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BACKGROUND: When using lesion size index (LSI) to guide catheter ablation, it is unclear what combination of power, contact force and time would be preferable to use and what LSI target value to aim for. This study aimed at identifying desirable ablation settings and LSI targets by using tissue impedance drop as indicator of lesion formation. METHODS: Consecutive patients, undergoing their first left atrial (LA) catheter ablation for atrial fibrillation, with radiofrequency energy (RF) powers of 20, 30 and 40 W were enrolled. Tissue impedance, contact force (CF), Force Time Integral (FTI) and LSI values were continuously recorded during ablation and sampled at 100 Hz. Mean CF and Contact Force Variability (CFV) were calculated for every lesion. The effect of RF power, ablation time, CF and CFV on impedance drop and LSI were assessed. RESULTS: A total of 3258 lesions were included in the analysis. For any target LSI value, use of higher RF powers translated into progressively higher impedance drops. The impact of lower CF and higher CFV on impedance drop was more relevant when using lower powers. Target LSI values corresponding to maximum impedance drop were identified depending on RF power, mean CF and CFV used. CONCLUSIONS: Even in the context of an LSI-guided ablation strategy, use of lower or higher powers might lead to different lesion sizes. Different LSI targets might be needed depending on the combination of RF power, CF and CFV used for ablation. Incorporating indicators of catheter stability, like CFV, in the LSI formula could improve the predictive value of LSI for lesion size. Studies with clinical outcomes are required to confirm the clinical relevance of these findings.
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BACKGROUND AND PURPOSE: No previous study has assessed the frequency and clinical-radiological characteristics of patients with diabetes mellitus (DM) and acute onset nonchoreic and nonballistic movements. We conducted a prospective study to investigate the spectrum of acute onset movement disorders in DM. METHODS: We recruited all the patients with acute onset movement disorders and hyperglycemia who attended the wards of three hospitals in West Bengal, India from August 2014 to July 2021. RESULTS: Among the 59 patients (mean age = 55.4 ± 14.3 years, 52.5% men) who were included, 41 (69.5%) had choreic or ballistic movements, and 18 (30.5%) had nonchoreic and nonballistic movements. Ballism was the most common movement disorder (n = 18, 30.5%), followed by pure chorea (n = 15, 25.4%), choreoathetosis (n = 8, 13.6%), tremor (n = 5, 8.5%), hemifacial spasm (n = 3, 5.1%), parkinsonism (n = 3, 5.1%), myoclonus (n = 3, 5.1%), dystonia (n = 2, 3.4%), and restless leg syndrome (n = 2, 3.4%). The mean duration of DM was 9.8 ± 11.4 years (89.8% of the patients had type 2 DM). Nonketotic hyperglycemia was frequently (76.3%) detected. The majority (55.9%) had no magnetic resonance imaging (MRI) changes; the remaining showed striatal hyperintensity. Eight patients with MRI changes exhibited discordance with sidedness of movements. Most of the patients (76.3%) recovered completely. CONCLUSIONS: This is the largest clinical series depicting the clinical-radiological spectrum of acute onset movement disorders in DM. Of note was that almost one third of patients had nonchoreic and nonballistic movements. Our findings highlight the importance of a capillary blood glucose measurement in patients with acute or subacute onset movement disorders, irrespective of their past glycemic status.
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Coreia , Diabetes Mellitus Tipo 2 , Hiperglicemia , Transtornos dos Movimentos , Adulto , Idoso , Coreia/epidemiologia , Feminino , Humanos , Hiperglicemia/complicações , Hiperglicemia/epidemiologia , Masculino , Pessoa de Meia-Idade , Transtornos dos Movimentos/etiologia , Estudos ProspectivosRESUMO
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
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Acute kidney injury (AKI) is a prevalent complication in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive inpatients, which is linked to an increased mortality rate compared to patients without AKI. Here we analysed the difference in kidney blood biomarkers in SARS-CoV-2 positive patients with non-fatal or fatal outcome, in order to develop a mortality prediction model for hospitalised SARS-CoV-2 positive patients. A retrospective cohort study including data from suspected SARS-CoV-2 positive patients admitted to a large National Health Service (NHS) Foundation Trust hospital in the Yorkshire and Humber regions, United Kingdom, between 1 March 2020 and 30 August 2020. Hospitalised adult patients (aged ≥ 18 years) with at least one confirmed positive RT-PCR test for SARS-CoV-2 and blood tests of kidney biomarkers within 36 h of the RT-PCR test were included. The main outcome measure was 90-day in-hospital mortality in SARS-CoV-2 infected patients. The logistic regression and random forest (RF) models incorporated six predictors including three routine kidney function tests (sodium, urea; creatinine only in RF), along with age, sex, and ethnicity. The mortality prediction performance of the logistic regression model achieved an area under receiver operating characteristic (AUROC) curve of 0.772 in the test dataset (95% CI: 0.694-0.823), while the RF model attained the AUROC of 0.820 in the same test cohort (95% CI: 0.740-0.870). The resulting validated prediction model is the first to focus on kidney biomarkers specifically on in-hospital mortality over a 90-day period.
