Physical effects of Covid-19 in patients with diabetes 1 year after hospitalisation in the UK: PHOSP-Covid study

Aim: To investigate the impact of Covid-19 on daily activity, maximal physical performance, and clinical frailty of people living with diabetes (any type) 1-year post-hospitalisation for Covid-19 in the UK. Method(s): This study is part of PHOSP-Covid, a multicentre long-term cohort study that recruited adults (>=18 years) who were discharged from one of the 83 NHS hospitals across the four UK nations following a clinical diagnosis of Covid-19 before March 31, 2021. We compared The Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue, Incremental shuttle walk test (ISWT) distance (m), and clinical frailty (Rockwood frailty level), 5-month and 1-year after discharge in patients with and without diabetes. Result(s): Out of 2545 individuals (538 (21%) with diabetes), the proportion of individuals who classified as either 'mildly frail' or 'moderately or higher frail severity' was higher in individuals with diabetes (month 5: diabetes 9.9%, no diabetes 4.7%;month 12: diabetes 8%, no diabetes 4.9%). ISWT distance in patients with diabetes were significantly lower at both follow-ups but this measure improved from 5-months to 1-year (290 [95% CI: 190-440] vs 370 [250-560] for diabetes and 340 [210-450] vs 420 [270-590] for those without, both p < 0.01). At both time points, people with diabetes reported higher levels of fatigue (36 [24-44] vs 39 [25-46] at 5-month (p = 0.03);37 [26-45] vs 40 [28-47] at 1-year visit (p < 0.01)). Conclusion(s): One year after hospitalisation long Covid is more observed in people with diabetes.

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians. METHOD: Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score. RESULTS: Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity. CONCLUSIONS: These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

SARS-CoV-2 Genomic Surveillance Reveals Little Spread between a Large University Campus and the Surrounding Community

Background. Understanding SARS-CoV-2 transmission dynamics is critical for controlling and preventing outbreaks. The genomic epidemiology of SARS-CoV-2 on college campuses has not been comprehensively studied, and the extent to which campus-associated outbreaks lead to transmission in nearby communities is unclear. We used high-density genomic surveillance to track SARS-CoV-2 transmission across the University of Michigan-Ann Arbor campus and Washtenaw County during the Fall 2020 semester. Methods. We retrieved all available residual diagnostic specimens from the Michigan Medicine Clinical Microbiology Laboratory and University Health Service that were positive for SARS-CoV-2 from August 16th - November 25th, 2020 (n = 2245). We extracted viral RNA, amplified the SARS-CoV-2 genome by multiplex RT-PCR, and sequenced these amplicons on an Illumina MiSeq. We applied maximum likelihood phylogenetic analysis to whole genome sequences to define and characterize transmission lineages. Results. We assembled complete viral genomes from 1659 individual infections, representing roughly 25% of confirmed cases in Washtenaw County across the fall semester. Of these cases, 468 were University of Michigan students. Phylogenetic analysis revealed 203 genetically distinct introductions of SARS-CoV-2 into the student population, most of which were singletons (n = 171) or small clusters of 2 - 8 students. We identified two large SARS-CoV-2 transmission lineages (115 and 73 students, respectively), including individuals from multiple on-campus residences. Viral descendants of these student outbreaks were rare, constituting less than 4% of cases in the community. Conclusion. We identified many SARS-CoV-2 transmission introductions into the University of Michigan campus in Fall 2020. While there was widespread transmission among students, there is little evidence that these outbreaks significantly contributed to the rise in COVID-19 cases that Washtenaw County experienced in November 2020.

POSC254 The Impact of COVID-19 on European HTA Decisions

Objectives: To investigate the impact of the COVID-19 pandemic on the drug technology assessment (TA) process of European HTA agencies. This study explored changes to routine practice and assessment in response to the pandemic, and assessed the impact of these changes on the number and type of assessments published during this time. Methods: Adaptations to current assessment practices were established for each HTA agency, including timelines where available. TAs published by the National Institute for Health and Care Excellence (NICE), Scottish Medicines Consortium (SMC), Haute Autorité de Santé (HAS) and Gemeinsame Bundesausschuss (G-BA) during the last 2 years were identified and compared. Results: Initial meeting suspension resulted in the absence of SMC assessments between May and July 2020, with a subsequent phased approach including scope for ‘fast-track to advice’. As part of the phased approach, medicines with the potential to deliver the greatest benefit to patients were prioritised for assessment, although the number of cancer-related assessments remained consistent. NICE published only one assessment per month in March and April 2020, with minimal notable impact on the number of subsequent assessments. Work related to addressing COVID-19 diagnostic or therapeutic interventions and medicines deemed ‘therapeutically critical’ was prioritised, and a higher proportion of assessments pertaining to cancer were published following the emergence of COVID-19. HAS initially assigned priority to drugs intended to manage COVID-19 in addition to new TAs or extension of indication assessments in oncology, paediatrics and in serious illnesses with an unmet medical need;the number of oncology assessments remained consistent following COVID-19 emergence. The reported impact on Germany was minimal;G-BA meetings were held virtually to minimise delays in assessments or pricing negotiations. Conclusions: Despite the individualised approach in response to the challenges of COVID-19, European HTA agencies demonstrated minimal long-term impact and a return to normal drug TA output.