'Super Rehab': can we achieve coronary artery disease regression? A feasibility study protocol.

INTRODUCTION: Patients diagnosed with coronary artery disease (CAD) are currently treated with medications and lifestyle advice to reduce the likelihood of disease progression and risk of future major adverse cardiovascular events (MACE). Where obstructive disease is diagnosed, revascularisation may be considered to treat refractory symptoms. However, many patients with coexistent cardiovascular risk factors, particularly those with metabolic syndrome (MetS), remain at heightened risk of future MACE despite current management.Cardiac rehabilitation is offered to patients post-revascularisation, however, there is no definitive evidence demonstrating its benefit in a primary prevention setting. We propose that an intensive lifestyle intervention (Super Rehab, SR) incorporating high-intensity exercise, diet and behavioural change techniques may improve symptoms, outcomes, and enable CAD regression.This study aims to examine the feasibility of delivering a multicentre randomised controlled trial (RCT) testing SR for patients with CAD, in a primary prevention setting. METHODS AND ANALYSIS: This is a multicentre randomised controlled feasibility study of SR versus usual care in patients with CAD. The study aims to recruit 50 participants aged 18-75 across two centres. Feasibility will be assessed against rates of recruitment, retention and, in the intervention arm, attendance and adherence to SR. Qualitative interviews will explore trial experiences of study participants and practitioners. Variance of change in CAD across both arms of the study (assessed with serial CT coronary angiography) will inform the design and power of a future, multi-centre RCT. ETHICS AND DISSEMINATION: Ethics approval was granted by South West-Frenchay Research Ethics Committee (reference: 21/SW/0153, 18 January 2022). Study findings will be disseminated via presentations to relevant stakeholders, national and international conferences and open-access peer-reviewed research publications. TRIAL REGISTRATION NUMBER: ISRCTN14603929.

Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT.

OBJECTIVE: To assess the diagnostic accuracy and clinical impact of automated artificial intelligence (AI) measurement of thoracic aorta diameter on routine chest CT. METHODS: A single-centre retrospective study involving three cohorts. 210 consecutive ECG-gated CT aorta scans (mean age 75 ± 13) underwent automated analysis (AI-Rad Companion Chest CT, Siemens) and were compared to a reference standard of specialist cardiothoracic radiologists for accuracy measuring aortic diameter. A repeated measures analysis tested reporting consistency in a second cohort (29 patients, mean age 61 ± 17) of immediate sequential pre-contrast and contrast CT aorta acquisitions. Potential clinical impact was assessed in a third cohort of 197 routine CT chests (mean age 66 ± 15) to document potential clinical impact. RESULTS: AI analysis produced a full report in 387/436 (89%) and a partial report in 421/436 (97%). Manual vs AI agreement was good to excellent (ICC 0.76-0.92). Repeated measures analysis of expert and AI reports for the ascending aorta were moderate to good (ICC 0.57-0.88). AI diagnostic performance crossed the threshold for maximally accepted limits of agreement (>5 mm) at the aortic root on ECG-gated CTs. AI newly identified aortic dilatation in 27% of patients on routine thoracic imaging with a specificity of 99% and sensitivity of 77%. CONCLUSION: AI has good agreement with expert readers at the mid-ascending aorta and has high specificity, but low sensitivity, at detecting dilated aortas on non-dedicated chest CTs. ADVANCES IN KNOWLEDGE: An AI tool may improve the detection of previously unknown thoracic aorta dilatation on chest CTs vs current routine reporting.

Impact of an ultra-low dose unenhanced planning scan on CT coronary angiography scan length and effective radiation dose.