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Injúria Renal Aguda , COVID-19 , Injúria Renal Aguda/diagnóstico , Injúria Renal Aguda/etiologia , Adulto , Biomarcadores , COVID-19/diagnóstico , Mortalidade Hospitalar , Humanos , Rim , Estudos Retrospectivos , SARS-CoV-2 , Medicina EstatalRESUMO
Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Imageamento Tridimensional , Imagem Cinética por Ressonância Magnética , Coração/diagnóstico por imagem , Humanos , Imageamento por Ressonância Magnética , Espectroscopia de Ressonância MagnéticaAssuntos
Glucagonoma/complicações , Eritema Migratório Necrolítico/etiologia , Neoplasias Pancreáticas/complicações , Complicações do Diabetes , Glucagonoma/diagnóstico por imagem , Humanos , Masculino , Pessoa de Meia-Idade , Eritema Migratório Necrolítico/patologia , Pâncreas/diagnóstico por imagem , Neoplasias Pancreáticas/diagnóstico por imagem , Radiografia Abdominal , Tomografia Computadorizada por Raios XRESUMO
Cystic lesions of the liver are common and a major proportion is formed by parasitic cysts and simple cysts. Biliary cystic tumors (BCTs), namely biliary cystadenoma (BCA) and biliary cystadenocarcinoma (BCAC), are rare tumors which usually arise from the intrahepatic biliary tree. BCAs have malignant potential and are difficult to differentiate from BCAC pre-operatively on radiological imaging. Here we have presented 4 patients with BCTs and reviewed the literature pertaining to them.The data of four patients with BCA/BCAC diagnosed and treated at our institute were retrieved from our database and records were reviewed for age, sex, history, imaging, surgery, pathology and follow-up. Mean age of the patients was 53.5 years (range 30-71 years). Two male and two female patients presented with abdominal pain, of which one male patient had pancreatitis at diagnosis. Characteristic features were seen on pre-operative imaging (cystic lesions with internal septations) and biliary communication was identified in the patient with pancreatitis. Three patients were diagnosed with a BCA on final histology, while one patient had a BCAC. Following surgical resection, all the patients are asymptomatic and disease free with a mean follow-up of 24 months (range 10-40 months). In conclusion, BCTs should be suspected in the presence of a well-encapsulated, cystic hepatic lesion with internal septations. Although pre-operative distinction between BCA and BCAC is difficult, the lesion, whenever possible, should be completely resected as long-term outcomes are good, especially with BCA.
