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Abstract Background Although APOE ε4 allele carriage confers a risk for coronary artery disease, its persistence in humans might be explained by certain survival advantages (antagonistic pleiotropy). Methods Combining data from ~ 37,000 persons from three older age British cohorts (1946 National Survey of Health and Development [NSHD], Southall and Brent Revised [SABRE], and UK Biobank) and one younger age cohort (Avon Longitudinal Study of Parents and Children [ALSPAC]), we explored whether APOE ε4 carriage associates with beneficial or unfavorable left ventricular (LV) structural and functional metrics by echocardiography and cardiovascular magnetic resonance (CMR). Results Compared to the non-APOE ε4 group, APOE ε4 carriers had similar cardiac phenotypes in terms of LV ejection fraction, E/e’, posterior wall and interventricular septal thickness, and LV mass. However, they had improved myocardial performance resulting in greater LV stroke volume generation per 1 mL of myocardium (higher myocardial contraction fraction). In NSHD (n = 1467) and SABRE (n = 1187), ε4 carriers had a 4% higher MCF (95% CI 1–7%, p = 0.016) using echocardiography. Using CMR data, in UK Biobank (n = 32,972), ε4 carriers had a 1% higher MCF 95% (CI 0–1%, p = 0.020) with a dose-response relationship based on the number of ε4 alleles. In addition, UK Biobank ε4 carriers also had more favorable radial and longitudinal strain rates compared to non APOE ε4 carriers. In ALSPAC (n = 1397), APOE ε4 carriers aged

Background: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports. Methods: Short-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for “LGE likelihood” on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes—either “high certainty” of whether LGE was present or not, or “low certainty.” The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center. Results: One thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were “high certainty” (255 for LGE, 953 for no LGE), and 437 were “low certainty”. An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86. Conclusion: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.

Abstract Background Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. Methods A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm (‘machine’) performance was compared to three clinicians (‘human’) and a commercial tool (cvi42, Circle Cardiovascular Imaging). Findings Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P

Abstract Purpose A systematic review to determine the effectiveness of intra-arterial anaesthetics on post- operative pain and opioid analgesia requirements in arterial embolisation procedures. Materials and methods A systematic review of the literature was performed (Embase, PubMed, MEDLINE and the Cochrane Library) from inception to 10th August 2020. Randomised controlled trials (RCTs) and cohort studies that utilised intra-arterial anaesthesia during an embolisation procedure for the purposes of pain control were included. Eligibility was assessed by two investigators independently. Results Eight hundred fifty-nine candidate articles were identified, and 9 studies met the inclusion criteria (6 RCTs and 3 retrospective cohort studies). Four studies were of hepatic chemoembolisation and 5 were of uterine artery embolisation. Five hundred twenty-nine patients were treated in total. All studies used lidocaine as the anaesthetic with doses ranging from 20 to 200 mg, and the anaesthetic was delivered varyingly before, during or after embolisation. Pain intensity was converted to a numeric scale from 0 to 10, and opioid doses were converted to milligram morphine equivalent doses. A random-effects meta-analysis model was used to analyse the results of RCTs, and the results of cohort studies were summarised with a narrative synthesis. The meta-analyses suggested that pain scores were reduced by a mean of 1.02 (95% CI − 2.34 to 0.30; p = 0.13) and opioid doses were reduced by a mean of 7.35 mg (95% CI, − 14.77, 0.06; p = 0.05) in the intervention group however neither finding was statistically significant. No serious adverse events were reported. Conclusion Intra-arterial anaesthetic may slightly reduce pain intensity and post-operative opioid consumption following embolisation, however the results are not statistically significant. There is very limited data available on the effect of anaesthetic on length of hospital admission. Whilst no serious adverse events were reported, there are some concerns regarding the effect of lidocaine on the technical success of embolisation procedures that preclude our recommendation for routine use in embolisation procedures. High quality randomised controlled trials are required to elucidate the dose-response effect of lidocaine on opioid consumption and pain following embolisation, particularly in the first few hours post-operatively, as well as effects on duration of hospital stay.