BACKGROUND:A respiratory syncytial virus (RSV) vaccination programme for older adults using bivalent pre-F vaccine was introduced in England from Sept 1, 2024. Although vaccine effectiveness has been reported against all-cause RSV-associated respiratory hospital admissions, data are scarce on vaccine effectiveness against different presentations of RSV-associated illness, such as exacerbation of chronic illness. METHODS:This multicentre, test-negative, case-control study used data from a national, hospital-based, acute respiratory infection sentinel surveillance (HARISS) system across 14 hospitals in England. Eligibility criteria were vaccine-eligible adults aged 75-79 years admitted to hospital with acute respiratory infection (ARI) for ≥24 h and tested with molecular diagnostic assays within 48 h of admission. Cases were RSV positive, and controls were negative for RSV, influenza, and SARS-CoV-2. Vaccination status and data on sex were obtained from the National Immunisation Information System. The primary outcome was hospital admission due to RSV-associated ARI, which was tested for using nasopharyngeal or combined nose and throat swabs. Clinical data were collected using a structured questionnaire. FINDINGS:Between Oct 1, 2024, and March 31, 2025, 1006 older adults were admitted to hospital with ARI; 173 were RSV positive (cases) and 833 were RSV negative (controls). 526 (52·3%) of 1006 individuals were female and 480 (47·7%) were male. Mean age was 77·8 years (SD 1·4) in individuals who were RSV positive and 77·6 years (SD 1·3) in those who were negative for RSV, influenza, and SARS-CoV-2. Vaccine effectiveness was 82·3% (95% CI 70·6-90·0) against hospitalisation for any RSV-associated ARI and 86·7% (75·4-93·6) in those with severe disease including oxygen supplementation. Vaccine effectiveness was 88·6% (75·6-95·6) among individuals admitted due to lower respiratory tract infection, including pneumonia, 77·4% (42·4-92·8) due to exacerbation of chronic lung disease, and 78·8% (47·8-93·0) due to exacerbation of chronic heart disease, lung disease, and/or frailty. In individuals with immunosuppression, vaccine effectiveness was 72·8% (39·5-89·3). INTERPRETATION:This study provides evidence that the RSV pre-F vaccine is highly effective against RSV-associated hospital admissions, including exacerbations of chronic disease, and in adults with immunosuppression. FUNDING:UK Health Security Agency.

Respiratory diseases are a major cause of death globally, placing a significant burden on healthcare services. Early-stage clinical decision-making is crucial for enabling personalized, prioritized treatment and more efficient allocation of healthcare resources. Clinicians can intervene proactively and develop appropriate treatment plans for patients when provided with vital information such as mortality prediction, deterioration detection, and length-of-stay prediction. To precisely predict such vitals, it is essential to leverage sequential information that is inherent in clinical variables. In this paper, we employ a unified framework for patient outcome forecasting in pneumonia patients. The proposed model utilizes clinical time-series data of varying lengths, along with static admission information, to effectively capture the sequential information of clinical variables. Additionally, we model the imbalanced distribution of mortality prediction and deterioration detection through weight constraints, and we account for the right-skewed distribution of length-of-stay data to enhance the robustness of the model. Furthermore, we develop a data splitting strategy to track dynamic changes in model performance at different timestamps, helping to bridge the gap between testing conditions and real-world scenarios. We conduct experiments on CAP-AI dataset that was obtained and collected from the University Hospitals of Leicester with the involvement of clinicians. It is based on real-world clinical data from patients admitted with pneumonia-related diagnoses. Extensive experimental results demonstrate the effectiveness and robustness of our approach whilst predicting patient outcomes in a clinical setting.

