The relationship between plastic ingestion and trace element concentrations in Arctic seabirds.

Seabirds ingest contaminants linked to their prey's tissues, but also adsorbed to ingested plastic debris. To explore relationships between ingested plastics and trace elements concentrations, we analyzed 25 essential non-essential trace elements in liver tissue in relation to plastic content in the gastrointestinal tract in adults of four species of Arctic seabirds with different propensity to ingest plastic. Linear Discriminant Analysis (LDA) provided a clear separation between species based on element concentrations, but not among individuals with and without plastics. Molybdenum, copper, vanadium, and zinc were strong drivers of the LDA, separating northern fulmars (Fulmarus glacialis) from other species (60.4 % of explained between-group variance). Selenium, vanadium, zinc, and mercury were drivers separating black-legged kittiwakes (Rissa tridactyla) from the other species (19.3 % of explained between-group variance). This study suggests that ingestion of plastic particles has little influence on the burden of essential and non-essential trace elements in Arctic seabird species.

Artificial intelligence and mental capacity legislation: Opening Pandora's modem.

People with impaired decision-making capacity enjoy the same rights to access technology as people with full capacity. Our paper looks at realising this right in the specific contexts of artificial intelligence (AI) and mental capacity legislation. Ireland's Assisted Decision-Making (Capacity) Act, 2015 commenced in April 2023 and refers to 'assistive technology' within its 'communication' criterion for capacity. We explore the potential benefits and risks of AI in assisting communication under this legislation and seek to identify principles or lessons which might be applicable in other jurisdictions. We focus especially on Ireland's provisions for advance healthcare directives because previous research demonstrates that common barriers to advance care planning include (i) lack of knowledge and skills, (ii) fear of starting conversations about advance care planning, and (iii) lack of time. We hypothesise that these barriers might be overcome, at least in part, by using generative AI which is already freely available worldwide. Bodies such as the United Nations have produced guidance about ethical use of AI and these guide our analysis. One of the ethical risks in the current context is that AI would reach beyond communication and start to influence the content of decisions, especially among people with impaired decision-making capacity. For example, when we asked one AI model to 'Make me an advance healthcare directive', its initial response did not explicitly suggest content for the directive, but it did suggest topics that might be included, which could be seen as setting an agenda. One possibility for circumventing this and other shortcomings, such as concerns around accuracy of information, is to look to foundational models of AI. With their capabilities to be trained and fine-tuned to downstream tasks, purpose-designed AI models could be adapted to provide education about capacity legislation, facilitate patient and staff interaction, and allow interactive updates by healthcare professionals. These measures could optimise the benefits of AI and minimise risks. Similar efforts have been made to use AI more responsibly in healthcare by training large language models to answer healthcare questions more safely and accurately. We highlight the need for open discussion about optimising the potential of AI while minimising risks in this population.

New technology, psychiatry, and the law: Panic, prudence, possibility.

Throughout human history, all new technology has been met with surprise, anxiety, panic, and - eventually - prudent adoption of certain aspects of specific technological advances. This pattern is evident in the histories of most technologies, ranging from steam power in the nineteenth century, to television in the twentieth century, and - now - 'artificial intelligence' (AI) in the twenty-first century. Each generation believes that the technological advances of its era are quantitatively and qualitatively different to those of previous generations, but the underlying phenomenon is the same: the shock of the new, followed by more gradual adjustment to (and of) new technology. These concerns are apparent today in relation to AI, which reflects interesting but incremental advances on existing technologies, rather than stand-alone developments. The usual concerns with all technologies (e.g., that they will replace certain aspects of human function) are, perhaps, more concerning in fields such as mental capacity law, which often applies to people with impaired decision-making capacity who might be especially vulnerable to technologies which appear capable of encroaching disproportionately on decision-making or other areas of core human function. This paper approaches this topic from an historical standpoint, noting both previous technological panics in the past and the possibilities offered by AI today, provided it is approached in a proportionate, prudent, and person-centered way, underpinned by appropriate ethical guidance and active ethical awareness in clinical and legal practice.

Should Dignity Preservation Be a Precondition for Safety and a Design Priority for Healing in Inpatient Psychiatry Spaces?

Therapeutic security in inpatient psychiatric settings requires careful planning and implementation if it is to support patients' safety and dignity. This commentary on a case considers patients' dignity experiences when restrictions on their freedom are used to keep them safe.

Comparison of SARS-CoV-2 variants of concern in primary human nasal cultures demonstrates Delta as most cytopathic and Omicron as fastest replicating.

