Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

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.

Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicentre, cross-tracer development and validation study.

BACKGROUND: The diagnosis of cardiac amyloidosis can be established non-invasively by scintigraphy using bone-avid tracers, but visual assessment is subjective and can lead to misdiagnosis. We aimed to develop and validate an artificial intelligence (AI) system for standardised and reliable screening of cardiac amyloidosis-suggestive uptake and assess its prognostic value, using a multinational database of 99mTc-scintigraphy data across multiple tracers and scanners. METHODS: In this retrospective, international, multicentre, cross-tracer development and validation study, 16 241 patients with 19 401 scans were included from nine centres: one hospital in Austria (consecutive recruitment Jan 4, 2010, to Aug 19, 2020), five hospital sites in London, UK (consecutive recruitment Oct 1, 2014, to Sept 29, 2022), two centres in China (selected scans from Jan 1, 2021, to Oct 31, 2022), and one centre in Italy (selected scans from Jan 1, 2011, to May 23, 2023). The dataset included all patients referred to whole-body 99mTc-scintigraphy with an anterior view and all 99mTc-labelled tracers currently used to identify cardiac amyloidosis-suggestive uptake. Exclusion criteria were image acquisition at less than 2 h (99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid, 99mTc-hydroxymethylene diphosphonate, and 99mTc-methylene diphosphonate) or less than 1 h (99mTc-pyrophosphate) after tracer injection and if patients' imaging and clinical data could not be linked. Ground truth annotation was derived from centralised core-lab consensus reading of at least three independent experts (CN, TT-W, and JN). An AI system for detection of cardiac amyloidosis-associated high-grade cardiac tracer uptake was developed using data from one centre (Austria) and independently validated in the remaining centres. A multicase, multireader study and a medical algorithmic audit were conducted to assess clinician performance compared with AI and to evaluate and correct failure modes. The system's prognostic value in predicting mortality was tested in the consecutively recruited cohorts using cox proportional hazards models for each cohort individually and for the combined cohorts. FINDINGS: The prevalence of cases positive for cardiac amyloidosis-suggestive uptake was 142 (2%) of 9176 patients in the Austrian, 125 (2%) of 6763 patients in the UK, 63 (62%) of 102 patients in the Chinese, and 103 (52%) of 200 patients in the Italian cohorts. In the Austrian cohort, cross-validation performance showed an area under the curve (AUC) of 1·000 (95% CI 1·000-1·000). Independent validation yielded AUCs of 0·997 (0·993-0·999) for the UK, 0·925 (0·871-0·971) for the Chinese, and 1·000 (0·999-1·000) for the Italian cohorts. In the multicase multireader study, five physicians disagreed in 22 (11%) of 200 cases (Fleiss' kappa 0·89), with a mean AUC of 0·946 (95% CI 0·924-0·967), which was inferior to AI (AUC 0·997 [0·991-1·000], p=0·0040). The medical algorithmic audit demonstrated the system's robustness across demographic factors, tracers, scanners, and centres. The AI's predictions were independently prognostic for overall mortality (adjusted hazard ratio 1·44 [95% CI 1·19-1·74], p<0·0001). INTERPRETATION: AI-based screening of cardiac amyloidosis-suggestive uptake in patients undergoing scintigraphy was reliable, eliminated inter-rater variability, and portended prognostic value, with potential implications for identification, referral, and management pathways. FUNDING: Pfizer.

Electrophysiological Characterization of Subclinical and Overt Hypertrophic Cardiomyopathy by Magnetic Resonance Imaging-Guided Electrocardiography.

