Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Association of coronary inflammation with plaque vulnerability and fractional flow reserve in coronary artery disease.

BACKGROUND: The fat attenuation index (FAI) measured using coronary computed tomography angiography (CCTA) enables the direct evaluation of pericoronary adipose tissue composition and vascular inflammation. We aimed to investigate the association of fractional flow reserve (FFR) and plaque vulnerability with coronary inflammation. METHODS: Patients with suspected coronary artery disease (CAD) who underwent CCTA and invasive FFR measurements within 90-day were included. A cloud-based medical device, CaRi-Heart, serves as a surrogate tool for evaluating coronary inflammation based on FAI by analyzing CCTA images. The correlations between CCTA-defined plaque characteristics, invasive coronary angiographic and physiologic assessments, and CaRi-Heart risk were analyzed. The primary endpoint was the patient-oriented composite outcome (POCO) consisting of all-cause death, any myocardial infarction, and any revascularization. RESULTS: A total of 564 patients (median age 67.0 years; 75.4 â€‹% men) were included. There were no significant differences in quantitative and qualitative plaque characteristics or FFR between the high- and low-CaRi-Heart risk groups (i.e., ≥5 â€‹% and <5 â€‹%). During the median follow-up of 3.2 years [1.13-4.73 years], CaRi-Heart risk ≥5 â€‹% was associated with a significantly higher rate of POCO compared to CaRi-Heart risk <5 â€‹% (0.9 â€‹% vs. 10.1 â€‹%, P â€‹= â€‹0.037). The CaRi-Heart risk was an independent predictor of POCO as a continuous (adjusted HR 1.016, 95 â€‹% CI 1.005-0.027, P â€‹= â€‹0.004) and categorical variable (CaRi-Heart risk ≥5 â€‹%, adjusted HR 2.949, 95 â€‹% CI 1.182-7.360, P â€‹= â€‹0.021), regardless of high-risk plaque characteristics and FFR. CONCLUSION: Coronary inflammation risk assessed using CaRi-Heart risk provides independent prognostic information regardless of plaque vulnerability and physiologic stenosis in patients with CAD.

Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine Coronary Computed Tomography Angiography.

AIMS: Coronary Computed Tomography Angiography (CCTA) is a first line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We assessed the lifetime cost-effectiveness of integrating a novel artificial intelligence-enhanced image analysis algorithm (AI-Risk) that stratifies the risk of cardiac events by quantifying coronary inflammation, combined with the extent of coronary artery plaque and clinical risk factors, by analysing images from routine CCTA. METHODS AND RESULTS: A hybrid decision-tree with population cohort Markov model was developed from 3,393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median(IQR) of 7.7(6.4-9.1) years. In a prospective real-world evaluation survey of 744 consecutive patients undergoing CCTA for chest pain investigation, the availability of AI-Risk assessment led to treatment initiation or intensification in 45% of patients. In a further prospective study of 1,214 consecutive patients with extensive guideline recommended cardiovascular risk profiling, AI-Risk stratification led to treatment initiation or intensification in 39% of patients beyond the current clinical guideline recommendations. Treatment guided by AI-Risk modelled over a lifetime horizon could lead to fewer cardiac events (relative reductions of 4%, 4%, 11%, and 12% for myocardial infarction, ischaemic stroke, heart failure and cardiac death, respectively). Implementing AI-Risk classification in routine interpretation of CCTA is highly likely to be cost-effective (Incremental cost-effectiveness ratio £1,371-3,244), both in scenarios of current guideline compliance or when applied only to patients without obstructive CAD. CONCLUSIONS: Compared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost effective, by refining risk guided medical management.

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.

BACKGROUND: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented. OBJECTIVES: This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care. METHODS: The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value. RESULTS: External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post-cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01). CONCLUSIONS: Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.

Quantitative analysis of optical coherence tomography and histopathology images of normal and dysplastic oral mucosal tissues.

Selecting the most representative site for biopsy is crucial in establishing a definitive diagnosis of oral epithelial dysplasia. The current process involves clinical examination that can be subjective and prone to sampling errors. The aim of this study was therefore to investigate the use of optical coherence tomography (OCT) for differentiation of normal and dysplastic oral epithelial samples, with a view to developing an objective and reproducible approach for biopsy site selection. Biopsy samples from patients with fibro-epithelial polyps (n = 13), mild dysplasia (n = 2), and moderate/severe dysplasia (n = 4) were scanned at 5-µm intervals using an OCT microscope and subsequently processed and stained with hematoxylin and eosin (H&E). Epithelial differentiation was measured from the rate of change (gradient) of the backscattered light intensity in the OCT signal as a function of depth. This parameter is directly related to the density of optical scattering from the cell nuclei. OCT images of normal oral epithelium showed a clear delineation of the mucosal layers observed in the matching histology. However, OCT images of oral dysplasia did not clearly identify the individual mucosal layers because of the increased density of abnormal cell nuclei, which impeded light penetration. Quantitative analysis on 2D-OCT and histology images differentiated dysplasia from normal control samples. Similar analysis on 3D-OCT datasets resulted in the reclassification of biopsy samples into the normal/mild and moderate/severe groups. Quantitative differentiation of normal and dysplastic lesions using OCT offers a non-invasive objective approach for localizing the most representative site to biopsy, particularly in oral lesions with similar clinical features.

Optical coherence tomography systems for evaluation of marginal and internal fit of ceramic reconstructions.