The SARS-CoV-2 pandemic was marked with emerging viral variants, some of which were designated as variants of concern (VOCs) due to selection and rapid circulation in the human population. Here, we elucidate functional features of each VOC linked to variations in replication rate. Patient-derived primary nasal cultures grown at air-liquid interface were used to model upper respiratory infection and compared to cell lines derived from human lung epithelia. All VOCs replicated to higher titers than the ancestral virus, suggesting a selection for replication efficiency. In primary nasal cultures, Omicron replicated to the highest titers at early time points, followed by Delta, paralleling comparative studies of population sampling. All SARS-CoV-2 viruses entered the cell primarily via a transmembrane serine protease 2 (TMPRSS2)-dependent pathway, and Omicron was more likely to use an endosomal route of entry. All VOCs activated and overcame dsRNA-induced cellular responses, including interferon (IFN) signaling, oligoadenylate ribonuclease L degradation, and protein kinase R activation. Among the VOCs, Omicron infection induced expression of the most IFN and IFN-stimulated genes. Infections in nasal cultures resulted in cellular damage, including a compromise of cell barrier integrity and loss of nasal cilia and ciliary beating function, especially during Delta infection. Overall, Omicron was optimized for replication in the upper respiratory tract and least favorable in the lower respiratory cell line, and Delta was the most cytopathic for both upper and lower respiratory cells. Our findings highlight the functional differences among VOCs at the cellular level and imply distinct mechanisms of pathogenesis in infected individuals. IMPORTANCE: Comparative analysis of infections by SARS-CoV-2 ancestral virus and variants of concern, including Alpha, Beta, Delta, and Omicron, indicated that variants were selected for efficiency in replication. In infections of patient-derived primary nasal cultures grown at air-liquid interface to model upper respiratory infection, Omicron reached the highest titers at early time points, a finding that was confirmed by parallel population sampling studies. While all infections overcame dsRNA-mediated host responses, infections with Omicron induced the strongest interferon and interferon-stimulated gene response. In both primary nasal cultures and lower respiratory cell line, infections by Delta were most damaging to the cells as indicated by syncytia formation, loss of cell barrier integrity, and nasal ciliary function.

iSPAN: Explainable prediction of outcomes post thrombectomy with Machine Learning.

PURPOSE: This study aimed to develop and evaluate a machine learning model and a novel clinical score for predicting outcomes in stroke patients undergoing endovascular thrombectomy. MATERIALS AND METHODS: This retrospective study included all patients aged over 18 years with an anterior circulation stroke treated at a thrombectomy centre from 2010 to 2020 with external validation. The primary outcome was day 90 mRS ≥3. Existing clinical scores (SPAN and PRE) and Machine Learning (ML) models were compared. A novel clinical score (iSPAN) was derived by adding an optimised weighting of the most important ML features to the SPAN. RESULTS: 812 patients were initially included (397 female, average age 73), 63 for external validation. The best performing clinical score and ML model were SPAN and XGB (sensitivity, specificity and accuracy 0.290, 0.967, 0.628 and 0.693, 0.783, 0.738 respectively). A significant difference was found overall and our XGB model was more accurate than SPAN (p < 0.0018). The most important features were Age, mTICI and total number of passes. The addition of 11 points for mTICI of ≤2B and 3 points for ≥3 passes to the SPAN achieved the best accuracy and was used to create the iSPAN. iSPAN was not significantly less accurate than our XGB model (p > 0.5). In the external validation set, iSPAN and SPAN achieved sensitivity, specificity, and accuracy of (0.735, 0.862, 0.79) and (0.471, 0.897, 0.67) respectively. CONCLUSION: iSPAN incorporates machine-derived features to achieve better predictions compared to existing clinical scores. It is not inferior to our XGB model and is externally generalisable.

SARS-CoV-2 evolution during prolonged infection in immunocompromised patients.

