BackgroundImmunocompromised patients may be at higher risk of mortality if hospitalised with Coronavirus Disease 2019 (COVID-19) compared with immunocompetent patients. However, previous studies have been contradictory. We aimed to determine whether immunocompromised patients were at greater risk of in-hospital death and how this risk changed over the pandemic.Methods and findingsWe included patients > = 19 years with symptomatic community-acquired COVID-19 recruited to the ISARIC WHO Clinical Characterisation Protocol UK prospective cohort study. We defined immunocompromise as immunosuppressant medication preadmission, cancer treatment, organ transplant, HIV, or congenital immunodeficiency. We used logistic regression to compare the risk of death in both groups, adjusting for age, sex, deprivation, ethnicity, vaccination, and comorbidities. We used Bayesian logistic regression to explore mortality over time. Between 17 January 2020 and 28 February 2022, we recruited 156,552 eligible patients, of whom 21,954 (14%) were immunocompromised. In total, 29% (n = 6,499) of immunocompromised and 21% (n = 28,608) of immunocompetent patients died in hospital. The odds of in-hospital mortality were elevated for immunocompromised patients (adjusted OR 1.44, 95% CI [1.39, 1.50], p < 0.001). Not all immunocompromising conditions had the same risk, for example, patients on active cancer treatment were less likely to have their care escalated to intensive care (adjusted OR 0.77, 95% CI [0.7, 0.85], p < 0.001) or ventilation (adjusted OR 0.65, 95% CI [0.56, 0.76], p < 0.001). However, cancer patients were more likely to die (adjusted OR 2.0, 95% CI [1.87, 2.15], p < 0.001). Analyses were adjusted for age, sex, socioeconomic deprivation, comorbidities, and vaccination status. As the pandemic progressed, in-hospital mortality reduced more slowly for immunocompromised patients than for immunocompetent patients. This was particularly evident with increasing age: the probability of the reduction in hospital mortality being less for immunocompromised patients aged 50 to 69 years was 88% for men and 83% for women, and for those >80 years was 99% for men and 98% for women. The study is limited by a lack of detailed drug data prior to admission, including steroid doses, meaning that we may have incorrectly categorised some immunocompromised patients as immunocompetent.ConclusionsImmunocompromised patients remain at elevated risk of death from COVID-19. Targeted measures such as additional vaccine doses, monoclonal antibodies, and nonpharmaceutical preventive interventions should be continually encouraged for this patient group.Trial registrationISRCTN 66726260.

Background: Patient reported outcome measures (PROMs) provide a standardised method to capture patient perspectives which can be used in comparative effectiveness research to inform the evaluation of treatment. Thus far, PROMs have focussed on the long-term sequelae of COVID-19 rather than the acute illness and initial recovery period. Aim: To design a psychometrically validated COVID-19 specific PROM to be used in the acute and recovery phase of illness. Methods: A review of existing literature, evaluation of existing PROMs, input from local experts (n=15) and in-depth qualitative concept elicitation interviews with patients (n=8) were used to create a conceptual framework which informed the generation of items included in the PROM. Cognitive interviews with patients (n=8) were then used to develop and refine the items for inclusion in the PROM and confirm the content and face validity of the draft tool. A nominal group meeting of the expert panel (n=6) was held to confirm the final items for inclusion. Results: The CoV-Sym PROM consists of 19 domains and 44 items which are scored using a five-point Likert scale to enable objective measurement of patients’ symptomatic recovery from acute COVID-19 illness. Questions address physical, psychological and social domains. Cognitive interviews revealed acceptable content and face validity. Conclusion: With the involvement of both patients and experts in the development and validation process, we have created the first COVID-19 specific PROM to measure patient’s symptomatic recovery from acute COVID-19 infection. It is ready for further psychometric testing to confirm reliability and responsiveness, the results of which will be presented.