Background Tuberculosis (TB) diagnosis in the UK is impacted by delay and suboptimal culture-based microbiological confirmation rates due to the high prevalence of paucibacillary disease. We examine the real-world clinical utility of Xpert MTB/RIF Ultra (Xpert-Ultra) as a diagnostic test and biomarker of transmissible infection in a UK TB service. Methods Clinical specimens from suspected TB cases triple tested (smear microscopy, mycobacterial culture and Xpert-Ultra) at University Hospitals of Leicester NHS Trust (1 March 2018-28 February 2019) were retrospectively analysed. Diagnostic sensitivity and specificity were calculated using positive MTB culture and clinical TB diagnosis as reference standards. The QuantiFERON (QFT) positive proportion of pulmonary TB (PTB) contacts was used as a metric of transmitted infection to evaluate Xpert-Ultra and smear grade as markers of infectiousness. Results 251 samples (188 respiratory) from 231 patients (86 TB) were analysed. Compared with microscopy, Xpert-Ultra had higher diagnostic sensitivity (24.7% vs 78.7%, p<0.001) and comparable specificity (97.5% vs 99.4%). Xpert-Ultra and culture had comparable sensitivity (78.7% vs 71.9%) and specificity (99.4% vs 100.0%). Incorporating Xpert-Ultra with culture increased microbiologically verified diagnosis to 91.7% for PTB and 75.9% for extrapulmonary TB, compared with 85.0% and 44.8%, using culture alone. In PTB, both smear and Xpert-Ultra grade were positively associated with the proportion of contacts testing QFT positive. However, Xpert-Ultra had a higher negative predictive value than smear (QFT-positive contacts 6.7% vs 17.7%). Conclusion In low-TB-burden settings, systematic adoption of Xpert-Ultra for clinical assessment of suspected TB can improve the proportion of microbiologically verified diagnoses and improve the stratification of transmission risk.

Background: Community-acquired pneumonia (CAP) is an acute respiratory condition associated with high mortality in adult populations and is potentially more serious in older patients. Accurate and consistently applied prediction of outcome may contribute to reduce in-hospital mortality. Currently, CAP outcomes are assessed with clinical scores like CURB65, based on signs and symptoms that are non-specific to the disease. Recent literature has shown that machine learning (ML) has the potential to improve outcome prediction, but the sparse and incomplete nature of the data present a challenge for the development of models that can be implemented clinically. Methods: This study aimed to developed ML models that can support outcome prediction in hospital admissions with CAP using routinely collected and time-dependent data from Leicester hospitals. Thus, by modelling mortality prediction, and predicting URB65 on the third day of admission with the forecast of vital signs, implementing a methodology that explores how different characteristics involved in the training process influence the results of the predictions. Results: Data comprised 9390 admissions in the training set, and 7892 in the validation set, for thirty-four clinical variables (fifteen time-dependent). Results of CAP mortality modelling reported AUC of 0.77 using a GRU model that was trained with the time series of vital signs and blood test. Results also showed improvement in models when balancing classes of the target variable in the training set, as well as improvement when using time dependent data. And importantly when predicting URB65 accuracy of 0.85 was obtained when modelled using GRU, when time series were processed using local scaling. Conclusions: This approach might represent an opportunity to anticipate adverse outcomes. These results suggest that ML models utilising time series can have sizable impact in the prediction of CAP outcome, from many perspectives: Complementing currently applied scoring systems approaches like CURB65 in hospital settings, prediction of mortality or forecasting the severity of patients from vital signs that have shown correlation with CAP mortality. The models presented require further validation and development, although they present important indication for CAP mortality prediction. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was co-funded by the NIHR Leicester Biomedical Research Centre, the University of Leicester and Minciencias Colombia (Colombian Ministry of Science, Technology & Innovation). ### 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: This study was reviewed and approved by the West Midlands - Solihull Research Ethics Committee, with approval number 20/WM/0144. All procedures performed in this study involving clinical data adhered to the ethical standards of the institutional and/or national research committee. 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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data that support the findings of this study are available from the authors but restrictions apply to the availability of these data, which were used under REC reference number 20/WM/0144, and so are not publicly available. Data are, however, available from the authors upon reasonable request and pertinent permissions and amendments of the responsible research committee.

In the context of climate change and increasing global populations, thunderstorm asthma may become a greater threat at both individual and population levels. The unpredictable nature of epidemic thunderstorm asthma events makes them challenging to study; however, they can have devastating consequences. Novel approaches are required to characterise the mechanisms driving these events to allow researchers and other stakeholders to understand who is at risk and when. This will support the development of interventions that protect patients and healthcare services. In this commentary, we provide an overview of thunderstorm asthma and briefly describe an epidemic affecting Leicester, UK in June 2023. Our analysis highlights Cladosporium spores as a key player in mediating UK thunderstorm asthma. Low levels of background treatment in adults and an increase in emergency assessments but not hospitalisations in children suggest that epidemics could be prevented by improving awareness and ensuring access to standard inhaled therapies. Finally, we consider future risk and suggest research priorities with an ultimate goal of minimising the adverse impact related to thunderstorm asthma going forward.