Prolonged infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in immunocompromised patients provides an opportunity for viral evolution, potentially leading to the generation of new pathogenic variants. To investigate the pathways of viral evolution, we carried out a study on five patients experiencing prolonged SARS-CoV-2 infection (quantitative polymerase chain reaction-positive for 79-203 days) who were immunocompromised due to treatment for lymphoma or solid organ transplantation. For each timepoint analyzed, we generated at least two independent viral genome sequences to assess the heterogeneity and control for sequencing error. Four of the five patients likely had prolonged infection; the fifth apparently experienced a reinfection. The rates of accumulation of substitutions in the viral genome per day were higher in hospitalized patients with prolonged infection than those estimated for the community background. The spike coding region accumulated a significantly greater number of unique mutations than other viral coding regions, and the mutation density was higher. Two patients were treated with monoclonal antibodies (bebtelovimab and sotrovimab); by the next sampled timepoint, each virus population showed substitutions associated with monoclonal antibody resistance as the dominant forms (spike K444N and spike E340D). All patients received remdesivir, but remdesivir-resistant substitutions were not detected. These data thus help elucidate the trends of emergence, evolution, and selection of mutational variants within long-term infected immunocompromised individuals. IMPORTANCE: SARS-CoV-2 is responsible for a global pandemic, driven in part by the emergence of new viral variants. Where do these new variants come from? One model is that long-term viral persistence in infected individuals allows for viral evolution in response to host pressures, resulting in viruses more likely to replicate efficiently in humans. In this study, we characterize replication in several hospitalized and long-term infected individuals, documenting efficient pathways of viral evolution.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

A Multinational Case Series Describing Successful Treatment of Persistent Severe Acute Respiratory Syndrome Coronavirus 2 Infection Caused by Omicron Sublineages With Prolonged Courses of Nirmatrelvir/Ritonavir.

The optimum treatment for persistent infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is not known. Our case series, across 5 hospitals in 3 countries, describes 11 cases where persistent SARS-CoV-2 infection was successfully treated with prolonged courses (median, 10 days [range, 10-18 days]) of nirmatrelvir/ritonavir (Paxlovid). Most cases (9/11) had hematological malignancy and 10 (10/11) had received CD20-depleting therapy. The median duration of infection was 103 days (interquartile range, 85-138 days). The majority (10/11) were hospitalized, and 7 (7/11) had severe/critical disease. All survived and 9 of 11 demonstrated viral clearance, almost half (4/9) of whom received nirmatrelvir/ritonavir as monotherapy. This case series suggests that prolonged nirmatrelvir/ritonavir has a role in treating persistent infection.

Comparing mental health and mental capacity law data across borders: Challenges and opportunities.

The island of Ireland is partitioned into Northern Ireland and the Republic of Ireland. In both jurisdictions, there have been important developments in mental health and mental capacity law, and associated policies and services. This includes an emphasis on developing more comprehensive approaches to collecting data on outcomes and so there is an opportunity to align these processes to enable comparison and shared learning across the border. This article explores: legal and policy developments; international approaches to mental health outcomes; and the type of data that would be helpful to collect to better understand the use of mental health and mental capacity laws. It is argued that an inclusive strategy to developing a comprehensive, integrated and aligned approach to collecting and analysing data would benefit citizens, policy makers and professionals.

A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.

BACKGROUND AND PURPOSE: MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. MATERIALS AND METHODS: This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. RESULTS: The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. CONCLUSIONS: Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

Medicine in history and history in medicine: the inaugural Davis Coakley Memorial Lecture.

Professor Davis Coakley (1946-2022) was an outstanding physician, historian, and leader of reform in medical services and education. This inaugural Davis Coakley Memorial Lecture, delivered in The Edward Worth Library at Dr Steevens' Hospital, Dublin, Ireland, on 28 September 2023, focuses on 'Medicine in History & History in Medicine'. It explores the position of the physician-historian in medical historiography, discusses Coakley's extensive historical work (especially his many books about the history of medicine), and concludes with comments about one of Coakley's great interests: the work of Sir William Wilde (1815-1876). Sir William was a prominent Victorian doctor, father of playwright Oscar, and remarkably prescient commentator on public health in Ireland. Sir William's comments about patterns of epidemics are especially arresting and relevant today in the immediate wake of COVID-19. Coakley's interest in Sir William echoed Coakley's broader commitment to medical care, progressive education, and genuine scholarship that shed light on suffering, illness, healing, and recovery. The fields of both medicine and history are greatly enriched by Coakley's life and work.

Robust airway microbiome signatures in acute respiratory failure and hospital-acquired pneumonia.

