Characteristics and risk factors for post-COVID-19 breathlessness after hospitalisation for COVID-19.

Background: Persistence of respiratory symptoms, particularly breathlessness, after acute coronavirus disease 2019 (COVID-19) infection has emerged as a significant clinical problem. We aimed to characterise and identify risk factors for patients with persistent breathlessness following COVID-19 hospitalisation. Methods: PHOSP-COVID is a multicentre prospective cohort study of UK adults hospitalised for COVID-19. Clinical data were collected during hospitalisation and at a follow-up visit. Breathlessness was measured by a numeric rating scale of 0-10. We defined post-COVID-19 breathlessness as an increase in score of ≥1 compared to the pre-COVID-19 level. Multivariable logistic regression was used to identify risk factors and to develop a prediction model for post-COVID-19 breathlessness. Results: We included 1226 participants (37% female, median age 59 years, 22% mechanically ventilated). At a median 5 months after discharge, 50% reported post-COVID-19 breathlessness. Risk factors for post-COVID-19 breathlessness were socioeconomic deprivation (adjusted OR 1.67, 95% CI 1.14-2.44), pre-existing depression/anxiety (adjusted OR 1.58, 95% CI 1.06-2.35), female sex (adjusted OR 1.56, 95% CI 1.21-2.00) and admission duration (adjusted OR 1.01, 95% CI 1.00-1.02). Black ethnicity (adjusted OR 0.56, 95% CI 0.35-0.89) and older age groups (adjusted OR 0.31, 95% CI 0.14-0.66) were less likely to report post-COVID-19 breathlessness. Post-COVID-19 breathlessness was associated with worse performance on the shuttle walk test and forced vital capacity, but not with obstructive airflow limitation. The prediction model had fair discrimination (concordance statistic 0.66, 95% CI 0.63-0.69) and good calibration (calibration slope 1.00, 95% CI 0.80-1.21). Conclusions: Post-COVID-19 breathlessness was commonly reported in this national cohort of patients hospitalised for COVID-19 and is likely to be a multifactorial problem with physical and emotional components.

Characteristics and risk factors for post-COVID breathlessness after hospitalisation for COVID-19

Background Persistence of respiratory symptoms—particularly breathlessness—after acute COVID-19 infection has emerged as a significant clinical problem. We aimed to characterise and identify risk factors for patients with persistent breathlessness following COVID-19 hospitalisation. Methods PHOSP-COVID is a multi-centre prospective cohort study of UK adults hospitalised for COVID-19. Clinical data were collected during hospitalisation and at a follow-up visit. Breathlessness was measured by a numeric rating scale of 0–10. We defined post-COVID breathlessness as an increase in score of 1 or more compared to the pre-COVID-19 level. Multivariable logistic regression was used to identify risk factors, and to develop a prediction model for post-COVID breathlessness. Results We included 1226 participants (37% female, median age 59 years, 22% mechanically ventilated). At a median five months after discharge, 50% reported post-COVID breathlessness. Risk factors for post-COVID breathlessness were socio-economic deprivation (adjusted odds ratio, 1.67;95% confidence interval, 1.14–2.44), pre-existing depression/anxiety (1.58;1.06–2.35), female sex (1.56;1.21–2.00) and admission duration (1.01;1.00–1.02). Black ethnicity (0.56;0.35–0.89) and older age groups (0.31;0.14–0.66) were less likely to report post-COVID breathlessness. Post-COVID breathlessness was associated with worse performance on the shuttle walk test and forced vital capacity, but not with obstructive airflow limitation. The prediction model had fair discrimination (concordance-statistic 0.66;0.63–0.69), and good calibration (calibration slope 1.00;0.80–1.21). Conclusions Post-COVID breathlessness was commonly reported in this national cohort of patients hospitalised for COVID-19 and is likely to be a multifactorial problem with physical and emotional components.

Dysregulated Neutrophil Phenotype and Function in Hospitalised Non-ICU COVID-19 Pneumonia.