Objectives: We investigated whether quantifying the serial QuantiFERON-TB Gold (QFT) response improves tuberculosis (TB) risk stratification in pulmonary TB (PTB) contacts. Methods: A total of 297 untreated adult household PTB contacts, QFT tested at baseline and 3 months after index notification, were prospectively observed (median 1460 days). Normal variance of serial QFT responses was established in 46 extrapulmonary TB contacts. This informed categorisation of the response in QFT-positive PTB contacts as converters, persistently QFT-positive with significant increase (PPincrease), and without significant increase (PPno-increase). Results: In total, eight co-prevalent TB (disease ≤3 months after index notification) and 12 incident TB (>3 months after index notification) cases were diagnosed. Genetic linkage to the index strain was confirmed in all culture-positive progressors. The cumulative 2-year incident TB risk in QFT-positive contacts was 8.4% (95% confidence interval, 3.0-13.6%); stratifying by serial QFT response, significantly higher risk was observed in QFT converters (28%), compared with PPno-increase (4.8%) and PPincrease (3.7%). Converters were characterised by exposure to index cases with a shorter interval from symptom onset to diagnosis (median reduction 50.0 days, P = 0.013). Conclusions: QFT conversion, rather than quantitative changes of a persistently positive serial QFT response, is associated with greater TB risk and exposure to rapidly progressive TB.

Background Incipient tuberculosis, a progressive state of Mycobacterium tuberculosis infection with an increased risk of developing into tuberculosis disease, remains poorly characterised. Animal models suggest an association of progressive infection with bacteraemia. Circulating M tuberculosis DNA has previously been detected in pulmonary tuberculosis by use of Actiphage, a bacteriophage-based real-time PCR assay. We aimed to investigate whether serial [18F]fluorodeoxyglucose ([18F]FDG)-PET-CT could be used to characterise the state and progressive trajectory of incipient tuberculosis, and examine whether these PET -CT findings are associated with Actiphage-based detection of circulating M tuberculosis DNA. Methods We did a prospective 12 -month cohort study in healthy, asymptomatic adults (aged >= 16 years) who were household contacts of patients with pulmonary tuberculosis, and who had a clinical phenotype of latent tuberculosis infection, in Leicester, UK. Actiphage testing of participants' blood samples was done at baseline, and [18F]FDG PET -CT at baseline and after 3 months. Baseline PET -CT features were classified as positive, indeterminate, or negative, on the basis of the quantitation (maximum standardised uptake value [SUVmax]) and distribution of [18F]FDG uptake. Microbiological sampling was done at amenable sites of [18F]FDG uptake. Changes in [18F]FDG uptake after 3 months were quantitatively categorised as progressive, stable, or resolving. Participants received treatment if features of incipient tuberculosis, defined as microbiological detection of M tuberculosis or progressive PET -CT change, were identified. Findings 20 contacts were recruited between Aug 5 and Nov 5, 2020; 16 of these participants had a positive result on IFN gamma release assay (QuantiFERON-TB Gold Plus [QFT]) indicating tuberculosis infection. Baseline PET -CT scans were positive in ten contacts (all QFT positive), indeterminate in six contacts (three QFT positive), and negative in four contacts (three QFT positive). Four of eight PET -CT -positive contacts sampled had M tuberculosis identified (three through culture, one through Xpert MTB/RIF Ultra test) from intrathoracic lymph nodes or bronchial wash and received full antituberculosis treatment. Two further unsampled PET -CT -positive contacts were also treated: one with [18F]FDG uptake in the lung (SUVmax 9.4) received empirical antituberculosis treatment and one who showed progressive [18F]FDG uptake received preventive treatment. The ten untreated contacts with [18F]FDG uptake at baseline (seven QFT positive) had stable or resolving changes at follow-up and remained free of tuberculosis disease after 12 months. A positive baseline Actiphage test was associated with the presence of features of incipient tuberculosis requiring treatment (p=0.018). Interpretation Microbiological and inflammatory features of incipient tuberculosis can be visualised on PET -CT and are associated with M tuberculosis detection in the blood, supporting the development of pathogen -directed blood biomarkers of tuberculosis risk.