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Neoplasias dos Ductos Biliares/patologia , Ductos Biliares Intra-Hepáticos/patologia , Cistadenocarcinoma/patologia , Cistadenoma/patologia , Adulto , Idoso , Neoplasias dos Ductos Biliares/diagnóstico por imagem , Neoplasias dos Ductos Biliares/cirurgia , Ductos Biliares Intra-Hepáticos/cirurgia , Biópsia , Cistadenocarcinoma/diagnóstico por imagem , Cistadenocarcinoma/cirurgia , Cistadenoma/diagnóstico por imagem , Cistadenoma/cirurgia , Feminino , Hepatectomia/métodos , Humanos , Laparoscopia , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Tomografia Computadorizada por Raios X , Resultado do TratamentoRESUMO
Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings. In this work, we present the Point Cloud Deformation Network (PCD-Net) as a novel geometric deep learning approach for direct modeling of 3D cardiac mechanics of the biventricular anatomy between the extreme ends of the cardiac cycle. Its encoder-decoder architecture combines a low-dimensional latent space with recent advances in point cloud deep learning for effective multi-scale feature learning directly on flexible and memory-efficient point cloud representations of the cardiac anatomy. We first evaluate the PCD-Net's predictive capability for both cardiac contraction and relaxation on a large UK Biobank dataset of over 10,000 subjects and find average Chamfer distances between the predicted and ground truth anatomies below the pixel resolution of the underlying image acquisition. We then show the PCD-Net's ability to capture subpopulation-specific differences in 3D cardiac mechanics between normal and myocardial infarction (MI) subjects and visualize abnormal phenotypes between predicted normal 3D shapes and corresponding observed ones. Finally, we demonstrate that the PCD-Net's learned 3D deformation encodings outperform multiple clinical and machine learning benchmarks by 11% in terms of area under the receiver operating characteristic curve for the tasks of prevalent MI detection and incident MI prediction and by 7% in terms of Harrell's concordance index for MI survival analysis.
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Aprendizado Profundo , Imageamento Tridimensional , Contração Miocárdica , Humanos , Contração Miocárdica/fisiologia , Imageamento Tridimensional/métodos , Coração/fisiologia , Coração/diagnóstico por imagem , Modelos Cardiovasculares , MasculinoRESUMO
Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.
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Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia associated with significant morbidity and mortality. Managing risk of stroke and AF burden are pillars of AF management. Atrial geometry has long been recognized as a useful measure in achieving these goals. However, traditional diagnostic approaches often overlook the complex spatial dynamics of the atria. This review explores the emerging role of three-dimensional (3D) atrial geometry in the evaluation and management of AF. Advancements in imaging technologies and computational modeling have enabled detailed reconstructions of atrial anatomy, providing insights into the pathophysiology of AF that were previously unattainable. We examine current methodologies for interpreting 3D atrial data, including qualitative, basic quantitative, global quantitative, and statistical shape modeling approaches. We discuss their integration into clinical practice, highlighting potential benefits such as personalized treatment strategies, improved outcome prediction, and informed treatment approaches. Additionally, we discuss the challenges and limitations associated with current approaches, including technical constraints and variable interpretations, and propose future directions for research and clinical applications. This comprehensive review underscores the transformative potential of leveraging 3D atrial geometry in the evaluation and management of AF, advocating for its broader adoption in clinical practice.
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Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ±0.317 and 0.302 ±0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://github.com/lileitech/MI_inverse_inference.
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Eletrocardiografia , Imageamento por Ressonância Magnética , Infarto do Miocárdio , Infarto do Miocárdio/diagnóstico por imagem , Infarto do Miocárdio/fisiopatologia , Humanos , Eletrocardiografia/métodos , Imageamento por Ressonância Magnética/métodos , Simulação por Computador , Coração/diagnóstico por imagem , Aprendizado Profundo , AlgoritmosRESUMO
BACKGROUND: In suspected non-ST-segment elevation myocardial infarction (NSTEMI), this presumed diagnosis may not hold true in all cases, particularly in patients with nonobstructive coronary arteries (NOCA). Additionally, in multivessel coronary artery disease, the presumed infarct-related artery may be incorrect. OBJECTIVES: This study sought to assess the diagnostic utility of cardiac magnetic resonance (CMR) before invasive coronary angiogram (ICA) in suspected NSTEMI. METHODS: A total of 100 consecutive stable patients with suspected acute NSTEMI (70% male, age 62 ± 11 years) prospectively underwent CMR pre-ICA to assess cardiac function (cine), edema (T2-weighted imaging, T1 mapping), and necrosis/scar (late gadolinium enhancement). CMR images were interpreted blinded to ICA findings. The clinical care and ICA teams were blinded to CMR findings until post-ICA. RESULTS: Early CMR (median 33 hours postadmission and 4 hours pre-ICA) confirmed only 52% (52 of 100) of patients had subendocardial infarction, 15% transmural infarction, 18% nonischemic pathologies (myocarditis, Takotsubo and other forms of cardiomyopathies), and 11% normal CMR; 4% were nondiagnostic. Subanalyses according to ICA findings showed that, in patients with obstructive coronary artery disease (73 of 100), CMR confirmed only 84% (61 of 73) had MI, 10% (7 of 73) nonischemic pathologies, and 5% (4 of 73) normal. In patients with NOCA (27 of 100), CMR found MI in only 22% (6 of 27 true MI with NOCA), and reclassified the presumed diagnosis of NSTEMI in 67% (18 of 27: 11 nonischemic pathologies, 7 normal). In patients with CMR-MI and obstructive coronary artery disease (61 of 100), CMR identified a different infarct-related artery in 11% (7 of 61). CONCLUSIONS: In patients presenting with suspected NSTEMI, a CMR-first strategy identified MI in 67%, nonischemic pathologies in 18%, and normal findings in 11%. Accordingly, CMR has the potential to affect at least 50% of all patients by reclassifying their diagnosis or altering their potential management.
