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Extreme precipitation events have a significant impact on life and property. The U.S. experiences huge economic losses due to severe floods caused by extreme precipitation. With the complex terrain of the region, it becomes increasingly important to understand the spatial variability of extreme precipitation to conduct a proper risk assessment of natural hazards such as floods. In this work, we use a complex network-based approach to identify distinct regions exhibiting spatially coherent precipitation patterns due to various underlying climate mechanisms. To quantify interactions between event series of different locations, we use a nonlinear similarity measure, called the edit-distance method, which considers not only the occurrence of the extreme events but also their intensity, while measuring similarity between two event series. Using network measures, namely, degree and betweenness centrality, we are able to identify the specific regions affected by the landfall of atmospheric rivers in addition to those where the extreme precipitation due to storm track activity is modulated by different mountain ranges such as the Rockies and the Appalachians. Our approach provides a comprehensive picture of the spatial patterns of extreme winter precipitation in the U.S. due to various climate processes despite its vast, complex topography.

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.

BackgroundThere is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission.MethodsWe analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set.FindingsThe LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors.InterpretationThis simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available athttps://mdscore.net/.Summary boxWhat is already known on this topic?Clinical prediction models such as NEWS2 is currently used in practice as mortality risk assessment. In a rapid response to support COVID-19 patient assessment and resource management, published risk tools and models have been found to have a high risk of bias and therefore cannot be translated into clinical practice.What this study adds?A newly developed and validated risk tool (LUCAS) based on rapid and routine blood tests predicts the mortality of patients infected with SARS-CoV-2 virus. This prediction model has both high and robust predictive power and has been tested on an external set of patients and therefore can be used to effectively triage patients when resources are limited. In addition, LUCAS can be used with chest imaging information and NEWS2 score.

Probabilistic characterisation of image data for accurate prognosis and treatment planning remains a long-standing problem in medical research, especially when the data distribution depicts flat-top and high-order contact. Such flat-top distributions are quite common in brain magnetic resonance (MR) image data, where the density drops sharply beyond the flat interval. Intuitively, it would indicate a bipartition of data into positive region containing observations definitely belonging to the image class and boundary region with observations possibly belonging to it. The flat peak would also imply that multiple values are equally most likely to belong to that class. However, the popular probability distributions used in such cases are unimodal, creating ambiguity about the positive region. In this work, we study the statistical properties and develop likelihood-based iterative estimation method for the parameters of a novel class of platykurtic probability distributions containing normal, called the stomped normal distribution, that provides more accurate modelling to the flat-top data distributions. The robustness of the proposed stomped normal model has been illustrated with six simulated and nine real brain MR volumes. Our analysis shows substantial improvement in explaining a variety of shapes of data distributions using the proposed probability model.

Background: There is an urgent need to develop a simplified risk tool that enables rapid triaging of SARS CoV-2 positive patients during hospital admission, which complements current practice. Many predictive tools developed to date are complex, rely on multiple blood results and past medical history, do not include chest X ray results and rely on Artificial Intelligence rather than simplified algorithms. Our aim was to develop a simplified risk-tool based on five parameters and CXR image data that predicts the 60-day survival of adult SARS CoV-2 positive patients at hospital admission. Methods: We analysed the NCCID database of patient blood variables and CXR images from 19 hospitals across the UK contributed clinical data on SARS CoV-2 positive patients using multivariable logistic regression. The initial dataset was non-randomly split between development and internal validation dataset with 1434 and 310 SARS CoV-2 positive patients, respectively. External validation of final model conducted on 741 Accident and Emergency admissions with suspected SARS CoV-2 infection from a separate NHS Trust which was not part of the initial NCCID data set. Findings: The LUCAS mortality score included five strongest predictors (lymphocyte count, urea, CRP, age, sex), which are available at any point of care with rapid turnaround of results. Our simple multivariable logistic model showed high discrimination for fatal outcome with the AUC-ROC in development cohort 0.765 (95% confidence interval (CI): 0.738 - 0.790), in internal validation cohort 0.744 (CI: 0.673 - 0.808), and in external validation cohort 0.752 (CI: 0.713 - 0.787). The discriminatory power of LUCAS mortality score was increased slightly when including the CXR image data (for normal versus abnormal): internal validation AUC-ROC 0.770 (CI: 0.695 - 0.836) and external validation AUC-ROC 0.791 (CI: 0.746 - 0.833). The discriminatory power of LUCAS and LUCAS + CXR performed in the upper quartile of pre-existing risk stratification scores with the added advantage of using only 5 predictors. Interpretation: This simplified prognostic tool derived from objective parameters can be used to obtain valid predictions of mortality in patients within 60 days SARS CoV-2 RT-PCR results. This free-to-use simplified tool can be used to assist the triage of patients into low, moderate, high or very high risk of fatality and is available at https://mdscore.net/. Funding Statement: EPSRC Impact Acceleration account fund EP/R511705/1; AS is funded by a Wellcome Trust fellowship 205188/Z/16/Z; University of Brighton COVID-19 Research Urgency Fund. Declaration of Interests: All researchers declare no competing interests. Ethics Approval Statement: De-identified and pseudo-anonymised patient data were obtained from data sets which were approved by the ethics committee as part of the existing Cardiac MRI Database NHS REC IRAS Ref: 222349 and University of Brighton REC (8011).