BACKGROUND: Ventricular arrhythmia in hypertrophic cardiomyopathy (HCM) relates to adverse structural change and genetic status. Cardiovascular magnetic resonance (CMR)-guided electrocardiographic imaging (ECGI) noninvasively maps cardiac structural and electrophysiological (EP) properties. OBJECTIVES: The purpose of this study was to establish whether in subclinical HCM (genotype [G]+ left ventricular hypertrophy [LVH]-), ECGI detects early EP abnormality, and in overt HCM, whether the EP substrate relates to genetic status (G+/G-LVH+) and structural phenotype. METHODS: This was a prospective 211-participant CMR-ECGI multicenter study of 70 G+LVH-, 104 LVH+ (51 G+/53 G-), and 37 healthy volunteers (HVs). Local activation time (AT), corrected repolarization time, corrected activation-recovery interval, spatial gradients (GAT/GRTc), and signal fractionation were derived from 1,000 epicardial sites per participant. Maximal wall thickness and scar burden were derived from CMR. A support vector machine was built to discriminate G+LVH- from HV and low-risk HCM from those with intermediate/high-risk score or nonsustained ventricular tachycardia. RESULTS: Compared with HV, subclinical HCM showed mean AT prolongation (P = 0.008) even with normal 12-lead electrocardiograms (ECGs) (P = 0.009), and repolarization was more spatially heterogenous (GRTc: P = 0.005) (23% had normal ECGs). Corrected activation-recovery interval was prolonged in overt vs subclinical HCM (P < 0.001). Mean AT was associated with maximal wall thickness; spatial conduction heterogeneity (GAT) and fractionation were associated with scar (all P < 0.05), and G+LVH+ had more fractionation than G-LVH+ (P = 0.002). The support vector machine discriminated subclinical HCM from HV (10-fold cross-validation accuracy 80% [95% CI: 73%-85%]) and identified patients at higher risk of sudden cardiac death (accuracy 82% [95% CI: 78%-86%]). CONCLUSIONS: In the absence of LVH or 12-lead ECG abnormalities, HCM sarcomere gene mutation carriers express an aberrant EP phenotype detected by ECGI. In overt HCM, abnormalities occur more severely with adverse structural change and positive genetic status.

Technical development and feasibility of a reusable vest to integrate cardiovascular magnetic resonance with electrocardiographic imaging.

BACKGROUND: Electrocardiographic imaging (ECGI) generates electrophysiological (EP) biomarkers while cardiovascular magnetic resonance (CMR) imaging provides data about myocardial structure, function and tissue substrate. Combining this information in one examination is desirable but requires an affordable, reusable, and high-throughput solution. We therefore developed the CMR-ECGI vest and carried out this technical development study to assess its feasibility and repeatability in vivo. METHODS: CMR was prospectively performed at 3T on participants after collecting surface potentials using the locally designed and fabricated 256-lead ECGI vest. Epicardial maps were reconstructed to generate local EP parameters such as activation time (AT), repolarization time (RT) and activation recovery intervals (ARI). 20 intra- and inter-observer and 8 scan re-scan repeatability tests. RESULTS: 77 participants were recruited: 27 young healthy volunteers (HV, 38.9 ± 8.5 years, 35% male) and 50 older persons (77.0 ± 0.1 years, 52% male). CMR-ECGI was achieved in all participants using the same reusable, washable vest without complications. Intra- and inter-observer variability was low (correlation coefficients [rs] across unipolar electrograms = 0.99 and 0.98 respectively) and scan re-scan repeatability was high (rs between 0.81 and 0.93). Compared to young HV, older persons had significantly longer RT (296.8 vs 289.3 ms, p = 0.002), ARI (249.8 vs 235.1 ms, p = 0.002) and local gradients of AT, RT and ARI (0.40 vs 0.34 ms/mm, p = 0,01; 0.92 vs 0.77 ms/mm, p = 0.03; and 1.12 vs 0.92 ms/mm, p = 0.01 respectively). CONCLUSION: Our high-throughput CMR-ECGI solution is feasible and shows good reproducibility in younger and older participants. This new technology is now scalable for high throughput research to provide novel insights into arrhythmogenesis and potentially pave the way for more personalised risk stratification. CLINICAL TRIAL REGISTRATION: Title: Multimorbidity Life-Course Approach to Myocardial Health-A Cardiac Sub-Study of the MRC National Survey of Health and Development (NSHD) (MyoFit46). National Clinical Trials (NCT) number: NCT05455125. URL: https://clinicaltrials.gov/ct2/show/NCT05455125?term=MyoFit&draw=2&rank=1.

Extended Total Temporomandibular Joint Replacement - A Feasible Option for Functional and Aesthetic Reconstruction of Mandibular Defects Involving the Temporomandibular Joint.