Rationale: As the prevalence of multimorbidity increases, understanding the impact of isolated comorbidities in people COPD becomes increasingly challenging. A simplified model of common comorbidity patterns may improve outcome prediction and allow targeted therapy. Objectives: To assess whether comorbidity phenotypes derived from routinely collected clinical data in people with COPD show differences in risk of hospitalisation and mortality. Methods: Twelve clinical measures related to common comorbidities were collected during annual reviews for people with advanced COPD and k -means cluster analysis performed. Cox proportional hazards with adjustment for covariates was used to determine hospitalisation and mortality risk between clusters. Measurements and main results: In 203 participants (age 66 +/- 9 years, 60 % male, FEV1%predicted 31 +/- 10 %) no comorbidity in isolation was predictive of worse admission or mortality risk. Four clusters were described: cluster A (cardiometabolic and anaemia), cluster B (malnourished and low mood), cluster C (obese, metabolic and mood disturbance) and cluster D (less comorbid). FEV1%predicted did not significantly differ between clusters. Mortality risk was higher in cluster A (HR 3.73 [95%CI 1.09-12.82] p = 0.036) and B (HR 3.91 [95%CI 1.17-13.14] p = 0.027) compared to cluster D. Time to admission was highest in cluster A (HR 2.01 [95%CI 1.11-3.63] p = 0.020). Cluster C was not associated with increased risk of mortality or hospitalisation. Conclusions: Despite presence of advanced COPD, we report striking differences in prognosis for both mortality and hospital admissions for different co -morbidity phenotypes. Objectively assessing the multi -system nature of COPD could lead to improved prognostication and targeted therapy for patients.

Respiratory diseases are a major cause of death globally, leading to a significant burden on healthcare services. Mortality and length-of-stay prediction are key tasks that help clinicians intervene proactively and develop appropriate treat-ment plans. In our work, we employ a Long Short-Term Memory (LSTM) based model for both mortality and length-of-stay prediction in patients with pneumonia. This model utilizes clinical time-series data of varying lengths, along with static variables from admission information, to effectively capture the sequential information of clinical variables. Additionally, we implement a data splitting strategy to track dynamic changes in model performance at different timestamps, helping to bridge the gap between model development and real-world scenarios. Furthermore, we account for the right-skewed distribution of length-of-stay data during model establishment to enhance its robustness. Extensive experiments on the CAP-AI dataset demonstrate the effectiveness and robustness of our approach for both prediction tasks.

Introduction Tuberculosis infection (TBI) comprises a spectrum of infection states poorly characterised by clinical screening with chest X-ray (CXR) and interferon gamma release assays (IGRA). Here we report utility of PET-CT as a highly sensitive imaging modality to visualise the heterogeneity of TBI. Objectives A descriptive account of findings after serial PET-CT scans with targeted invasive sampling in six immunocompetent household pulmonary TB contacts (HHCs) with normal CXRs recruited to a larger prospective observational study. Methods All recruited HHCs underwent routine CXR and IGRA testing (QuantiFERON-TB Gold Plus (QFT)), followed by 18F-FDG PET-CT soon after index notification. Invasive sampling with bronchoalveolar lavage (BAL) and/or endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) was performed if there was significant PET-CT tracer uptake. QFT-negative participants with baseline 18F-FDG-avid lesions had follow-up PET-CT and repeat QFT after 3 months. Results Three QFT-positive patients with 18F-FDG-avid abnormalities in lung parenchyma and/or intrathoracic lymph nodes that underwent bronchoscopic sampling yielded positive Mycobacterium tuberculosis cultures in BAL (N=2) and EBUS-TBNA (N=1). Time to positive culture ranged from 25 days to 34 days (figure 1). Linkage to index cases was confirmed in two out of three patients on whole genome sequencing. Post-treatment PET-CT showed partial or complete resolution of metabolic activity. Three QFT negative patients that had 18F-FDG-avid intrathoracic lymph nodes at baseline demonstrated QFT conversion after 3 to 6 months. Follow-up PET-CT at the time of QFT conversion showed increasing avidity at baseline sites of tracer uptake (N=2) or appearance of additional 18F-FDG-avid lesions in the mediastinal lymph nodes (N=1) (figure 1). Conclusions PET-CT identifies intrathoracic inflammation in HHCs with a clinical phenotype of latent TBI that predominates in mediastinal nodes and is evident prior to IGRA conversion. Sampling detects metabolically active, culturable TBI in a subset with PET-CT avidity that may be representative of incipient TB. These observations also support the view that M.tuberculosis is transmitted in a metabolically active state. PET-CT offers a tool for characterising the heterogeneity of latent TBI that has utility to support studies of biomarker development and infection pathogenesis.