Rationale: Infection with the SARS-CoV2 virus is associated with elevated neutrophil counts. Evidence of neutrophil dysfunction in COVID-19 is based on transcriptomics or single functional assays. Cell functions are interwoven pathways, and understanding the effect across the spectrum of neutrophil function may identify therapeutic targets. Objectives: Examine neutrophil phenotype and function in 41 hospitalised, non-ICU COVID-19 patients versus 23 age-matched controls (AMC) and 26 community acquired pneumonia patients (CAP). Methods: Isolated neutrophils underwent ex vivo analyses for migration, bacterial phagocytosis, ROS generation, NETosis and receptor expression. Circulating DNAse 1 activity, levels of cfDNA, MPO, VEGF, IL-6 and sTNFRI were measured and correlated to clinical outcome. Serial sampling on day three to five post hospitalization were also measured. The effect of ex vivo PI3K inhibition was measured in a further cohort of 18 COVID-19 patients. Results: Compared to AMC and CAP, COVID-19 neutrophils demonstrated elevated transmigration (p = 0.0397) and NETosis (p = 0.0332), and impaired phagocytosis (p = 0.0036) associated with impaired ROS generation (p < 0.0001). The percentage of CD54+ neutrophils (p < 0.001) was significantly increased, while surface expression of CD11b (p = 0.0014) and PD-L1 (p = 0.006) were significantly decreased in COVID-19. COVID-19 and CAP patients showed increased systemic markers of NETosis including increased cfDNA (p = 0.0396) and impaired DNAse activity (p < 0.0001). The ex vivo inhibition of PI3K γ and δ reduced NET release by COVID-19 neutrophils (p = 0.0129). Conclusions: COVID-19 is associated with neutrophil dysfunction across all main effector functions, with altered phenotype, elevated migration and NETosis, and impaired antimicrobial responses. These changes highlight that targeting neutrophil function may help modulate COVID-19 severity.

Symptoms and risk factors for long COVID in non-hospitalized adults.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with a range of persistent symptoms impacting everyday functioning, known as post-COVID-19 condition or long COVID. We undertook a retrospective matched cohort study using a UK-based primary care database, Clinical Practice Research Datalink Aurum, to determine symptoms that are associated with confirmed SARS-CoV-2 infection beyond 12 weeks in non-hospitalized adults and the risk factors associated with developing persistent symptoms. We selected 486,149 adults with confirmed SARS-CoV-2 infection and 1,944,580 propensity score-matched adults with no recorded evidence of SARS-CoV-2 infection. Outcomes included 115 individual symptoms, as well as long COVID, defined as a composite outcome of 33 symptoms by the World Health Organization clinical case definition. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for the outcomes. A total of 62 symptoms were significantly associated with SARS-CoV-2 infection after 12 weeks. The largest aHRs were for anosmia (aHR 6.49, 95% CI 5.02-8.39), hair loss (3.99, 3.63-4.39), sneezing (2.77, 1.40-5.50), ejaculation difficulty (2.63, 1.61-4.28) and reduced libido (2.36, 1.61-3.47). Among the cohort of patients infected with SARS-CoV-2, risk factors for long COVID included female sex, belonging to an ethnic minority, socioeconomic deprivation, smoking, obesity and a wide range of comorbidities. The risk of developing long COVID was also found to be increased along a gradient of decreasing age. SARS-CoV-2 infection is associated with a plethora of symptoms that are associated with a range of sociodemographic and clinical risk factors.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Therapies for Long COVID in non-hospitalised individuals: from symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study).

INTRODUCTION: Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. METHODS AND ANALYSIS: A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. TRIAL REGISTRATION NUMBER: 1567490.

How do we identify acute medical admissions that are suitable for same day emergency care?