Background Sleep disturbance is common following hospitalisation both for COVID-19 and other causes. The clinical associations are poorly understood, despite it altering pathophysiology in other scenarios. We, therefore, investigated whether sleep disturbance is associated with dyspnoea along with relevant mediation pathways. Methods Sleep parameters were assessed in a prospective cohort of patients (n=2,468) hospitalised for COVID-19 in the United Kingdom in 39 centres using both subjective and device-based measures. Results were compared to a matched UK biobank cohort and associations were evaluated using multivariable linear regression. Findings 64% (456/714) of participants reported poor sleep quality; 56% felt their sleep quality had deteriorated for at least 1-year following hospitalisation. Compared to the matched cohort, both sleep regularity (44.5 vs 59.2, p<0.001) and sleep efficiency (85.4% vs 88.5%, p<0.001) were lower whilst sleep period duration was longer (8.25h vs 7.32h, p<0.001). Overall sleep quality (effect estimate 4.2 (3.0–5.5)), deterioration in sleep quality following hospitalisation (effect estimate 3.2 (2.0–4.5)), and sleep regularity (effect estimate 5.9 (3.7–8.1)) were associated with both dyspnoea and impaired lung function (FEV[1][1] and FVC). Depending on the sleep metric, anxiety mediated 13–42% of the effect of sleep disturbance on dyspnoea and muscle weakness mediated 29-43% of this effect. Interpretation Sleep disturbance is associated with dyspnoea, anxiety and muscle weakness following COVID-19 hospitalisation. It could have similar effects for other causes of hospitalisation where sleep disturbance is prevalent. Funding UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by UK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was ethically approved by a NHS research ethics committee. The reference is (Ref: 20/YH/0225) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors <https://www.phosp.org/> [1]: #ref-1

Background The BTS national audit on Community Acquired Pneumonia (CAP) reported no differences in outcome when comparing weekend and weekday admissions due to CAP (Lawrence et al Thorax 2020;75:594–596). We wished to compare weekend and weekday hospitalisations due to CAP within the Advancing Quality (AQ) dataset encompassing a 12-year period to see if these mirror the findings from the BTS audit. Methodology An analysis was performed of CAP admissions in the AQ Pneumonia Program from May 2010–2022. For submission, the diagnosis of CAP must be made by a consultant physician within 24 hours of hospital admission along with compatible CXR findings. Comorbidity was measured using the Charlson Comorbidity Index (CCI) Results 117,953 admissions with CAP (mean age 72 (16) years, 44% female; CCI 1.69 (1.68)) were analysed with a length of stay (LOS) of 9.64 (SD 12.57) days and in-hospital mortality of 12.5% (see table). 26% of admissions (n=30,378) occurred at weekends with weekend admission not associated with elevated mortality. A greater proportion were admitted with severe CAP (CURB 65 score 3–5) during weekends (29.4% v 28.3%; p=0.01) but no significant difference in age or CCI was observed between weekdays and weekends. Despite this, the overall LOS was significantly lower in the weekend admissions compared to the weekdays (9.43 (12.78) v 9.71 (12.49) days; p<0.001). Whilst those admissions receiving a CXR within 4 hours of admission did not differ overall between weekdays and weekends, significantly more patients received antibiotics within 4 hours of admission in the weekends compared to weekdays (54% v 50.7%; p<0.001). A greater proportion of patients presenting with severe CAP received antibiotics within 4 hours of admission (69.4% v 60.6%; p<0.001) and the CCI was also greater in this group (1.85 (1.66) v 1.51 (1.67); p<0.001). Conclusion Community Acquired Pneumonia admitted during the weekend was associated with increased severity at presentation but not linked to delays in treatment and worse outcomes when compared to weekdays. Further analysis is needed examining longitudinal trends in this data if we are to truly understand the impact of changes in national policy and practice and to shape future therapy goals.