Purpose: To evaluate the marginal and internal fit of lithium disilicate and zirconia crowns using two optical coherence tomography (OCT) systems in order to estimate inter-system variations. Materials and methods: Ten lithium disilicate and 10 cubic stabilized zirconia crowns were placed on prepared artificial teeth without cement. Marginal discrepancy and internal cement gap of the crowns were assessed on images obtained using a swept source OCT (SS-OCT) and a spectral domain OCT (SD-OCT). Medians and interquartile ranges were calculated for both materials and OCT systems. Thereafter, Wilcoxon signed rank test was carried out. Results: No significant difference was found between the two OCT systems for absolute marginal discrepancy of either lithium disilicate (SS-OCT: 182 µm, SD-OCT: 214 µm; p = .922) or zirconia crowns (SS-OCT: 116 µm, SD-OCT: 121 µm; p = .232). Regarding internal cement gap, no significant difference was found between the two OCT systems for lithium disilicate crowns (SS-OCT: 128 µm, SD-OCT: 128 µm; p = .064). However, for zirconia crowns the SD-OCT showed significantly higher (p = .027) internal cement gap (92 µm) than the SS-OCT (68 µm). Moreover, it was not possible to assess the internal fit of zirconia crowns in 47% and 34% of the sites using SD-OCT and SS-OCT, respectively. Conclusions: No significant difference was noted in the ability of SS-OCT and SD-OCT to assess the marginal and internal fit of lithium disilicate crowns, nor the marginal fit of zirconia crowns. On the contrary, drawbacks regarding the assessment of internal fit of zirconia crowns using both OCT systems were observed.

Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device.

AIMS: Coronary computed tomography angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular fat attenuation index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardized FAI mapping together with clinical risk factors and plaque metrics to provide individualized cardiovascular risk prediction. METHODS AND RESULTS: The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the USA (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardized interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological, and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model [Δ(C-statistic) of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort] and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. CONCLUSION: Mapping of perivascular FAI on CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which allows standardized measurement of coronary inflammation by calculating the FAI-Score of each coronary artery. The CaRi-Heart® device provides a reliable prediction of the patient's absolute risk for a fatal cardiac event by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a dual diagnostic and prognostic tool among patients referred for CCTA.

Experimental hydrophilic vinyl polysiloxane (VPS) impression materials incorporating a novel surfactant compared with commercial VPS.

OBJECTIVES: To formulate experimental hydrophilic (Exp) VPS impression materials incorporating a novel surfactant (Rhodasurf CET-2), and to compare their contact angles (CAs) with commercial materials, before/after disinfection. METHODS: CAs were measured immediately after setting and after disinfection (1% NaOCl; 30min and 24h), together with their change whilst a droplet remained on the materials surface (over 10, 20, 30 60 and 120s), on three commercial (Aquasil Ultra-Monophase [Aq M], Elite HD-Monophase [Elt M], Extrude Medium-bodied [Extr M]) and four experimental (Exp I-IV) materials, using the Drop Shape Analysis 100 technique. The results were compared statistically. RESULTS: CAs of all experimental materials were within the range of those obtained for the commercial materials, with the exception of Exp-IV, which presented with the lowest CAs at the three time points. The control Exp-I was hydrophobic at all three time points (CAs ∼100+), as was Elite. Immediately after setting, Aq M had low CAs but these increased significantly after 30min of disinfection. After twenty four hours' disinfection CAs of all Exp/commercial VPS increased significantly compared to immediately after setting. The CAs of droplets left on the material (120s) decreased with time, even after disinfection, except for Exp-I. SIGNIFICANCE: The novel surfactant Rhodasurf CET-2 in Exp-III and IV, is an effective surfactant, retaining a low CA after disinfection, compared with Igepal CO-530 in Aq M. Disinfecting VPS impression materials for more than 30min increases their surface CAs, and therefore prolonged disinfection periods should be avoided.

Scattering attenuation microscopy of oral epithelial dysplasia.

We present a new method for quantitative visualization of premalignant oral epithelium called scattering attenuation microscopy (SAM). Using low-coherence interferometry, SAM projects measurements of epithelial optical attenuation onto an image of the tissue surface as a color map. The measured attenuation is dominated by optical scattering that provides a metric of the severity of oral epithelial dysplasia (OED). Scattering is sensitive to the changes in size and distribution of nuclear material that are characteristic of OED, a condition recognized by the occurrence of basal-cell-like features throughout the epithelial depth. SAM measures the axial intensity change of light backscattered from epithelial tissue. Scattering measurements are obtained from sequential axial scans of a 3-D tissue volume and displayed as a 2-D SAM image. A novel segmentation method is used to confine scattering measurement to epithelial tissue. This is applied to oral biopsy samples obtained from 19 patients. Our results show that imaging of tissue scattering can be used to discriminate between different dysplastic severities and furthermore presents a powerful tool for identifying the most representative tissue site for biopsy.

Elastographic contrast generation in optical coherence tomography from a localized shear stress.

A technique for generating contrast in two-dimensional shear strain elastograms from a localized stress is presented. The technique involves generating a non-uniform, localized stress via a magnetically actuated implant. Its effectiveness is demonstrated using finite-element simulations and a phantom study provides experimental verification of this. The method is applied to a superficial cancerous lesion model represented as a stiff inclusion in normal tissue. The lesion was best distinguished from its surroundings using total shear strain elastograms, rather than individual strain components. In experimental phantom studies, the lesion was imaged using optical coherence tomography (OCT) and could still be distinguished in elastograms when not readily identifiable in standard OCT images.