Medical emergencies causing unplanned hospital admission place considerable demands on acute healthcare services. Some patients can be assessed and treated through ambulatory pathways without inpatient admission, via same day emergency care (SDEC), potentially benefiting patients and reducing demands on inpatient services. There is currently considerable variation within acute medicine in aspects of SDEC delivery ranging from overall service design to patient selection methods. Scoring systems identifying patients likely to be successfully managed through SDEC services have been suggested, but evidence of utility in diverse populations is lacking. Specific scoring systems exist for some common medical problems, including cardiac chest pain and pulmonary embolism, but further research is needed to demonstrate how these are most effectively incorporated into SDEC services. This review defines SDEC and describes the variation in services nationally. It reviews the evidence for their clinical impact, tools to screen patients for SDEC and current gaps in our knowledge regarding service deployment.

The clinical profile and associated mortality in people with and without diabetes with Coronavirus disease 2019 on admission to acute hospital services.

INTRODUCTION: To assess if in adults with COVID-19, whether those with diabetes and complications (DM+C) present with a more severe clinical profile and if that relates to increased mortality, compared to those with diabetes with no complications (DM-NC) and those without diabetes. METHODS: Service-level data was used from 996 adults with laboratory confirmed COVID-19 who presented to the Queen Elizabeth Hospital Birmingham, UK, from March to June 2020. All individuals were categorized into DM+C, DM-NC, and non-diabetes groups. Physiological and laboratory measurements in the first 5 days after admission were collated and compared among groups. Cox proportional hazards regression models were used to evaluate associations between diabetes status and the risk of mortality. RESULTS: Among the 996 individuals, 104 (10.4%) were DM+C, 295 (29.6%) DM-NC and 597 (59.9%) non-diabetes. There were 309 (31.0%) in-hospital deaths documented, 40 (4.0% of total cohort) were DM+C, 99 (9.9%) DM-NC and 170 (17.0%) non-diabetes. Individuals with DM+C were more likely to present with high anion gap/metabolic acidosis, features of renal impairment, and low albumin/lymphocyte count than those with DM-NC or those without diabetes. There was no significant difference in mortality rates among the groups: compared to individuals without diabetes, the adjusted HRs were 1.39 (95% CI 0.95-2.03, p = 0.093) and 1.18 (95% CI 0.90-1.54, p = 0.226) in DM+C and DM-C, respectively. CONCLUSIONS: Those with COVID-19 and DM+C presented with a more severe clinical and biochemical profile, but this did not associate with increased mortality in this study.

Rapid adaptation of a local healthcare digital system to COVID-19: The experience in Birmingham (UK).

BACKGROUND: The COVID-19 pandemic created unprecedented pressure on hospitals globally. Digital tools developed before the crisis provided novel aspects of management, and new digital tools were rapidly developed as the crisis progressed. In our institution, a digitally mature NHS Trust in England which builds software systems, development during the early months of the crisis allowed increased patient safety and care, efficient management of the hospital and publication of data. The aim of this paper is to present this experience as a case study, describing development and lessons learned applicable to wider electronic healthcare record development. METHODS: Request, triage, build and test processes for the digital systems were altered in response to the pandemic. Senior Responsible Officers appointed for the emergency triaged all changes and were supported by expert opinion and research active clinicians. Build and test cycles were compressed. New tools were built or existing ones modified in the central Electronic Healthcare Record, PICS (Prescribing, Information and Communication System), Clinical Dashboards and video platforms for remote consultation were developed. FINDINGS: 2236 patients were admitted to UHB with suspected COVID-19 between March and May 2020. Dashboards and visualisation tools enabled by efficient real-time data collection for all new patients, contributed to strategic, operational and clinical decision making.Over 70 urgent changes were made to digital systems, including a screening proforma, improved infection control functions, help and order panels, data dashboards, and updated prescribing features. Novel uses were found for existing functions. INTERPRETATION: Digital tools contributed to a co-ordinated response to COVID-19 in an area with a high disease burden. Change management processes were modified during the pandemic and successfully delivered rapid software modifications and new tools. Principal benefits came from the ability to adapt systems to rapidly changing clinical situations. Lessons learned from this intense development period are widely applicable to EHR development. LAY SUMMARY: Digital tools, which are well designed, can help clinicians and safeguard patients. Health crises such as the COVID pandemic drove rapid development of digital tools. This case study outlines accelerated development within a governance framework that successfully reused existing tools and built new ones. The lessons from this development are generalizable to digital developments in healthcare.