Study design: Retrospective case series. Objective: Alloplastic temporomandibular joint replacement has been established as a standard technique for end- stage temporomandibular (TMJ) pathologies. Joint replacement when there are extensive mandibular defects remains a challenging clinical problem. Custom-made extended temporomandibular joint replacement is a feasible option but there is limited information about this emerging technique. Methods: Included were all patients undergoing extended TMJ-replacements (TMJe), all operatrions were carried out by the senior author. Surgical technique was either single stage or two stage protocol. Surgical details and pitfalls and outcome of more than 2 years follow-up with reference to thirteen including twelve patients were recorded. Results: The most common diagnosis was ameloblastoma of the mandibular ramus. Single stage or two stagge regime were carried out depending on resection requirements and involvement of teeth. Improved mouth opening of more than 30mm was achieved in 10 of 12 patients. One patient with previous TMJ replacement reported temporary weakness of the facial nerve, which resolved after 10 months. Conclusions: The authors suggest a simplified anatomically based single-stage or two-stage regime, with both regimes achieved excellent anatomic reconstruction, facial appearance and function with low surgical morbidity. Custom-made extended temporomandibular joint protheses appear an advanced and reliable solution for reconstruction of combined complex mandibular defects including the temporomandibular joint. If surgical clearance of the pathology can be achieved, a single-stage regime is favoured.

Improved Diagnostic Criteria for Apical Hypertrophic Cardiomyopathy.

BACKGROUND: There is no acceptable maximum wall thickness (MWT) threshold for diagnosing apical hypertrophic cardiomyopathy (ApHCM), with guidelines referring to ≥15 mm MWT for all hypertrophic cardiomyopathy subtypes. A normal myocardium naturally tapers apically; a fixed diagnostic threshold fails to account for this. Using cardiac magnetic resonance, "relative" ApHCM has been described with typical electrocardiographic features, loss of apical tapering, and cavity obliteration but also with MWT <15 mm. OBJECTIVES: The authors aimed to define normal apical wall thickness thresholds in healthy subjects and use these to accurately identify ApHCM. METHODS: The following healthy subjects were recruited: healthy UK Biobank imaging substudy subjects (n = 4,112) and an independent healthy volunteer group (n = 489). A clinically defined disease population of 104 ApHCM subjects was enrolled, with 72 overt (MWT ≥15 mm) and 32 relative (MWT <15 mm but typical electrocardiographic/imaging findings) ApHCM subjects. Cardiac magnetic resonance-derived MWT was measured in 16 segments using a published clinically validated machine learning algorithm. Segmental normal reference ranges were created and indexed (for age, sex, and body surface area), and diagnostic performance was assessed. RESULTS: In healthy cohorts, there was no clinically significant age-related difference for apical wall thickness. There were sex-related differences, but these were not clinically significant after indexing to body surface area. Therefore, segmental reference ranges for apical hypertrophy required indexing to body surface area only (not age or sex). The upper limit of normal (the largest of the 4 apical segments measured) corresponded to a maximum apical MWT in healthy subjects of 5.2 to 5.6 mm/m2 with an accuracy of 0.94 (the unindexed equivalent being 11 mm). This threshold was categorized as abnormal in 99% (71/72) of overt ApHCM patients, 78% (25/32) of relative ApHCM patients, 3% (122/4,112) of UK Biobank subjects, and 3% (13/489) of healthy volunteers. CONCLUSIONS: Per-segment indexed apical wall thickness thresholds are highly accurate for detecting apical hypertrophy, providing confidence to the reader to diagnose ApHCM in those not reaching current internationally recognized criteria.

Design and Implementation of an Innovative, Rapid Data-Monitoring Strategy for Public Health Emergencies: Pilot of the United States School COVID-19 Mitigation Strategies Project.