Respiratory microbial dysbiosis is associated with acute respiratory distress syndrome (ARDS) and hospital-acquired pneumonia (HAP) in critically ill patients. However, we lack reproducible respiratory microbiome signatures that can increase our understanding of these conditions and potential treatments. Here, we analyze 16S rRNA sequencing data from 2,177 respiratory samples collected from 1,029 critically ill patients (21.7% with ARDS and 26.3% with HAP) and 327 healthy controls, sourced from 17 published studies. After data harmonization and pooling of individual patient data, we identified microbiota signatures associated with ARDS, HAP and prolonged mechanical ventilation. Microbiota signatures for HAP and prolonged mechanical ventilation were characterized by depletion of a core group of microbes typical of healthy respiratory samples, and the ARDS microbiota signature was distinguished by enrichment of potentially pathogenic respiratory microbes, including Pseudomonas and Staphylococcus. Using machine learning models, we identified clinically informative, three- and four-factor signatures that predicted ARDS, HAP and prolonged mechanical ventilation with relatively high accuracy (area under the curve of 0.751, 0.72 and 0.727, respectively). We validated the signatures in an independent prospective cohort of 136 patients on mechanical ventillation and found that patients with microbiome signatures associated with ARDS, HAP or prolonged mechanical ventilation had longer times to successful extubation than patients lacking these signatures (hazard ratios of 1.56 (95% confidence interval (CI) 1.07-2.27), 1.51 (95% CI 1.02-2.23) and 1.50 (95% CI 1.03-2.18), respectively). Thus, we defined and validated robust respiratory microbiome signatures associated with ARDS and HAP that may help to identify promising targets for microbiome therapeutic modulation in critically ill patients.

Priorities and Progress in Gram-positive Bacterial Infection Research by the Antibacterial Resistance Leadership Group: A Narrative Review.

The Antibacterial Resistance Leadership Group (ARLG) has prioritized infections caused by gram-positive bacteria as one of its core areas of emphasis. The ARLG Gram-positive Committee has focused on studies responding to 3 main identified research priorities: (1) investigation of strategies or therapies for infections predominantly caused by gram-positive bacteria, (2) evaluation of the efficacy of novel agents for infections caused by methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci, and (3) optimization of dosing and duration of antimicrobial agents for gram-positive infections. Herein, we summarize ARLG accomplishments in gram-positive bacterial infection research, including studies aiming to (1) inform optimal vancomycin dosing, (2) determine the role of dalbavancin in MRSA bloodstream infection, (3) characterize enterococcal bloodstream infections, (4) demonstrate the benefits of short-course therapy for pediatric community-acquired pneumonia, (5) develop quality of life measures for use in clinical trials, and (6) advance understanding of the microbiome. Future studies will incorporate innovative methodologies with a focus on interventional clinical trials that have the potential to change clinical practice for difficult-to-treat infections, such as MRSA bloodstream infections.

Multi-level protection of rights: India's Mental Healthcare Act, 2017 and the United Nations' Convention on the Rights of Persons with Disabilities (Articles 12 and 14).

Mental health law is a feature of mental health systems around the world, so legislative reform is an important way to advance compliance with the United Nations' Convention on the Rights of Persons with Disabilities (CRPD). This paper provides an overview of India's Mental Healthcare Act, 2017 in the context of Articles 12 ('Equal recognition before the law') and 14 ('Liberty and security of person'). Notwithstanding the potential of law to produce change, exclusive or disproportionate emphasis on law draws attention from other areas in need of equal if not greater reform, such as public education, mental health services, physical healthcare, social care networks, criminal justice systems, and gathering political backing for the profound reforms needed to transform the socio-economic landscapes in which mental illness develops, diagnoses are made, treatment is provided (or not provided), recovery occurs (or does not occur), and social citizenship is protected and promoted (or eroded and obstructed). There is a particular issue with misunderstandings of psychiatry in academic discussions of mental health legislation which appears to stem from lack of engagement with the full diversity of service-user views and the evidence base for psychiatric care. Greater recognition of upstream factors that shape both suffering and services would acknowledge that the decisions of politicians and service managers have a much greater impact on rights, especially the right to treatment and support, than the decisions of individual clinicians whose options are often very limited at the level of individual care. India's legislation seeks to address not only individual-level care, but also this higher level of reform through its articulation of an explicit 'right to access mental healthcare and treatment'. Similar multi-level legislative reform elsewhere would increase cooperation from all stakeholders, improve compliance with the CRPD, and help move towards zero coercion in mental health services.

Comparison of SARS-CoV-2 variants of concern in primary human nasal cultures demonstrates Delta as most cytopathic and Omicron as fastest replicating.