Symptoms, complications and management of long COVID: a review.

Globally, there are now over 160 million confirmed cases of COVID-19 and more than 3 million deaths. While the majority of infected individuals recover, a significant proportion continue to experience symptoms and complications after their acute illness. Patients with 'long COVID' experience a wide range of physical and mental/psychological symptoms. Pooled prevalence data showed the 10 most prevalent reported symptoms were fatigue, shortness of breath, muscle pain, joint pain, headache, cough, chest pain, altered smell, altered taste and diarrhoea. Other common symptoms were cognitive impairment, memory loss, anxiety and sleep disorders. Beyond symptoms and complications, people with long COVID often reported impaired quality of life, mental health and employment issues. These individuals may require multidisciplinary care involving the long-term monitoring of symptoms, to identify potential complications, physical rehabilitation, mental health and social services support. Resilient healthcare systems are needed to ensure efficient and effective responses to future health challenges.

Renin-angiotensin system inhibitors and susceptibility to COVID-19 in patients with hypertension: a propensity score-matched cohort study in primary care.

INTRODUCTION: Renin-angiotensin system (RAS) inhibitors have been postulated to influence susceptibility to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This study investigated whether there is an association between their prescription and the incidence of COVID-19 and all-cause mortality. METHODS: We conducted a propensity-score matched cohort study comparing the incidence of COVID-19 among patients with hypertension prescribed angiotensin-converting enzyme I (ACE) inhibitors or angiotensin II type-1 receptor blockers (ARBs) to those treated with calcium channel blockers (CCBs) in a large UK-based primary care database (The Health Improvement Network). We estimated crude incidence rates for confirmed/suspected COVID-19 in each drug exposure group. We used Cox proportional hazards models to produce adjusted hazard ratios for COVID-19. We assessed all-cause mortality as a secondary outcome. RESULTS: The incidence rate of COVID-19 among users of ACE inhibitors and CCBs was 9.3 per 1000 person-years (83 of 18,895 users [0.44%]) and 9.5 per 1000 person-years (85 of 18,895 [0.45%]), respectively. The adjusted hazard ratio was 0.92 (95% CI 0.68 to 1.26). The incidence rate among users of ARBs was 15.8 per 1000 person-years (79 out of 10,623 users [0.74%]). The adjusted hazard ratio was 1.38 (95% CI 0.98 to 1.95). There were no significant associations between use of RAS inhibitors and all-cause mortality. CONCLUSION: Use of ACE inhibitors was not associated with the risk of COVID-19 whereas use of ARBs was associated with a statistically non-significant increase compared to the use of CCBs. However, no significant associations were observed between prescription of either ACE inhibitors or ARBs and all-cause mortality.

Mitigating Health Risks to Reopen a Clinical Research Laboratory During the COVID-19 Pandemic: A Framework.

BACKGROUND: The COVID-19 pandemic has led to many countries implementing lockdown procedures, resulting in the suspension of laboratory research. With lockdown measures now easing in some areas, many laboratories are preparing to reopen. This is particularly challenging for clinical research laboratories due to the dual risk of patient samples carrying the virus that causes COVID-19, SARS-CoV-2, and the risk to patients being exposed to research staff during clinical sampling. To date, no confirmed transmission of the virus has been confirmed within a laboratory setting; however, operating processes and procedures should be adapted to ensure safe working of samples of positive, negative, or unknown COVID-19 status. OBJECTIVE: In this paper, we propose a framework for reopening a clinical research laboratory and resuming operations with the aim to maximize research capacity while minimizing the risk to research participants and staff. METHODS: This framework was developed by consensus among experienced laboratory staff who have prepared to reopen a clinical research laboratory. RESULTS: Multiple aspects need to be considered to reopen a clinical laboratory. We describe our process to stratify projects by risk, including assessment of donor risk and COVID-19 clinical status, the COVID-19 status of the specific sample type, and how to safely process each sample type. We describe methods to prepare the laboratory for safe working including maintaining social distancing through signage, one-way systems and access arrangements for staff and patients, limiting staff numbers on site and encouraging home working for all nonlaboratory tasks including data analysis and writing. Shared equipment usage was made safe by adapting booking systems to allow for the deployment of cleaning protocols. All risk assessments and standard operating procedures were rewritten and approved by local committees, and staff training was initiated to ensure compliance. CONCLUSIONS: Laboratories can adopt and adapt this framework to expedite reopening a clinical laboratory during the current COVID-19 pandemic while mitigating the risk to research participants and staff.