During the COVID-19 pandemic, an urgent need existed for near-real-time data collection to better understand how individual beliefs and behaviors, state and local policies, and organizational practices influenced health outcomes. We describe the processes, methods, and lessons learned during the development and pilot testing of an innovative rapid data collection process we developed to inform decision-making during the COVID-19 public health emergency. We used a fully integrated mixed-methods approach to develop a structured process for triangulating quantitative and qualitative data from traditional (cross-sectional surveys, focus groups) and nontraditional (social media listening) sources. Respondents included students, parents, teachers, and key school personnel (eg, nurses, administrators, mental health providers). During the pilot phase (February-June 2021), data from 12 cross-sectional and sector-based surveys (n = 20 302 participants), 28 crowdsourced surveys (n = 26 820 participants), 10 focus groups (n = 64 participants), and 11 social media platforms (n = 432 754 503 responses) were triangulated with other data to support COVID-19 mitigation in schools. We disseminated findings through internal dashboards, triangulation reports, and policy briefs. This pilot demonstrated that triangulating traditional and nontraditional data sources can provide rapid data about barriers and facilitators to mitigation implementation during an evolving public health emergency. Such a rapid feedback and continuous improvement model can be tailored to strengthen response efforts. This approach emphasizes the value of nimble data modernization efforts to respond in real time to public health emergencies.

Microstructural and Microvascular Phenotype of Sarcomere Mutation Carriers and Overt Hypertrophic Cardiomyopathy.

BACKGROUND: In hypertrophic cardiomyopathy (HCM), myocyte disarray and microvascular disease (MVD) have been implicated in adverse events, and recent evidence suggests that these may occur early. As novel therapy provides promise for disease modification, detection of phenotype development is an emerging priority. To evaluate their utility as early and disease-specific biomarkers, we measured myocardial microstructure and MVD in 3 HCM groups-overt, either genotype-positive (G+LVH+) or genotype-negative (G-LVH+), and subclinical (G+LVH-) HCM-exploring relationships with electrical changes and genetic substrate. METHODS: This was a multicenter collaboration to study 206 subjects: 101 patients with overt HCM (51 G+LVH+ and 50 G-LVH+), 77 patients with G+LVH-, and 28 matched healthy volunteers. All underwent 12-lead ECG, quantitative perfusion cardiac magnetic resonance imaging (measuring myocardial blood flow, myocardial perfusion reserve, and perfusion defects), and cardiac diffusion tensor imaging measuring fractional anisotropy (lower values expected with more disarray), mean diffusivity (reflecting myocyte packing/interstitial expansion), and second eigenvector angle (measuring sheetlet orientation). RESULTS: Compared with healthy volunteers, patients with overt HCM had evidence of altered microstructure (lower fractional anisotropy, higher mean diffusivity, and higher second eigenvector angle; all P<0.001) and MVD (lower stress myocardial blood flow and myocardial perfusion reserve; both P<0.001). Patients with G-LVH+ were similar to those with G+LVH+ but had elevated second eigenvector angle (P<0.001 after adjustment for left ventricular hypertrophy and fibrosis). In overt disease, perfusion defects were found in all G+ but not all G- patients (100% [51/51] versus 82% [41/50]; P=0.001). Patients with G+LVH- compared with healthy volunteers similarly had altered microstructure, although to a lesser extent (all diffusion tensor imaging parameters; P<0.001), and MVD (reduced stress myocardial blood flow [P=0.015] with perfusion defects in 28% versus 0 healthy volunteers [P=0.002]). Disarray and MVD were independently associated with pathological electrocardiographic abnormalities in both overt and subclinical disease after adjustment for fibrosis and left ventricular hypertrophy (overt: fractional anisotropy: odds ratio for an abnormal ECG, 3.3, P=0.01; stress myocardial blood flow: odds ratio, 2.8, P=0.015; subclinical: fractional anisotropy odds ratio, 4.0, P=0.001; myocardial perfusion reserve odds ratio, 2.2, P=0.049). CONCLUSIONS: Microstructural alteration and MVD occur in overt HCM and are different in G+ and G- patients. Both also occur in the absence of hypertrophy in sarcomeric mutation carriers, in whom changes are associated with electrocardiographic abnormalities. Measurable changes in myocardial microstructure and microvascular function are early-phenotype biomarkers in the emerging era of disease-modifying therapy.

Behavioral health and treatment-seeking behaviors among deployed vs. non-deployed service members: How impactful is deployment on well-being?