Objective: Imaged scan length (z-axis coverage) is a simple parameter that can reduce CT dose without compromising image quality. In CT coronary angiography (CTCA), z-axis coverage may be planned using non-contrast calcium score scan (CaCS) to identify the relevant coronary anatomy. However, standardised Agatston CaCS is acquired at 120 kV which adds a relatively high contribution to total study dose and CaCS is no longer routinely recommended in UK guidelines. We evaluate an ultra-low dose unenhanced planning scan on CTCA scan length and effective radiation dose. Methods: An ultra-low dose tin filter (Sn-filter) planning scan (100 kVp, maximum iterative reconstruction) was performed and used to plan the z-axis coverage on 48 consecutive CTCAs (62% men, 62 ± 13 years) compared with 47 CTCA planned using a localiser alone (46% men, 59 ± 12 years) between May and June 2019. Excess scanning beyond the ideal scan length was calculated for both groups. Estimations of radiation dose were also compared between the two groups. Results: Addition of an ultra-low dose unenhanced planning scan to CTCA protocol was associated with reduction in overscanning with no impact on image quality. There was no significant difference in total study effective dose with the addition of the planning scan, which had an average dose-length product of 3 mGy.cm. (total study dose: Protocol A 2.1 mSv vs Protocol B 2.2 mSv, p = 0.92). Conclusion: An ultra-low dose unenhanced planning scan facilitates optimal scan length for the diagnostic CTCA, reducing overscanning and preventing incomplete cardiac imaging with no significant dose penalty or impact on image quality. Advances in knowledge: An ultra-low dose CTCA planning is feasible and effective at optimising scan length.

Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging.

OBJECTIVES: To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard. METHODS: Consecutive patients with contrast-enhanced CT thorax and CMR within 31 days (2016-2020) were analysed (n = 84). LV dilatation was defined against age-, sex- and body surface area-specific values for CMR. CTs underwent automated artificial intelligence(AI)-derived analysis that segmented ventricular chambers, presenting maximal LV diameter and volume. Area under the receiver operator curve (AUC-ROC) analysis identified CT thresholds with ≥90% sensitivity and highest specificity and ≥90% specificity with highest sensitivity. Youden's Index was used to identify thresholds with optimised sensitivity and specificity. RESULTS: Automated diameter analysis was feasible in 92% of cases (77/84; 45 men, age 61 ± 14 years, mean CT to CMR interval 10 ± 8 days). Relative to CMR as a reference standard, 45% had LV dilatation. In males, an automated LV diameter measurement of ≥55.5 mm was ≥90% specific for CMR-defined LV dilatation (positive predictive value (PPV) 85.7%, negative predictive value (NPV) 61.2%, accuracy 68.9%). In females, an LV diameter of ≥49.7 mm was ≥90% specific for CMR-defined LV dilatation (PPV 66.7%, NPV 73.1%, accuracy 71.9%). AI CT volumetry data did not significantly improve AUC performance. CONCLUSION: Fully automated AI-derived analysis LV dilatation on routine unselected non-gated contrast-enhanced CT thorax studies is feasible. We have defined thresholds for the detection of LV dilatation on CT relative to CMR, which could be used to routinely screen for dilated cardiomyopathy at the time of CT. ADVANCES IN KNOWLEDGE: We show, for the first time, that a fully-automated AI-derived analysis of maximal LV chamber axial diameter on non-ECG-gated thoracic CT is feasible in unselected real-world cases and that the derived measures can predict LV dilatation relative to cardiac magnetic resonance imaging, the non-invasive reference standard for determining cardiac chamber size. We have derived sex-specific cut-off values to screen for LV dilatation on routine contrast-enhanced thoracic CT. Future work should validate these thresholds and determine if technology can alter clinical outcomes in a cost-effective manner.

Spontaneous coronary artery dissection in cutis laxa.

We describe the case of a 21-year-old female with Cutis Laxa presenting with an acute coronary syndrome. A CT coronary angiogram (CTCA) diagnosed spontaneous coronary artery dissection (SCAD) of the right coronary artery, which was successfully managed with medical therapy. Cutis Laxa is a rare connective-tissue disorder in which the skin becomes inelastic. Lax, redundant skin hangs in folds give a prematurely aged appearance and several forms of the disease have been described. Although certain cardiovascular complications are recognised in Cutis Laxa, to our knowledge SCAD has not been previously described. SCAD is an uncommon cause of acute coronary syndrome and sudden cardiac death. The condition particularly affects young females, those with connective tissue diseases, arteriopathies, pregnant females, contraceptive use and cocaine use. Atherosclerotic risk factors are seldom reported. The condition is underdiagnosed as symptoms may not generate a high index of clinical suspicion in this demographic. Diagnosis is traditionally made on invasive coronary angiogram although the procedure carries risks in SCAD and non-invasive CTCA should be considered in appropriately selected patient cohorts or as an adjunctive measure to assess for extracoronary vascular abnormalities. Our patient was diagnosed on CTCA, avoiding the need for invasive catheter angiogram.