Introduction Use of rapid molecular diagnostics such as Xpert MTB/RIF and Xpert MTB/RIF Ultra (Xpert-Ultra) is endorsed by World Health Organization for rapid diagnosis of TB. Xpert-Ultra has improved sensitivity, but utility in low TB-prevalent settings, where paucibacillary and extrapulmonary disease are more common, remains uncertain. Current UK National Institute for Health and Care Excellence guidance for use of rapid molecular diagnostics remains unclear. Objectives To examine utility of Xpert-Ultra as a clinical first-line diagnostic tool in pulmonary (PTB) and extrapulmonary TB (EPTB) and to investigate Xpert-Ultra grade as a biomarker of infectiousness in a low TB burden setting. Methods Retrospective analysis of all patients with suspected TB that were triple tested with Xpert-Ultra, smear microscopy and mycobacterial culture at the University Hospitals of Leicester NHS Trust between 01/03/2018 and 28/02/2019. Sensitivity and specificity analyses of all three diagnostic markers were performed using TB culture and TB diagnosis at the primary disease site as independent reference standards. We also investigated the association between Xpert-Ultra grade from index PTB respiratory tract samples, and infectiousness defined as the proportion of close contacts with QuantiFERON-TB Gold plus positive latent TB infection (LTBI). Results 251 samples (188 respiratory samples) were analysed, from 231 patients of whom 86 had TB. 64 samples (63 patients) were Mycobacterium tuberculosis (Mtb) culture positive. 26 samples were smear positive, including four with non-tuberculous mycobacteria. 71 were Xpert-Ultra positive. Compared with culture alone, Xpert-Ultra increased microbiological confirmation of TB diagnosis from 50.0% to 73.1% in EPTB, and from 87.7% to 89.5% in PTB (table 1). Combining both Xpert and culture as a composite for microbiological diagnosis yielded overall sensitivity of 84.6% and 94.7% for the diagnosis of EPTB and PTB, respectively. Overall, 28.6% of 224 screened PTB contacts from 49 PTB cases had LTBI. Stratifying by index Xpert-ultra grade (high/low/negative), the proportion of contacts with LTBI was 45.5%, 17.4% and 4.4% respectively. Conclusions Xpert-Ultra is a highly sensitive and specific TB diagnostic that usefully informs infectiousness of PTB. Our data support using Xpert-Ultra routinely with culture, in the diagnostic assessment of clinically relevant cohorts.

Introduction Disruption to tuberculosis (TB)-control programmes caused by restricted and virtual access to healthcare during the COVID-19 pandemic remains to be fully characterised. Objectives To evaluate performance and patient outcomes of the reconfigured virtual rapid access TB (RATB) service at Leicester (UK), comparing periods before, during and after the pandemic. Methods Retrospective analysis of patient referrals to Leicester RATB services and outcomes between 1st April and 31stMarch, in 2019/2020 (pre-pandemic); 2020/2021 (lockdown period); and 2021/22 (post-lockdown). Results Overall, TB was diagnosed in 270/772 patients. In the lockdown period, the median number of in-person clinic appointments fell, with a corresponding rise in virtual reviews that persisted post-lockdown. There was a decrease in the proportion of UK-origin patients diagnosed and increase in South-Asian patients. There was also a change in the source of referral, with an increased contribution from contact tracing, radiology and in-patients during the lockdown period, likely reflecting limited access to healthcare services and a higher rate of significant household exposure. Although overall interval from symptom onset to starting antituberculous therapy (ATT) was unchanged in the lockdown period, time to starting treatment increased for pulmonary TB (PTB) and fell for extrapulmonary TB (EPTB), likely reflecting attribution of symptoms to COVID-19 for PTB and possible cancer for EPTB in virtually assessed patients. Importantly, interval to starting ATT after symptom onset increased in the post lockdown period that is partially explained by delayed patient presentation, likely reflecting incomplete recovery of primary healthcare services. ATT completion rates were highest (97.7%) during the lockdown period, when case-manager support was facilitated by restricted patient mobility, but dropped post-lockdown (86.3%) with more patients lost to follow-up. Conclusions Our data provides evidence for changes to TB presentation during the pandemic. Expected delays in PTB diagnosis and treatment were observed during lockdown, however these were attributable to delayed presentation to services. A virtual RATB model, with intensive case-manager support provided effective care. Delayed diagnosis and falling treatment completion rates are observed post-lockdown, supporting prioritisation of recovery from COVID-19 in the TB action plan (2021–26).1 Reference https://www.gov.uk/government/publications/tuberculosis-tb-action-plan-for-england/tuberculosis-tb-action-plan-for-england-2021-to-202