Increasing attention has been dedicated to studying behavioral health of non-deployed military personnel. This investigation explored the impacts of a variety of sociodemographic and health factors on key behavioral health outcomes among active duty personnel. A secondary analysis was conducted using 2014 Defense Health Agency Health Related Behaviors Survey data (unweighted n = 45,762, weighted n = 1,251,606). Three logistic regression models investigated factors associated with reporting symptomatology consistent with depression, anxiety, and stress. We found that after adjusting for sociodemographic and other health variables (e.g., sleep), deployment was associated with stress but not anxiety or depression. Although deployed personnel were more likely to report increased levels of stress overall, few differences with respect to the sources of stressors were identified. While behavioral health screening and treatment needs may differ for non-deployed and deployed personnel, programs to support mental and physical well-being among all service members should be robustly promoted.

Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives.

INTRODUCTION: Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed. AREAS COVERED: AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies. EXPERT OPINION: The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.

Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging.

Purpose: Neural networks have potential to automate medical image segmentation but require expensive labeling efforts. While methods have been proposed to reduce the labeling burden, most have not been thoroughly evaluated on large, clinical datasets or clinical tasks. We propose a method to train segmentation networks with limited labeled data and focus on thorough network evaluation. Approach: We propose a semi-supervised method that leverages data augmentation, consistency regularization, and pseudolabeling and train four cardiac magnetic resonance (MR) segmentation networks. We evaluate the models on multiinstitutional, multiscanner, multidisease cardiac MR datasets using five cardiac functional biomarkers, which are compared to an expert's measurements using Lin's concordance correlation coefficient (CCC), the within-subject coefficient of variation (CV), and the Dice coefficient. Results: The semi-supervised networks achieve strong agreement using Lin's CCC ( > 0.8 ), CV similar to an expert, and strong generalization performance. We compare the error modes of the semi-supervised networks against fully supervised networks. We evaluate semi-supervised model performance as a function of labeled training data and with different types of model supervision, showing that a model trained with 100 labeled image slices can achieve a Dice coefficient within 1.10% of a network trained with 16,000+ labeled image slices. Conclusion: We evaluate semi-supervision for medical image segmentation using heterogeneous datasets and clinical metrics. As methods for training models with little labeled data become more common, knowledge about how they perform on clinical tasks, how they fail, and how they perform with different amounts of labeled data is useful to model developers and users.

Apical Ischemia Is a Universal Feature of Apical Hypertrophic Cardiomyopathy.

BACKGROUND: Apical hypertrophic cardiomyopathy (ApHCM) accounts for ≈10% of hypertrophic cardiomyopathy cases and is characterized by apical hypertrophy, apical cavity obliteration, and tall ECG R waves with ischemic-looking deep T-wave inversion. These may be present even with <15 mm apical hypertrophy (relative ApHCM). Microvascular dysfunction is well described in hypertrophic cardiomyopathy. We hypothesized that apical perfusion defects would be common in ApHCM. METHODS: A 2-center study using cardiovascular magnetic resonance short- and long-axis quantitative adenosine vasodilator stress perfusion mapping. One hundred patients with ApHCM (68 overt hypertrophy [≥15 mm] and 32 relative ApHCM) were compared with 50 patients with asymmetrical septal hypertrophy hypertrophic cardiomyopathy and 40 healthy volunteer controls. Perfusion was assessed visually and quantitatively as myocardial blood flow and myocardial perfusion reserve. RESULTS: Apical perfusion defects were present in all overt ApHCM patients (100%), all relative ApHCM patients (100%), 36% of asymmetrical septal hypertrophy hypertrophic cardiomyopathy, and 0% of healthy volunteers (P<0.001). In 10% of patients with ApHCM, perfusion defects were sufficiently apical that conventional short-axis views missed them. In 29%, stress myocardial blood flow fell below rest values. Stress myocardial blood flow was most impaired subendocardially, with greater hypertrophy or scar, and with apical aneurysms. Impaired apical myocardial blood flow was most strongly predicted by thicker apical segments (ß-coefficient, -0.031 mL/g per min [CI, -0.06 to -0.01]; P=0.013), higher ejection fraction (-0.025 mL/g per min [CI, -0.04 to -0.01]; P<0.005), and ECG maximum R-wave height (-0.023 mL/g per min [CI, -0.04 to -0.01]; P<0.005). CONCLUSIONS: Apical perfusion defects are universally present in ApHCM at all stages. Its ubiquitous presence along with characteristic ECG suggests ischemia may play a disease-defining role in ApHCM.