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.

OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH). METHODS: Searches of Medline, Embase and Web of Science were undertaken on 1 July 2020. Original publications studying AI applied to cross-sectional imaging for the diagnosis of acquired PAH in adults were identified through two-staged double-blinded review. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies and Checklist for Artificial Intelligence in Medicine frameworks. Narrative synthesis was undertaken following Synthesis Without Meta-Analysis guidelines. This review received no funding and was registered in the International Prospective Register of Systematic Reviews (ID:CRD42020196295). RESULTS: Searches returned 476 citations. Three retrospective observational studies, published between 2016 and 2020, were selected for data-extraction. Two methods applied to cardiac-MRI demonstrated high diagnostic accuracy, with the best model achieving AUC=0.90 (95% CI: 0.85-0.93), 89% sensitivity and 81% specificity. Stronger results were achieved using cardiac-MRI for classification of idiopathic PAH, achieving AUC=0.97 (95% CI: 0.89-1.0), 96% sensitivity and 87% specificity. One study reporting CT-based AI demonstrated lower accuracy, with 64.6% sensitivity and 97.0% specificity. CONCLUSIONS: Automated methods for identifying PAH on cardiac-MRI are emerging with high diagnostic accuracy. AI applied to cross-sectional imaging may provide non-invasive support to reduce diagnostic delay in PAH. This would be helped by stronger solutions in other modalities. ADVANCES IN KNOWLEDGE: There is a significant shortage of research in this important area. Early detection of PAH would be supported by further research advances on the promising emerging technologies identified.

The corrected left ventricular ejection fraction: a potential new measure of ventricular function.

Left ventricular ejection fraction (LVEF) has a limited role in predicting outlook in heart diseases including heart failure. We quantified the independent geometric factors that determine LVEF using cardiac MRI and sought to provide an improved measure of ventricular function by adjusting for such independent variables. A mathematical model was used to analyse the independent effects of structural variables and myocardial shortening on LVEF. These results informed analysis of cardiac MRI data from 183 patients (53 idiopathic dilated cardiomyopathy (DCM), 36 amyloidosis, 55 hypertensives and 39 healthy controls). Left ventricular volumes, LVEF, wall thickness, internal dimensions and longitudinal and midwall fractional shortening were measured. The modelling demonstrated LVEF increased in a curvilinear manner with increasing mFS and longitudinal shortening and wall thickness but decreased with increasing internal diameter. Controls in the clinical cohort had a mean LVEF 64  ±  7%, hypertensives 66  ±  8%, amyloid 49 ±  16% and DCM 30  ±  11%. The mean end-diastolic wall thickness in controls was 8  ±  1 mm, DCM 8  ±  1 mm, hypertensives 11  ±  3 mm and amyloid 14  ±  3 mm, P < 0.0001). LVEF correlated with absolute wall thickening relative to ventricular size (R2 = 0.766). A regression equation was derived from raw MRI data (R2 = 0.856) and used to 'correct' LVEF (EFc) by adjusting the wall thickness and ventricular size to the mean of the control group. Improved quantification of the effects of geometric changes and strain significantly enhances understanding the myocardial mechanics. The EFc resulted in reclassification of a 'ventricular function' in some individuals and may provide an improved measure of myocardial performance especially in thick-walled, low-volume ventricles.

Don't get caught out! A rare case of a calcified urachal remnant mimicking a bladder calculus.

Computer tomography through the kidneys, ureters and bladder (CT KUB) is the mainstay investigation of suspected renal tract calculi. However, several pathologies other than renal tract calculi can cause apparent urinary bladder calcification. We describe the case of a 45 year old man who presented with left sided renal colic. Prone CT KUB performed on admission revealed a calcified urachal remnant mimicking a urinary bladder calculus in the dependent portion of the urinary bladder, confirmed by reviewing the multi-planar reformatted images. This is the first reported case in the literature of this phenomenon. We discuss the importance of using multi-planar reformatted images (MPR) and maximum intensity projection images (MIP), as well as careful review of previous imaging, in making the correct diagnosis. We also discuss the differential diagnoses that should be considered when presented with urinary bladder calcification.