Introduction Shared characteristics between COVID-19 and pulmonary fibrosis, including symptoms, genetic architecture, and circulating biomarkers, suggests interstitial lung disease (ILD) development may be associated with SARS-CoV-2 infection. Methods The UKILD Post-COVID study planned interim analysis was designed to stratify risk groups and estimate the prevalence of Post-COVID Interstitial Lung Damage (ILDam) using the Post-HOSPitalisation COVID-19 (PHOSP-COVID) Study. Demographics, radiological patterns and missing data were assessed descriptively. Bayes binomial regression was used to estimate the risk ratio of persistent lung damage >10% involvement in linked, clinically indicated CT scans. Indexing thresholds of percent predicted DLco, chest X-ray findings and severity of admission were used to generate risk strata. Number of cases within strata were used to estimate the amount of suspected Post-COVID ILDam. Results A total 3702 people were included in the UKILD interim cohort, 2406 completed an early follow-up research visit within 240 days of discharge and 1296 had follow-up through routine clinical review. We linked the cohort to 87 clinically indicated CTs with visually scored radiological patterns (median 119 days from discharge; interquartile range 83 to 155, max 240), of which 74 people had ILDam. ILDam was associated with abnormal chest X-ray (RR 1.21 95%CrI 1.05; 1.40), percent predicted DLco<80% (RR 1.25 95%CrI 1.00; 1.56) and severe admission (RR 1.27 95%CrI 1.07; 1.55). A risk index based on these features suggested 6.9% of the interim cohort had moderate to very-high risk of Post-COVID ILDam. Comparable radiological patterns were observed in repeat scans >90 days in a subset of participants. Conclusion These interim data highlight that ILDam was not uncommon in clinically indicated thoracic CT up to 8 months following SARS-CoV-2 hospitalisation. Whether the ILDam will progress to ILD is currently unknown, however health services should radiologically and physiologically monitor individuals who have Post-COVID ILDam risk factors. ### Competing Interest Statement JJ reports fees from Boehringer Ingelheim, F. Hoffmann-La Roche, GlaxoSmithKline, NHSX, Takeda and patent: UK patent application number 2113765.8 all unrelated to the submitted work. PMG reports honoraria from Boehringer Ingelheim, Roche, AstraZeneca, Cipla, Brainomix. JCP reports grants from LifeArc, NIHR, Breathing Matters, consulting fees from Carrick Therapeutics, AstraZeneca and honoraria from The Limbic. RAE reports speaker fees from Boehringer Ingelheim and membership positions on European Respiratory Society and American Thoracic Society committees. PM reports consulting fees from EUSA pharma and SOBI, and honoraria from SOBI, UCB, Lilly, and Abbvie. MGS reports grants from NIHR, MRC, board positions on Pfizer External Data Monitoring Committee and Integrum Scientific LLC Infectious Disease Scientific Advisory Board, member positions of HMG UK SAGE and MHG UK NERVTAG, stocks in Integrum Scientific LLC and MedEx Solutions Ltd, gifts from Chiesi Farmaceutici S.p.A. AART reports grants and travel support from Janssen-Cilag Ltd. CEB reports consultancy fees paid to institution from GSK, AstraZeneca, Sanofi, Boehringer Ingelheim, Chiesi, Novartis, Roche, Genentech, Mologic, 4DPharma, TEVA. LVW reports recent and current research funding from GSK and Orion, and consultancy from Galapagos. RGJ reports honoraria from Chiesi, Roche, PatientMPower, AstraZeneca, GSK, Boehringer Ingelheim, and consulting fees from Bristol Myers Squibb, Daewoong, Veracyte, Resolution Therapeutics, RedX, Pliant, Chiesi. All remaining authors declare no competing interests. ### Funding Statement Jointly funded by UK Research and Innovation and National Institute of Health Research (grant references: MR/V027859/1 and COV0319). Ethics Approval Ethics Ref: 20/YH/0225 The authors would like to acknowledge the support of the eDRIS Team (Public Health Scotland) for their involvement in obtaining approvals, provisioning and linking data and the use of the secure analytical platform within the National Safe Haven. This study would not be possible without all the participants who have given their time and support. We thank all the participants and their families. We thank the many research administrators, health-care and social-care professionals who contributed to setting up and delivering the study at all of the 65 NHS trusts/Health boards and 25 research institutions across the UK, as well as all the supporting staff at the NIHR Clinical Research Network, Health Research Authority, Research Ethics Committee, Department of Health and Social Care, Public Health Scotland, and UK Health Security Agency, and support from the ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C). We thank Kate Holmes at the NIHR Office for Clinical Research Infrastructure (NOCRI) for her support in coordinating the charities group. The PHOSP-COVID industry framework was formed to provide advice and support in commercial discussions, and we thank the Association of the British Pharmaceutical Industry as well as Ivana Poparic and Peter Sargent at NOCRI for coordinating this. We are very grateful to all the charities that have provided insight to the study: Action Pulmonary Fibrosis, Alzheimers Research UK, Asthma & Lung UK, British Heart Foundation, Diabetes UK, Cystic Fibrosis Trust, Kidney Research UK, MQ Mental Health, Muscular Dystrophy UK, Stroke Association Blood Cancer UK, McPin Foundations, and Versus Arthritis. We thank the NIHR Leicester Biomedical Research Centre patient and public involvement group and the Long Covid Support Group. JB acknowledges MRC Transition Fellowship (MR/T032529/1) and Manchester BRC funding. BG acknowledges UKRI-MRC Programme Grant and Confidence in Concept Grant, British Lung Foundation and the NIHR Leicester BRC funding. BGG acknowledges funding from Wellcome Trust grant 221680/Z/20/Z. NG is funded by an NIHR fellowship. PLM and RJA are funded by the Action for Pulmonary Fibrosis Mike Bray Fellowships. DGW is funded by an NIHR Advanced Fellowship. JP is supported by UKRI PC-ILD grant, Breathing Matters Charity, and UCLH/UCL funding from the Department of Health NIHR Biomedical Research Centres funding scheme. AART is funded by an Intermediate Clinical Fellowship from the British Heart Foundation (FS/18/13/33281). LVW is supported by GSK / Asthma + Lung UK Chair in Respiratory Research (C17-1). GJ acknowledges funding from a NIHR Research Professorship. IS fellowship is funded by the Rayne Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Study has received ethical approval from the Yorkshire and Humber - Leeds West Research Ethics Committee and approval from the Health Research Authority. Ethics Approval Ref: 20/YH/0225 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data were obtained through the PHOSP-COVID Study