Introduction: Tuberculosis infection (TBI) is increasingly recognised as a spectrum of infection. However, current clinical screening tools with chest X-ray (CXR) and interferon gamma release assays (IGRA) are insufficient to characterise underlying heterogeneity. Aims: A descriptive account of outcomes following PET-CT and targeted invasive sampling in four asymptomatic immunocompetent household pulmonary TB contacts with normal CXRs. Methods: The participants were recruited to a prospective observational study and underwent routine screening with CXR and IGRA testing (QuantiFERON-TB Gold Plus (QFT)). A subgroup with positive QFT and normal CXRs had further imaging with 18F-FDG PET-CT. Invasive sampling with bronchoalveolar lavage (BAL) and/or endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) was performed according to imaging findings. Results: All four participants had subtle 18F-FDG avid abnormalities in lung parenchyma and/or mediastinal lymph nodes. Positive M.tuberculosis cultures were obtained in BAL (N=2) and EBUS-TBNA samples (N=2). Time to positive culture ranged from 25 days to 39 days. Conclusions: PET-CT can detect metabolically active culturable TBI, prior to features of subclinical disease becoming evident with clinical screening. These observations may represent incipient TB.

Despite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important that the users can understand and visualise the reasons for the decision support predictions. Plausibility can be hard to achieve for complex pathways and models and perceived “black-box” functionality often leads to a lack of trust. Here, we describe and evaluate a data-driven framework which moderates some of these issues and demonstrate its applicability to the in-hospital management of community acquired pneumonia, an acute respiratory disease which is a leading cause of in-hospital mortality world-wide. We use the framework to develop and test a clinical decision support tool based on local guideline aligned management of the disease and show how it could be used to effectively prioritise patients using retrospective analysis. Furthermore, we show how this tool can be embedded into a prototype clinical system for disease management by integrating metrics and visualisations. This will assist decision makers to examine complex patient journeys, risk scores and predictions from embedded machine learning and artificial intelligence models. Our results show the potential of this approach for developing, testing and evaluating workflow based clinical decision support tools which include complex models and embedding them into clinical systems.

In the second cycle, 1,895 patients were admitted in July 2021.Of these 1,673 (88%) were assessed for their smoking status and 222 (12%) were not assessed.Of the 1,673 who were assessed, 275 (16%) were current smokers while 1,398 (84%) were either non-smokers or ex-smokers.All the 275 current smokers (100%) were offered referral to Stop Smoking services but only 53 (19%) patients agreed, while 222 (81%) declined referral.In the second cycle, there was a 52% increase in assessments and 27% increase in the smoking cessation referrals compared with the first cycle.