Ethnicity and risk of death in patients hospitalised for COVID-19 infection in the UK: an observational cohort study in an urban catchment area.

BACKGROUND: Studies suggest that certain black and Asian minority ethnic groups experience poorer outcomes from COVID-19, but these studies have not provided insight into potential reasons for this. We hypothesised that outcomes would be poorer for those of South Asian ethnicity hospitalised from a confirmed SARS-CoV-2 infection, once confounding factors, health-seeking behaviours and community demographics were considered, and that this might reflect a more aggressive disease course in these patients. METHODS: Patients with confirmed SARS-CoV-2 infection requiring admission to University Hospitals Birmingham NHS Foundation Trust (UHB) in Birmingham, UK between 10 March 2020 and 17 April 2020 were included. Standardised admission ratio (SAR) and standardised mortality ratio (SMR) were calculated using observed COVID-19 admissions/deaths and 2011 census data. Adjusted HR for mortality was estimated using Cox proportional hazard model adjusting and propensity score matching. RESULTS: All patients admitted to UHB with COVID-19 during the study period were included (2217 in total). 58% were male, 69.5% were white and the majority (80.2%) had comorbidities. 18.5% were of South Asian ethnicity, and these patients were more likely to be younger and have no comorbidities, but twice the prevalence of diabetes than white patients. SAR and SMR suggested more admissions and deaths in South Asian patients than would be predicted and they were more likely to present with severe disease despite no delay in presentation since symptom onset. South Asian ethnicity was associated with an increased risk of death, both by Cox regression (HR 1.4, 95% CI 1.2 to 1.8), after adjusting for age, sex, deprivation and comorbidities, and by propensity score matching, matching for the same factors but categorising ethnicity into South Asian or not (HR 1.3, 95% CI 1.0 to 1.6). CONCLUSIONS: Those of South Asian ethnicity appear at risk of worse COVID-19 outcomes. Further studies need to establish the underlying mechanistic pathways.

COVID-19: to be or not to be; that is the diagnostic question.

Since the first cases in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread across the globe, resulting in the COVID-19 pandemic. Early clinical experiences have demonstrated the wide spectrum of SARS-CoV-2 presentations, including various reports of atypical presentations of COVID-19 and possible mimic conditions.This article summarises the current evidence surrounding atypical presentations of COVID-19 including neurological, cardiovascular, gastrointestinal, otorhinolaryngology and geriatric features. A case from our hospital of pneumocystis pneumonia initially suspected to be COVID-19 forms the basis for a discussion surrounding mimic conditions of COVID-19. The dual-process model of clinical reasoning is used to analyse the thought processes used to make a diagnosis of COVID-19, including consideration of the variety of differential diagnoses.While SARS-CoV-2 is likely to remain on the differential diagnostic list for a plethora of presentations for the foreseeable future, clinicians should be cautious of ignoring other potential diagnoses due to availability bias. An awareness of atypical presentations allows SARS-CoV-2 to be a differential so that it can be appropriately investigated. A knowledge of infectious mimics prevents COVID-19 from overshadowing other diagnoses, hence preventing delayed diagnosis or even misdiagnosis and consequent adverse outcomes for patients.