The SARS-CoV-2 pandemic was marked with emerging viral variants, some of which were designated as variants of concern (VOCs) due to selection and rapid circulation in the human population. Here we elucidate functional features of each VOC linked to variations in replication rate. Patient-derived primary nasal cultures grown at air-liquid-interface (ALI) were used to model upper-respiratory infection and human lung epithelial cell lines used to model lower-respiratory infection. All VOCs replicated to higher titers than the ancestral virus, suggesting a selection for replication efficiency. In primary nasal cultures, Omicron replicated to the highest titers at early time points, followed by Delta, paralleling comparative studies of population sampling. All SARS-CoV-2 viruses entered the cell primarily via a transmembrane serine protease 2 (TMPRSS2)-dependent pathway, and Omicron was more likely to use an endosomal route of entry. All VOCs activated and overcame dsRNA-induced cellular responses including interferon (IFN) signaling, oligoadenylate ribonuclease L degradation and protein kinase R activation. Among the VOCs, Omicron infection induced expression of the most IFN and IFN stimulated genes. Infections in nasal cultures resulted in cellular damage, including a compromise of cell-barrier integrity and loss of nasal cilia and ciliary beating function, especially during Delta infection. Overall, Omicron was optimized for replication in the upper-respiratory system and least-favorable in the lower-respiratory cell line; and Delta was the most cytopathic for both upper and lower respiratory cells. Our findings highlight the functional differences among VOCs at the cellular level and imply distinct mechanisms of pathogenesis in infected individuals.

Quantifying individual-level heterogeneity in infectiousness and susceptibility through household studies.

The spread of SARS-CoV-2, like that of many other pathogens, is governed by heterogeneity. "Superspreading," or "over-dispersion," is an important factor in transmission, yet it is hard to quantify. Estimates from contact tracing data are prone to potential biases due to the increased likelihood of detecting large clusters of cases, and may reflect variation in contact behavior more than biological heterogeneity. In contrast, the average number of secondary infections per contact is routinely estimated from household surveys, and these studies can minimize biases by testing all members of a household. However, the models used to analyze household transmission data typically assume that infectiousness and susceptibility are the same for all individuals or vary only with predetermined traits such as age. Here we develop and apply a combined forward simulation and inference method to quantify the degree of inter-individual variation in both infectiousness and susceptibility from observations of the distribution of infections in household surveys. First, analyzing simulated data, we show our method can reliably ascertain the presence, type, and amount of these heterogeneities given data from a sufficiently large sample of households. We then analyze a collection of household studies of COVID-19 from diverse settings around the world, and find strong evidence for large heterogeneity in both the infectiousness and susceptibility of individuals. Our results also provide a framework to improve the design of studies to evaluate household interventions in the presence of realistic heterogeneity between individuals.

Post-COVID care delivery: The experience from an Irish tertiary centre's post-COVID clinic.

BACKGROUND: The long-term effects of SARS-CoV-2 infection and optimal follow-up approach are not well-recognised. Here we describe the implementation of a post-COVID clinic in an Irish tertiary centre after the first wave of the pandemic. This study describes the characteristics of our patient cohort and the operations and outcomes of the clinic, exploring some of the risk factors for developing post-COVID syndrome and the appropriateness of the triage system employed. METHODS: All SARS-CoV-2 positive patients from March 10th to June 14th 2020 were telephone-triaged as red, amber or green based on ongoing symptoms with clinic appointments scheduled accordingly. All clinic visits were face-to-face with the infectious diseases medical team and a proforma for each patient was completed. Data were collected retrospectively by reviewing the proformas and the electronic medical record (EMR). RESULTS: 311 patients attended the clinic. Median time from illness to clinic appointment was 95 days (IQR 77-105.5). 204 patients (66%) were female, 192 (62%) were hospital staff, and the median age was 43 years (IQR 31-53). 138 patients (44%) had required hospital admission. At their first clinic visit 219 patients (70%) had ongoing symptoms. A further appointment was made for 62 patients (20%). 34 patients (11%) were discussed at an MDT meeting, and 55 (18%) were referred onward to a specialist service. 85% of those triaged green, 73% of those triaged amber, and 39% of those triaged red did not receive further follow up after one clinic visit. Patients were more likely to require follow up with reported dyspnoea (OR 5.6; 95% CI 2.8-11.3; p <0.001), cough (OR 3.0; 95% CI 1.1-8.4, p = 0.04), and palpitations (OR 3.6; 95% CI 1.0-12.3; p = 0.04). Female sex was associated with increased odds of a higher triage category (OR 1.8; 95% CI 1.08 to 3.20; p = 0.02), as was requiring admission to hospital (OR 4.0; 95% CI 2.34 to 6.90; p < 0.001). CONCLUSION: The long-term effects of COVID-19 are significant with 70% of our cohort experiencing persistent symptoms. Persistent dyspnoea, cough and palpitations were associated with increased need for follow up. This study also suggests that a traffic light telephone-triage service followed by a face-to-face medical-led clinic could be an effective way of identifying patients who require further management.