Background Sleep disturbance is common following hospitalisation both for COVID-19 and other causes. The clinical associations are poorly understood, despite it altering pathophysiology in other scenarios. We, therefore, investigated whether sleep disturbance is associated with dyspnoea along with relevant mediation pathways. Methods Sleep parameters were assessed in a prospective cohort of patients (n=2,468) hospitalised for COVID-19 in the United Kingdom in 39 centres using both subjective and device-based measures. Results were compared to a matched UK biobank cohort and associations were evaluated using multivariable linear regression. Findings 64% (456/714) of participants reported poor sleep quality; 56% felt their sleep quality had deteriorated for at least 1-year following hospitalisation. Compared to the matched cohort, both sleep regularity (44.5 vs 59.2, p<0.001) and sleep efficiency (85.4% vs 88.5%, p<0.001) were lower whilst sleep period duration was longer (8.25h vs 7.32h, p<0.001). Overall sleep quality (effect estimate 4.2 (3.0-5.5)), deterioration in sleep quality following hospitalisation (effect estimate 3.2 (2.0-4.5)), and sleep regularity (effect estimate 5.9 (3.7-8.1)) were associated with both dyspnoea and impaired lung function (FEV1 and FVC). Depending on the sleep metric, anxiety mediated 13-42% of the effect of sleep disturbance on dyspnoea and muscle weakness mediated 29-43% of this effect. Interpretation Sleep disturbance is associated with dyspnoea, anxiety and muscle weakness following COVID-19 hospitalisation. It could have similar effects for other causes of hospitalisation where sleep disturbance is prevalent.

Introduction and Objectives The COVID-19 pandemic is ongoing yet, due to the lack of a COVID-19 specific tool, clinicians must use pre-existing illness severity scores for initial prognostication. However, the validity of such scores in COVID-19 is unknown. The aim of this study was to determine the performance characteristics of these scores in the context of COVID-19 and to investigate potential components of a COVID-19 specific prognostication tool for future validation. Methods The North West Collaborative Organization for Respiratory Research (NW-CORR), a group of research-interested higher specialty trainees, performed a multi-centre prospective evaluation of adult patients admitted to hospital with confirmed COVID-19 during a two-week period in April 2020. Clinical variables measured as part of usual care at presentation to hospital were recorded, including the CURB-65, NEWS2, and qSOFA scores. Outcomes of interest were 30-day and 72-hour mortality. Scores were compared in terms of calibration and discrimination with multivariable logistic regression performed to assess individual components of each score. Results Data were collected for 830 people with COVID-19 admitted across 7 hospitals. By 30 days, a total of 300 (36.1%) had died and 142 (17.1%) had been in ICU. Calibration plots suggested all scores underestimated mortality compared to their original validation in non-COVID-19 populations, and overall discriminatory ability was generally sub-optimal (AUCs 0.62–0.77). Among the 'low risk' categories (CURB-65<2, NEWS2<5, qSOFA<2) 30-day mortality was 16.7% (vs 1.5% in CAP), 32.9% (vs 5.5% in sepsis) and 21.4% (vs 4.3% in infection) respectively. The diagnostic performances of each score are presented in table 1. Multivariable logistic regression identified features associated with respiratory compromise rather than circulatory collapse as most relevant prognostic variables. Conclusion ll existing prognostic scores evaluated here underestimated adverse outcomes and performed sub-optimally in the COVID-19 setting. New prognostic tools including a focus on features of respiratory compromise rather than circulatory collapse are needed. We provide a baseline set of variables which are relevant to COVID-19 outcomes and may be used as a basis for developing a bespoke COVID-19 prognostication tool. This collaborative project demonstrates the ability of regional trainee networks to collate large datasets to address important clinical questions.