We read with interest the article by Federico and colleagues, which found that secondary bacterial infections play a critical role in adverse outcomes for patients with severe COVID-19.1Federica M. Maura F. Silvia S. Sabrina O. Cristina G.M. Eleonora C. et al.The impact of secondary infections in COVID-19 critically ill patients.J Infect. 2022; https://doi.org/10.1016/j.jinf.2022.03.017Abstract Full Text Full Text PDF Scopus (2) Google Scholar In the United Kingdom (UK), the National Early Warning Score-2 (NEWS-2) score, consisting of an a priori weighted composition of the patient's observations, is used routinely to monitor patients in hospital and identify early those who may deteriorate.2Royal College of Physicians. National early warning score (NEWS 2). Available at https://www.rcplondon.ac.uk/projects/outputs/national-early-warning-score-news-2. Accessed March 25, 2022, 2017.Google Scholar NEWS-2 was originally developed in a cohort of UK patients who had an underlying diagnosis of bacterial sepsis, but has been shown in previous studies to be sensitive in identifying those at risk of in-hospital cardiac arrest, unanticipated intensive care unit (ICU) admission or death.3Prytherch David R. Smith Gary B. Schmidt Paul E. Featherstone Peter I. ViEWS-towards a national early warning score for detecting adult inpatient deterioration.Resuscitation. 2010; 81: 932-937https://doi.org/10.1016/j.resuscitation.2010.04.014Abstract Full Text Full Text PDF PubMed Scopus (355) Google Scholar,4Smith Gary B. Prytherch David R. Meredith P. Schmidt Paul E. Featherstone Peter I. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death.Resuscitation. 2013; 84: 465-470https://doi.org/10.1016/j.resuscitation.2012.12.016Abstract Full Text Full Text PDF PubMed Scopus (502) Google Scholar In previous work published in the Journal, we showed that in hospitalised patients with COVID-19, variability in NEWS-2 scores during hospitalization, but not the admission NEWS-2 scores, was related to mortality.5Sze S. Pan D. Williams C.M.L. Wong N. Sahota A. Bell D. et al.Variability, but not admission or trends in NEWS2 score predicts clinical outcome in elderly hospitalised patients with COVID-19.J Infect. 2021; 82: 159-198https://doi.org/10.1016/j.jinf.2020.08.002Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar In contrast, the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) 4C Mortality Score (from now on referred to as the ISARIC -NEWS-2 score synergy) was developed specifically to predict risk of death from viral pneumonia in patients with COVID-19.6Knight Stephen R. Ho A. Pius R. Buchan I. Carson G. Drake Thomas M. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370 (September): 1-13https://doi.org/10.1136/bmj.m3339Crossref Scopus (400) Google Scholar Developed from a large UK cohort, the ISARIC score consists of a statistically weighted composition of age, gender, comorbidities, patient's observations, serum urea and C-reactive protein. Perhaps unsurprisingly, the ISARIC score has been shown to have a higher predictive value for mortality compared to a concomitantly calculated NEWS-2 score, since it takes into account more variables.6Knight Stephen R. Ho A. Pius R. Buchan I. Carson G. Drake Thomas M. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370 (September): 1-13https://doi.org/10.1136/bmj.m3339Crossref Scopus (400) Google Scholar An ISARIC score on admission is recommended as part of COVID-19 guidance in around half of UK hospitals.7Blunsum A.E., Perkins J.S., Arshad A., Bajpai S., Barclay-Eilliott K., Brito-Mutunatagam S., et al. Evaluation of the implementation of the 4C mortality score in United Kingdom hospitals during the second pandemic wave. 2022.Google Scholar However, there is currently no data on whether the highest NEWS-2 scores taken during hospitalization would be of incremental value in predicting mortality taking into account ISARIC scores following admission to hospital. We therefore set out to investigate this issue by using a cohort of 315 consecutive patients who presented to our Acute Respiratory Unit at the University Hospitals of Leicester NHS Trust, UK between October 2020 and January 2021, during the emergence of the Alpha (B.1.1.7) variant. Data collected as part of a clinical outcome audit are summarised in Table 1. The mean age of the patients was 63 (standard deviation [SD] 15); most were of White ethnicity. Clinical evidence of bacterial infection was low; most had evidence of pneumonia on their chest x-ray. Most participants were given supportive oxygen, antibiotics and steroids during their hospital stay. The mean ISARIC score was 10 (SD 4); mean admission NEWS-2 score was 5 (SD 2) and the mean of the maximum NEWS-2 score recorded for each patient during admission was 9 (SD 3). Our cohort suffered significant mortality and morbidity: 33% of study participants died; 4% were admitted to ICU and length of stay was close to two weeks (mean: 13 days, SD 11).Table 1Clinical description of the cohort.DemographicsPatientsAge (years) – mean (SD)63 (15)Gender (male) – number (%)203 (64%)Ethnic group – number (%)White192 (61%)Asian87 (27%)Black9 (3%)mixed/others29 (9%)Clinical characteristicsISARIC score – mean (SD)10 (4)Admission NEWS-2 score – mean (SD)5 (2)Maximum NEWS-2 score recorded during hospitalisation – mean (SD)9 (3)Blood/sputum culture done – n (%)147 (46%)Pathogen in those with culture14 (9%)Coliform5 (3%)Staphylococcus aureus7 (5%)Pseudomonas2 (1%)Stenotrophomonas maltophilia1 (1%)Findings of COVID-19 on chest x-ray – n (%)259 (82%)TreatmentO2 – n (%)296 (93%)Antibiotics – n (%)276 (87%)Dexamethasone – n (%)276 (87%)Remdesivir - n (%)9 (3%)Tocilizumab – n (%)14 (4%)OutcomesITU – n (%)12 (4%)Mortality – n (%)106 (33%)Length of stay – mean (SD)13 (11) Open table in a new tab Two logistic regression models were used to investigate the incremental value of the maximum NEWS-2 score in addition to other routinely collected clinical variables. A base model was first constructed, using variables that were related to mortality on univariable analysis. The NEWS-2 score was then added onto the base model, in order to find the best model that predicted mortality. The new models’ cumulative discrimination compared to the base model was measured using an Area under the Receiver Operating Curve (AUROC). A 2-sided p value of ≤ 0.05 was considered statistically significant. On univariable logistic regression analysis, admission ISARIC score, the maximum NEWS-2 score, treatment with steroids and antibiotics were related to mortality. Consistent with earlier findings, admission NEWS-2 scores was not related to all mortality. In our multivariable model, admission ISARIC score (adjusted odds ratio [aOR: 1.25, 95% confidence intervals [CI]: 1.14–1.36, p < 0.001) and the maximum NEWS-2 score (aOR: 1.62, 95% CI: 1.40–1.88, p < 0.001) remained independent predictors of mortality (Fig. 1A). Addition of the NEWS-2 score also improved the model's AUROC (from 0.74 to 0.85, Fig. 1B). Note that our study was observational in nature, and therefore our multivariable analysis does not reflect futility of the aforementioned treatment, but rather, strength of the prognostic value of both ISARIC and NEWS-2 scores in our cohort. In conclusion, our study has found that a combination of an admission ISARIC score, followed by NEWS-2 score monitoring most accurately predicts in-hospital mortality in hospitalized patients with COVID-19. Previous studies have only made head-to-head comparisons of different scoring systems without considering them synergistically.6Knight Stephen R. Ho A. Pius R. Buchan I. Carson G. Drake Thomas M. et al.Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score.BMJ. 2020; 370 (September): 1-13https://doi.org/10.1136/bmj.m3339Crossref Scopus (400) Google Scholar,8Gupta Rishi K. Harrison Ewen M. Ho A. Docherty Annemarie B. Knight Stephen R. van Smeden M. et al.Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study.Lancet Respir Med. 2021; 9: 349-359https://doi.org/10.1016/S2213-2600(20)30559-2Abstract Full Text Full Text PDF PubMed Scopus (79) Google Scholar,9Gupta Rishi K. Marks M. Samuels Thomas H.A. Luintel A. Rampling T. Chowdhury H. et al.Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study.Eur Respir J. 2020; 56https://doi.org/10.1183/13993003.03498-2020Crossref PubMed Scopus (80) Google Scholar Whilst the use of NEWS-2 scores alone may have limitations because they do not account for the degree of supplemental oxygen a patient with COVID-19 may require, a high NEWS-2 score during hospitalization continues to have important prognostic value in the prediction of mortality.10Lim Nicole T.Y. Pan D. Barker J. NEWS2 system requires modification to identify deteriorating patients with COVID-19.Clin Med J R Coll Physicians Lond. 2020; 20: E133-E134https://doi.org/10.7861/CLINMED.LET.20.4.6Crossref Scopus (6) Google Scholar This could be due to the possibility that NEWS-2 scores are predicting the probability of secondary bacterial sepsis, and/or cardiac arrest. Whilst we acknowledge that evidence of bacterial infection was low in our cohort, e.g. only half of our cohort had sputum/blood cultures done, possibly due to overlap in clinical syndromes between COVID-19 and bacterial infections, as well as the SARS-CoV-2 aerosol-generating concerns around sputum induction. With COVID-19 shifting from a pandemic to an endemic disease, increasing numbers of patients will have pre-existing immunity to SARS-CoV-2, either from previous exposure and/or vaccination. It is therefore reasonable to suggest that the causes of death in patients may shift from acute viral pneumonia (seen mainly in the immune naïve adults experiencing their first COVID-19 infection) to other causes, including superadded bacterial infections or decompensations of any pre-existing chronic medical conditions. In these circumstances, NEWS-2 scores will have an increasing role to play in identifying those who are at most risk of adverse outcomes. Note that as this study was performed during the emergence of the Alpha (B.1.1.7) variant, in a mostly unvaccinated UK population (UK COVID-19 vaccination started on 8 December 2020), this hybrid ISARIC-NEWS prognostic scoring approach may have different outcomes in the current Omicron (B.1.1.529) surge in a mostly vaccinated population. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acute dyspnea is one of the most common presentations in acute/emergency settings, and acute pulmonary edema remains a leading cause in adults resulting from either cardiogenic or non-cardiogenic etiologies. Neurogenic pulmonary edema (NPE) is one of the less common forms of non-cardiogenic pulmonary edema seen in emergency departments, neurology units, or intensive care units. It usually develops rapidly following significant neurological insult seen in patients with intracranial hemorrhage, traumatic brain injuries, and epileptic seizures. It is less commonly seen after a multitude of other sudden catastrophic neurologic insults. Here, we report a case study of a 32-year-old female with a history of epilepsy since childhood who was admitted to our respiratory admission unit on two separate occasions with acute NPE and type I respiratory failure after a witnessed tonic-clonic seizure episode. Although the clinical features of NPE and the results of investigations can mimic more common cardiorespiratory conditions, an accurate and timely diagnosis is vital for the appropriate emergency management and to improve the patient's outcome.