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Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.
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Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.
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Coração , Imageamento por Ressonância Magnética , Humanos , Coração/diagnóstico por imagem , Imagem Cinética por Ressonância Magnética/métodos , Tórax , Imageamento Tridimensional/métodosRESUMO
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of â¼13% and â¼5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.Clinical relevance- The presented approach enables the fast and fully automatic pathology-specific analysis of full 3D cardiac shapes. It can thus be employed as a real-time diagnostic tool in clinical practice to discover and visualize more intricate biomarkers than currently used single-valued metrics and improve predictive accuracy of myocardial infarction.
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Infarto do Miocárdio , Humanos , Infarto do Miocárdio/diagnóstico por imagem , Coração , Ventrículos do Coração , Benchmarking , BiomarcadoresRESUMO
Parkinson's disease (PD) is characterized by a progressive deterioration of motor and cognitive functions. Although death of dopamine neurons is the hallmark pathology of PD, this is a late-stage disease process preceded by neuronal dysfunction. Here we describe early physiological perturbations in patient-derived induced pluripotent stem cell (iPSC)-dopamine neurons carrying the GBA-N370S mutation, a strong genetic risk factor for PD. GBA-N370S iPSC-dopamine neurons show an early and persistent calcium dysregulation notably at the mitochondria, followed by reduced mitochondrial membrane potential and oxygen consumption rate, indicating mitochondrial failure. With increased neuronal maturity, we observed decreased synaptic function in PD iPSC-dopamine neurons, consistent with the requirement for ATP and calcium to support the increase in electrophysiological activity over time. Our work demonstrates that calcium dyshomeostasis and mitochondrial failure impair the higher electrophysiological activity of mature neurons and may underlie the vulnerability of dopamine neurons in PD.
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Background: Patients with a history of COVID-19 infection are reported to have cardiac abnormalities on cardiovascular magnetic resonance (CMR) during convalescence. However, it is unclear whether these abnormalities were present during the acute COVID-19 illness and how they may evolve over time. Methods: We prospectively recruited unvaccinated patients hospitalized with acute COVID-19 (n = 23), and compared them with matched outpatient controls without COVID-19 (n = 19) between May 2020 and May 2021. Only those without a past history of cardiac disease were recruited. We performed in-hospital CMR at a median of 3 days (IQR 1-7 days) after admission, and assessed cardiac function, edema and necrosis/fibrosis, using left and right ventricular ejection fraction (LVEF, RVEF), T1-mapping, T2 signal intensity ratio (T2SI), late gadolinium enhancement (LGE) and extracellular volume (ECV). Acute COVID-19 patients were invited for follow-up CMR and blood tests at 6 months. Results: The two cohorts were well matched in baseline clinical characteristics. Both had normal LVEF (62 ± 7 vs. 65 ± 6%), RVEF (60 ± 6 vs. 58 ± 6%), ECV (31 ± 3 vs. 31 ± 4%), and similar frequency of LGE abnormalities (16 vs. 14%; all p > 0.05). However, measures of acute myocardial edema (T1 and T2SI) were significantly higher in patients with acute COVID-19 when compared to controls (T1 = 1,217 ± 41 ms vs. 1,183 ± 22 ms; p = 0.002; T2SI = 1.48 ± 0.36 vs. 1.13 ± 0.09; p < 0.001). All COVID-19 patients who returned for follow up (n = 12) at 6 months had normal biventricular function, T1 and T2SI. Conclusion: Unvaccinated patients hospitalized for acute COVID-19 demonstrated CMR imaging evidence of acute myocardial edema, which normalized at 6âmonths, while biventricular function and scar burden were similar when compared to controls. Acute COVID-19 appears to induce acute myocardial edema in some patients, which resolves in convalescence, without significant impact on biventricular structure and function in the acute and short-term. Further studies with larger numbers are needed to confirm these findings.
