported values measured across different system configurations. Improved patient image quality is obtained with the 6 rings compared to the 5 rings, while image quality is retained even at reduced scan times, enabling WB dynamic acquisitions. CONCLUSIONS: The higher sensitivity of the 6-ring DMI compared to the 5-ring configuration may lead to improved image quality of clinical images at reduced scan time. Additionally, it could equally be used to allow improved temporal sampling and/or reduced overall scan time in dynamic acquisitions. Conversely, temporal sampling and scan time could be traded per application to further drive injected dose at lower levels.

Role of sex hormones in modulating myocardial perfusion and coronary flow reserve.

BACKGROUND: A growing body of evidence highlights sex differences in the diagnostic accuracy of cardiovascular imaging modalities. Nonetheless, the role of sex hormones in modulating myocardial perfusion and coronary flow reserve (CFR) is currently unclear. The aim of our study was to assess the impact of female and male sex hormones on myocardial perfusion and CFR. METHODS: Rest and stress myocardial perfusion imaging (MPI) was conducted by small animal positron emission tomography (PET) with [18F]flurpiridaz in a total of 56 mice (7-8 months old) including gonadectomized (Gx) and sham-operated males and females, respectively. Myocardial [18F]flurpiridaz uptake (% injected dose per mL, % ID/mL) was used as a surrogate for myocardial perfusion at rest and following intravenous regadenoson injection, as previously reported. Apparent coronary flow reserve (CFRApp) was calculated as the ratio of stress and rest myocardial perfusion. Left ventricular (LV) morphology and function were assessed by cardiac magnetic resonance (CMR) imaging. RESULTS: Orchiectomy resulted in a significant decrease of resting myocardial perfusion (Gx vs. sham, 19.4 ± 1.0 vs. 22.2 ± 0.7 % ID/mL, p = 0.034), while myocardial perfusion at stress remained unchanged (Gx vs. sham, 27.5 ± 1.2 vs. 27.3 ± 1.2 % ID/mL, p = 0.896). Accordingly, CFRApp was substantially higher in orchiectomized males (Gx vs. sham, 1.43 ± 0.04 vs. 1.23 ± 0.05, p = 0.004), and low serum testosterone levels were linked to a blunted resting myocardial perfusion (r = 0.438, p = 0.020) as well as an enhanced CFRApp (r = -0.500, p = 0.007). In contrast, oophorectomy did not affect myocardial perfusion in females. Of note, orchiectomized males showed a reduced LV mass, stroke volume, and left ventricular ejection fraction (LVEF) on CMR, while no such effects were observed in oophorectomized females. CONCLUSION: Our experimental data in mice indicate that sex differences in myocardial perfusion are primarily driven by testosterone. Given the diagnostic importance of PET-MPI in clinical routine, further studies are warranted to determine whether testosterone levels affect the interpretation of myocardial perfusion findings in patients.

Imaging of heart disease in women: review and case presentation.

Cardiovascular diseases (CVD) remain the leading cause of mortality worldwide. Although major diagnostic and therapeutic advances have significantly improved the prognosis of patients with CVD in the past decades, these advances have less benefited women than age-matched men. Noninvasive cardiac imaging plays a key role in the diagnosis of CVD. Despite shared imaging features and strategies between both sexes, there are critical sex disparities that warrant careful consideration, related to the selection of the most suited imaging techniques, to technical limitations, and to specific diseases that are overrepresented in the female population. Taking these sex disparities into consideration holds promise to improve management and alleviate the burden of CVD in women. In this review, we summarize the specific features of cardiac imaging in four of the most common presentations of CVD in the female population including coronary artery disease, heart failure, pregnancy complications, and heart disease in oncology, thereby highlighting contemporary strengths and limitations. We further propose diagnostic algorithms tailored to women that might help in selecting the most appropriate imaging modality.

Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography.

OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification. METHODS: This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed. RESULTS: Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland-Altman analysis revealed a mean difference of - 0.8 mm3 (± 2.5 mm3) and limits of agreement between - 5.8 and + 4.1 mm3. CONCLUSION: DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume. KEY POINTS: •Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

Computed tomography angiography versus Agatston score for diagnosis of coronary artery disease in patients with stable chest pain: individual patient data meta-analysis of the international COME-CCT Consortium.

OBJECTIVES: There is conflicting evidence about the comparative diagnostic accuracy of the Agatston score versus computed tomography angiography (CTA) in patients with suspected obstructive coronary artery disease (CAD). PURPOSE: To determine whether CTA is superior to the Agatston score in the diagnosis of CAD. METHODS: In total 2452 patients with stable chest pain and a clinical indication for invasive coronary angiography (ICA) for suspected CAD were included by the Collaborative Meta-analysis of Cardiac CT (COME-CCT) Consortium. An Agatston score of > 400 was considered positive, and obstructive CAD defined as at least 50% coronary diameter stenosis on ICA was used as the reference standard. RESULTS: Obstructive CAD was diagnosed in 44.9% of patients (1100/2452). The median Agatston score was 74. Diagnostic accuracy of CTA for the detection of obstructive CAD (81.1%, 95% confidence interval [CI]: 77.5 to 84.1%) was significantly higher than that of the Agatston score (68.8%, 95% CI: 64.2 to 73.1%, p < 0.001). Among patients with an Agatston score of zero, 17% (101/600) had obstructive CAD. Diagnostic accuracy of CTA was not significantly different in patients with low to intermediate (1 to < 100, 100-400) versus moderate to high Agatston scores (401-1000, > 1000). CONCLUSIONS: Results in our international cohort show CTA to have significantly higher diagnostic accuracy than the Agatston score in patients with stable chest pain, suspected CAD, and a clinical indication for ICA. Diagnostic performance of CTA is not affected by a higher Agatston score while an Agatston score of zero does not reliably exclude obstructive CAD. KEY POINTS: • CTA showed significantly higher diagnostic accuracy (81.1%, 95% confidence interval [CI]: 77.5 to 84.1%) for diagnosis of coronary artery disease when compared to the Agatston score (68.8%, 95% CI: 64.2 to 73.1%, p < 0.001). • Diagnostic performance of CTA was not affected by increased amount of calcium and was not significantly different in patients with low to intermediate (1 to <100, 100-400) versus moderate to high Agatston scores (401-1000, > 1000). • Seventeen percent of patients with an Agatston score of zero showed obstructive coronary artery disease by invasive angiography showing absence of coronary artery calcium cannot reliably exclude coronary artery disease.