Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

A Model of Contextual Factors and their Effects in the Interruptive Notification User Experience

Interruptive notifications in a desktop environment are an important service that knowledge workers rely on for maintaining awareness of information and services outside their current focus. Research to date has focused primarily on empirical laboratory-based testing, which is very specific and out of context of a realistic user environment, and broad ethnographic research, which is not specific enough for meaningful notification system design guidelines. This dissertation aims to address the gap between existing laboratory-based and ethnographic research by conducting a series of studies that explored the notification user experience in a both broad and deep way. The results of this research contribute the following: A catalog of significant contextual factors that affect the notification user experience; a series of models that describe how factors in the notification system context influence the overall user experience; a set of design guidelines, derived from this research but generalized to be applicable to any interruptive notification system

A Model of Contextual Factors and their Effects in the Interruptive Notification User Experience

Interruptive notifications in a desktop environment are an important service that knowledge workers rely on for maintaining awareness of information and services outside their current focus. Research to date has focused primarily on empirical laboratory-based testing, which is very specific and out of context of a realistic user environment, and broad ethnographic research, which is not specific enough for meaningful notification system design guidelines. This dissertation aims to address the gap between existing laboratory-based and ethnographic research by conducting a series of studies that explored the notification user experience in a both broad and deep way. The results of this research contribute the following: A catalog of significant contextual factors that affect the notification user experience; a series of models that describe how factors in the notification system context influence the overall user experience; a set of design guidelines, derived from this research but generalized to be applicable to any interruptive notification system

Abstract Object-based Annotations for Discovery and Collaboration

This paper discusses a design for object-based interaction and manipulation for annotating a text discovery application. Rather than attaching annotations to the interface or directly annotating the interface, objects from the interface can be directly annotated and copied in to a collection to be viewed outside the context of the main interface. Objects are smaller chunks of the interface which have greater meaning and easier for users to manage. These objects contain meta data which describes the object and contains a bookmark to the system state from which they were taken. Collections can be save and shared with colleagues who can view the objects and access the system state bookmark to continue with their own analysis. A case study of object-based annotation using the text discovery tool FeatureLens is described

VAST Challenge 2012: Visual Analytics for Big Data

National Security Agency The 2012 Visual Analytics Science and Technology (VAST) Challenge posed two challenge problems for participants to solve using a combination of visual analytics software and their own analytic reasoning abilities. Challenge 1 (C1) involved visualizing the network health of the fictitious Bank of Money to provide situation awareness and identify emerging trends that could signify network issues. Challenge 2 (C2) involved identifying the issues of concern within a region of the Bank of Money network experiencing operational difficulties utilizing the provided network logs. Participants were asked to analyze the data and provide solutions and explanations for both challenges. The data sets were downloaded by nearly 1100 people by the close of submissions. The VAST Challenge received 40 submissions with participants from 12 different countries, and 14 awards were given

Influencing Network Graph Perception and Judgment: Effects of DirectConnections, Base Rates, and Visual Layout Proximity on Social Network Analysis

Social network graphs are often used to help informjudgments in a variety of domains, such as public health, lawenforcement, and political science. Across two studies, weexamined how graph features influenced probabilisticjudgments in graph-based social network analysis andidentified multiple heuristics that participants used to informthese judgments. Study 1 demonstrated that participants’judgments were influenced by information about directconnections, base rates, and layout proximity, andparticipants’ self-reported strategies also reflected use of thisinformation. Study 2 replicated findings from Study 1 andprovided additional insight into the hierarchical ordering ofthese strategies and the decision process underlyingjudgments from social network graphs

Eventpad: Rapid malware analysis and reverse engineering using visual analytics

Forensic analysis of malware activity in network environments is a necessary yet very costly and time consuming part of incident response. Vast amounts of data need to be screened, in a very labor-intensive process, looking for signs indicating how the malware at hand behaves inside e.g., a corporate network. We believe that data reduction and visualization techniques can assist security analysts in studying behavioral patterns in network traffic samples (e.g., PCAP). We argue that the discovery of patterns in this traffic can help us to quickly understand how intrusive behavior such as malware activity unfolds and distinguishes itself from the rest of the traffic.In this paper we present a case study of the visual analytics tool EventPad and illustrate how it is used to gain quick insights in the analysis of PCAP traffic using rules, aggregations, and selections. We show the effectiveness of the tool on real-world data sets involving office traffic and ransomware activity