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INTRODUCTION: The COVID-19 pandemic has caused severe disruption of healthcare services worldwide and interrupted patients' access to essential services. During the first lockdown, many healthcare services were shut to all but emergencies. In this study, we aimed to determine the immediate and long-term indirect impact of COVID-19 health services utilisation on hepatocellular cancer (HCC) outcomes. METHODS: A prospective cohort study was conducted from 1 March 2020 until 30 June 2020, correlating to the first wave of the COVID-19 pandemic. Patients were enrolled from tertiary hospitals in the UK and Germany with dedicated HCC management services. All patients with current or past HCC who were discussed at a multidisciplinary meeting (MDM) were identified. Any delay to treatment (DTT) and the effect on survival at one year were reported. RESULTS: The median time to receipt of therapy following MDM discussion was 49 days. Patients with Barcelona Clinic Liver Cancer (BCLC) stages-A/B disease were more likely to experience DTT. Significant delays across all treatments for HCC were observed, but delay was most marked for those undergoing curative therapies. Even though severe delays were observed in curative HCC treatments, this did not translate into reduced survival in patients. CONCLUSION: Interruption of routine healthcare services because of the COVID-19 pandemic caused severe delays in HCC treatment. However, DTT did not translate to reduced survival. Longer follow is important given the delay in therapy in those receiving curative therapy.
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BACKGROUND: Acute ST-segment elevation myocardial infarction (STEMI) has effects on the myocardium beyond the immediate infarcted territory. However, pathophysiologic changes in the noninfarcted myocardium and their prognostic implications remain unclear. OBJECTIVES: The purpose of this study was to evaluate the long-term prognostic value of acute changes in both infarcted and noninfarcted myocardium post-STEMI. METHODS: Patients with acute STEMI undergoing primary percutaneous coronary intervention underwent evaluation with blood biomarkers and cardiac magnetic resonance (CMR) at 2 days and 6 months, with long-term follow-up for major adverse cardiac events (MACE). A comprehensive CMR protocol included cine, T2-weighted, T2∗, T1-mapping, and late gadolinium enhancement (LGE) imaging. Areas without LGE were defined as noninfarcted myocardium. MACE was a composite of cardiac death, sustained ventricular arrhythmia, and new-onset heart failure. RESULTS: Twenty-two of 219 patients (10%) experienced an MACE at a median of 4 years (IQR: 2.5-6.0 years); 152 patients returned for the 6-month visit. High T1 (>1250 ms) in the noninfarcted myocardium was associated with lower left ventricular ejection fraction (LVEF) (51% ± 8% vs 55% ± 9%; P = 0.002) and higher NT-pro-BNP levels (290 pg/L [IQR: 103-523 pg/L] vs 170 pg/L [IQR: 61-312 pg/L]; P = 0.008) at 6 months and a 2.5-fold (IQR: 1.03-6.20) increased risk of MACE (2.53 [IQR: 1.03-6.22]), compared with patients with normal T1 in the noninfarcted myocardium (P = 0.042). A lower T1 (<1,300 ms) in the infarcted myocardium was associated with increased MACE (3.11 [IQR: 1.19-8.13]; P = 0.020). Both noninfarct and infarct T1 were independent predictors of MACE (both P = 0.001) and significantly improved risk prediction beyond LVEF, infarct size, and microvascular obstruction (C-statistic: 0.67 ± 0.07 vs 0.76 ± 0.06, net-reclassification index: 40% [IQR: 12%-64%]; P = 0.007). CONCLUSIONS: The acute responses post-STEMI in both infarcted and noninfarcted myocardium are independent incremental predictors of long-term MACE. These insights may provide new opportunities for treatment and risk stratification in STEMI.