Myocardial Involvement After Hospitalization for COVID-19 Complicated by Troponin Elevation: A Prospective, Multicenter, Observational Study.

BACKGROUND: Acute myocardial injury in hospitalized patients with coronavirus disease 2019 (COVID-19) has a poor prognosis. Its associations and pathogenesis are unclear. Our aim was to assess the presence, nature, and extent of myocardial damage in hospitalized patients with troponin elevation. METHODS: Across 25 hospitals in the United Kingdom, 342 patients with COVID-19 and an elevated troponin level (COVID+/troponin+) were enrolled between June 2020 and March 2021 and had a magnetic resonance imaging scan within 28 days of discharge. Two prospective control groups were recruited, comprising 64 patients with COVID-19 and normal troponin levels (COVID+/troponin-) and 113 patients without COVID-19 or elevated troponin level matched by age and cardiovascular comorbidities (COVID-/comorbidity+). Regression modeling was performed to identify predictors of major adverse cardiovascular events at 12 months. RESULTS: Of the 519 included patients, 356 (69%) were men, with a median (interquartile range) age of 61.0 years (53.8, 68.8). The frequency of any heart abnormality, defined as left or right ventricular impairment, scar, or pericardial disease, was 2-fold greater in cases (61% [207/342]) compared with controls (36% [COVID+/troponin-] versus 31% [COVID-/comorbidity+]; P<0.001 for both). More cases than controls had ventricular impairment (17.2% versus 3.1% and 7.1%) or scar (42% versus 7% and 23%; P<0.001 for both). The myocardial injury pattern was different, with cases more likely than controls to have infarction (13% versus 2% and 7%; P<0.01) or microinfarction (9% versus 0% and 1%; P<0.001), but there was no difference in nonischemic scar (13% versus 5% and 14%; P=0.10). Using the Lake Louise magnetic resonance imaging criteria, the prevalence of probable recent myocarditis was 6.7% (23/342) in cases compared with 1.7% (2/113) in controls without COVID-19 (P=0.045). During follow-up, 4 patients died and 34 experienced a subsequent major adverse cardiovascular event (10.2%), which was similar to controls (6.1%; P=0.70). Myocardial scar, but not previous COVID-19 infection or troponin, was an independent predictor of major adverse cardiovascular events (odds ratio, 2.25 [95% CI, 1.12-4.57]; P=0.02). CONCLUSIONS: Compared with contemporary controls, patients with COVID-19 and elevated cardiac troponin level have more ventricular impairment and myocardial scar in early convalescence. However, the proportion with myocarditis was low and scar pathogenesis was diverse, including a newly described pattern of microinfarction. REGISTRATION: URL: https://www.isrctn.com; Unique identifier: 58667920.

Teamwork Is Associated with Reduced Hospital Staff Burnout at Military Treatment Facilities: Findings from the 2019 Department of Defense Patient Safety Culture Survey.

BACKGROUND: A National Academy of Medicine report emphasizes the importance of creating positive work environments to address the negative effects of burnout on health care workers. The purpose of this investigation was to determine the scope of burnout among military hospital personnel and explore the relationship between teamwork, burnout, and patient safety culture. METHODS: A logistic regression analysis investigated the relationship between teamwork and burnout using the 2019 US Department of Defense Patient Safety Culture Survey data from 15,838 military hospital workers. Additional regressions investigated teamwork/burnout relationships among individual work areas and staff positions. RESULTS: About one third of respondents (34.4%) reported experiencing burnout. Work areas most likely to report burnout included many different/other work areas (43.4%), pharmacy (41.8%), and labor and delivery/obstetrics (41.8%). Staff positions most likely to report burnout included pharmacy/pharmacists (39.7%), assistants/technicians/therapists (38.1%), and nurses/nursing (37.6%). Analysis revealed an association between lower burnout and high teamwork, both within (odds ratio [OR] 0.54, 95% confidence interval [CI] 0.48-0.60) and across (OR 0.64, 95% CI 0.57-0.72) units. Within-unit teamwork was associated with reduced odds of burnout across almost all work areas and staff positions, with the greatest odds reduction among personnel working in emergency (OR 0.25, 95% CI 0.14-0.43), radiology (OR 0.41, 95% CI 0.20-0.83), and labor and delivery/obstetrics (OR 0.42, 95% CI 0.27-0.65); and physicians/medical staff (OR = 0.44, 95% CI: 0.28-0.69), other staff positions (OR 0.48, 95% CI 0.28-0.81), and assistants/technicians/therapists (OR 0.58, 95% CI 0.46-0.73). CONCLUSION: Effective teamwork may reduce burnout in hospital workers. This association between teamwork (particularly teamwork within units) and burnout was found in all work areas, even in those with the highest levels of self-reported workplace chaos. Greater adoption of workplace interventions focused on improving teamwork, such as TeamSTEPPS, is warranted.