Background There is a paucity of UK data to aid healthcare professionals in predicting which patients hospitalized with Community Acquired Pneumonia (CAP) are at greatest risk of readmission and to determine which readmissions may occur soonest. Methodology An analysis of CAP cases admitted between 1/1/2017 and 31/3/2019 to 9 hospitals in Northwest England participating in the Advancing Quality Pneumonia program. For entry into the AQ program, patients hospitalised with CAP require the diagnosis to be made by a Consultant Physician along with a chest radiograph compatible with pneumonia Results 12,144 subjects with CAP (mean age 73 years (SD 16)) were admitted during the study period. Mean Charlson Comorbidity Index (CCI) was 9.47 (SD 8.81) and in-hospital mortality was 14.7%. 2691 (26%) were readmitted within 30 days of discharge. Readmission was predicted by severe liver disease (aOR = 2.43), non-metastatic cancer (aOR = 1.72), Diabetes with complications (aOR = 1.64), Chronic Kidney Disease (aOR = 1.25), Congestive Cardiac Failure (aOR = 1.16), Ischaemic Heart Disease (aOR = 1.16) and longer Length of Stay (LOS). 24% of those readmitted had Pneumonia as the principal readmission diagnosis. 41% of readmissions occurred within 7 days of discharge; 25% between day 8–14 and the remaining 34% between 14 to 30 days post discharge. Comparing patients readmitted within 14 days with those readmitted 14–30 post discharge, earlier readmissions were older (72 years (SD 14.72) v 71 years (SD 14.08) p=0.01) and have a diagnosis of metastatic cancer (6.6% v 4.4%; p=0.02). Of the readmitted patients who had a comorbidity, none with Severe Liver Disease had a principal readmission diagnosis of Pneumonia compared with 23% of those with Ischaemic Heart Disease, 20% with Congestive Cardiac Failure, 27% with Metastatic Cancer and 23% with Non-Metastatic Cancer. Discussion A quarter of patients who survive to discharge following hospital admission for CAP are subsequently readmitted within 30-days; of those, two thirds are readmitted within 2 weeks pointing to an unacceptable quality of care. Many readmissions may be preventable by measures including implementation of in-hospital cross-speciality comorbidity management, convalescence in intermediate care, targeted rehabilitation and early clinical review in the community.

The impact of hospital-acquired pneumonia and the pressure to reduce unnecessary antibiotic prescribing has lead to the publication of prescribing guidelines from the National Institute for Health and Care Excellence. This editorial gives an overview of the guidelines and emphasises the need for more high-quality evidence to inform decision making in this group of patients.

Background Patients admitted to hospital with Community Acquired Pneumonia (CAP) are at risk of readmission within 30 days of discharge. There is little UK evidence aiding healthcare professionals predict which CAP patients are at greatest risk of readmission. Methodology This study analyzed the Advancing Quality Alliance (AQuA) Pneumonia database. (https://www.aquanw.nhs.uk/events/advancing-quality-pneumonia/80258.), a CAP Quality Improvement program in the Northwest of England from October 2016 to March 2019. 30-day readmission was defined as any admission for the same patient within 30 days of discharge following the index admission. Patient comorbidities were identified using ICD10 diagnosis codes in the patient spell. Results A total of 12,144 adults (mean age 73 (SD16) years; 47% male) admitted with CAP were submitted to the AQ database during the study period. The in-hospital mortality was 14.7% (1791/12,144). Of the 10,353 cases discharged from hospital, 26% (2691) were readmitted within 30 days of discharge with 34% (913/2691) of readmissions being coded specifically due to Pneumonia. After applying multivariate analysis, the following factors emerged as significant predictors of 30 day readmission: a history of Chronic Kidney Disease (15.9% in those readmitted v 13.1% in those not readmitted), Congestive Cardiac Failure (16.8% v 13.9%), Cancer (16.2% v 9.7%), Ischaemic Heart Disease (12.7% v 11%), Diabetes with complications (1.4% v 0.9%) and Severe Liver Disease (0.4%v 0.2%). A longer index hospital stay was also associated with increased likelihood of 30 day readmission (median 6 (IQR 10) v 5 (9) days; p<0.01) whilst a background of Dementia was less likely to be associated with 30 day readmission being present in 5% of those readmitted at 30 days compared with 13.1% of those not readmitted (p=0.01). Conclusion Over a quarter of those patients admitted to hospital with a diagnosis of Community Acquired Pneumonia are readmitted within 30 days of discharge. Key comorbidities such as Cardiac and Renal Disease appear to be significant drivers for readmission. Further studies are required to determine whether optimization of such comorbidity following hospitalization with CAP results in a reduction in readmission rates and improved clinical outcomes.