Background and purpose: No previous study has assessed the frequency and clinical– radiological characteristics of patients with diabetes mellitus (DM) and acute onset non-choreic 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 hyper-glycemia 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 hypergly-cemia 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.

In the recent years, wireless communications are extremely useful in many disciplines including health monitoring, environment monitoring, signal processing etc. State estimation and prediction are quite challenging tasks in wireless communications. Traditionally, in the literature, dynamic state-space models have been used for the state estimation and predic- tion purpose. The estimation method is based on Kalman-Filter which is computationally demanding. In this work, we consider computationally simpler Gibbs sampler algorithm for the state estimation. We consider three different cases, (i) continuous state values, (ii) binary (0/1) state values, and (iii) categorical state values with more than two categories. We consider a simple linear model for the prediction purpose, and the underlying regression coefcients are estimated by Gibbs sampler. We compute the misclassication proportions for assessing the practical usefulness of our estimation approach. Areal dataset where 200 wireless sensor nodes are used for measuring the temperature of a chamber is analysed in this work.

Generative statistical models have a wide variety of applications in modelling of cardiac anatomy and function, including disease diagnosis and prediction, personalized shape analysis, and generation of population cohorts for electrophysiological and mechanical computer simulations. In this work, we propose a novel geometric deep learning method based on the variational autoencoder (VAE) framework capable of accurately encoding, reconstructing, and synthesizing 3D surface models of the biventricular anatomy. Our non-linear approach works directly with memory-efficient point clouds and is able to process multiple substructures of the cardiac anatomy at the same time in a multi-class setting. Furthermore, we introduce subpopulation-specific characteristics as additional conditional inputs to allow the generation of new personalized anatomies. Our method achieves high reconstruction quality on a dataset derived from the UK Biobank study with average Chamfer distances between reconstructed and gold standard point clouds below the underlying image pixel resolution, for all anatomical substructures and combinations of conditional inputs. We investigate our method’s generative capabilities and show that it is able to synthesize virtual populations of realistic hearts with volumetric measurements in line with established clinical precedent. We also analyse the effects of variations in the latent space of the autoencoder on the generated anatomies and find interpretable changes in cardiac shapes and sizes.

Mechanical contraction and relaxation of the heart play an important role in evaluating healthy and diseased cardiac function. Mechanical patterns consist of complex non-linear 3D deformations that vary considerably between subjects and are difficult to observe on 2D images, which impacts the prediction accuracy of cardiac outcomes. In this work, we aim to capture 3D biventricular deformations at the end-diastolic (ED) and end-systolic (ES) phases of the cardiac cycle with a novel geometric deep learning approach. Our network consists of an encoder-decoder structure that works directly with light-weight point cloud data. We initially train our network on pairs of ED and ES point clouds stemming from a mixed population of subjects with the aim of accurately predicting ED outputs from ES inputs as well as ES outputs from ED inputs. We validate our network’s performance using the Chamfer distance (CD) and find that ED and ES predictions can be achieved with an average CD of 1.66 ± 0.62 mm on a dataset derived from the UK Biobank cohort with an underlying voxel size of 1.8×1.8×8.0 mm [8]. We derive structural and functional clinical metrics such as myocardial mass, ventricular volume, ejection fraction, and stroke volume from the predictions and find an average mean deviation from their respective gold standards of 1.6% and comparable standard deviations. Finally, we show our method’s ability to capture deformation differences between specific subpopulations in the dataset.

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’.

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 (

The study aimed to develop a prediction model for differentiating suspected PDAC from benign conditions. We used a prospective cohort of patients with pancreatic disease (n = 762) enrolled at the Barts Pancreas Tissue Bank (2008-2021) and performed a case-control study examining the association of PDAC (n = 340) with predictor variables including demographics, comorbidities, lifestyle factors, presenting symptoms and commonly performed blood tests. Age (over 55), weight loss in hypertensive patients, recent symptoms of jaundice, high serum bilirubin, low serum creatinine, high serum alkaline phosphatase, low red blood cell count and low serum sodium were identified as the most important features. These predictors were then used for training several machine-learning-based risk-prediction models on 75% of the cohort. Models were assessed on the remaining 25%. A logistic regression-based model had the best overall performance in the validation cohort (area-under-the-curve = 0.90; Spiegelhalter’s z = −1·82, p = 0.07). Setting a probability threshold of 0.15 guided by the maximum F2-score of 0.855, 96.8% sensitivity was reached in the full cohort, which could lead to earlier detection of 84.7% of the PDAC patients. The prediction model has the potential to be applied in primary, secondary and emergency care settings for the early distinction of suspected PDAC patients and expedited referral to specialist hepato-pancreatico-biliary services.