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar.

BACKGROUND AND OBJECTIVES: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent. METHODS: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality. RESULTS: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction. CONCLUSIONS: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.

Teachers' dissatisfaction during the COVID-19 pandemic: Factors contributing to a desire to leave the profession.

Introduction: The COVID-19 pandemic required more responsibilities from teachers, including implementing prevention strategies, changes in school policies, and managing their own mental health, which yielded higher dissatisfaction in the field. Methods: A cross-sectional web survey was conducted among educators to collect information on their experiences teaching during the COVID-19 pandemic throughout the 2020-2021 academic year. Qualtrics, an online survey platform, fielded the survey from May 6 to June 8, 2021 to a national, convenience sample of 1,807 respondents. Results: Findings revealed that overall, 43% of K-12 teachers reported a greater intention to leave the profession than previously recalled prior to the COVID-19 pandemic. Intention to leave was multi-level, and associated with socio-demographic factors (e.g., age: AOR = 1.87, p < 0.05), individual factors (e.g., perceived COVID risks: AOR = 1.44, p < 0.05), and teachers' agency (e.g., dissatisfaction with school/district communications and decisions: AOR = 1.34, p < 0.05). We also found demographic disparities with respect to race and gender (e.g., female teachers: AOR: 1.78, p < 0.05) around teachers' ability to provide feedback to schools on opening/closing and overall dissatisfaction with school/district COVID-19 prevention strategies implementation and policies. Conclusion: These findings are consistent with the Job-Demand and Resources Model (JD-R), which posits that lack of organizational support can exacerbate job stressors, leading to burnout. Specifically, dissatisfaction with the way school policies were implemented took a toll on teachers' mental health, leading to a desire to leave the profession. These findings are also consistent with research conducted once in-person teaching resumed in 2020-2021, specifically that the COVID-19 pandemic exacerbated preexisting teacher shortages that led to self-reported issues of stress, burnout, and retention. Implications: Further research is necessary to understand the resources that may be most useful to reduce the demands of teaching in the context of the COVID-19 pandemic. Some teachers are more likely to leave the field, and educational agencies may wish to target their teacher-retention efforts with emphasis on strong employee wellness programs that help educators to manage and reduce their stress. Education agency staff may wish to review policies and practices to provide meaningful opportunities to give input to school/district decisions and enable proactive communication channels.

The temporo-mandibular joint: Reconstruction of the condyle post-ablation.

Ablative surgery of the mandibular condyle poses a unique reconstructive challenge for many reasons. The condyle and it's relationship to the TMJ is a unique, complex, functional and aesthetically relevant piece of human anatomy. Resection may be required for both malignant and benign pathologies; each posing a differing set of surgical variables. Particularly in neoplastic processes, there must remain a certain degree of peri-operative flexibility with regards to the extent of the resection, and forethought to the requirement for post-operative radiotherapy; both of which further complicate choice of reconstructive option and surgical or prosthetic planning. The cases involved can often concern paediatric patients, and an additional aspect to be considered is that of growth potential. In this piece, we will discuss the indications for ablation and the techniques involved. We will elaborate on the reconstructive challenges specific to reconstructing the condyle in post-ablative cases. We will then describe and analyse the established reconstructive techniques; aiming to provide a balanced view on the advantages and disadvantages. Our focus will include autologous options such as vascularised and non-vascularised free tissue transfer, and the non-autologous options of custom and stock implants. We will also touch on distraction osteogenesis and ramus osteotomies. Lastly we will look to the future and consider possible innovative techniques which may become available to the surgeon.