Search

BackgroundParkinson’s disease has been identified as a risk factor for severe Coronavirus disease 2019 (COVID-19) outcomes. However, whether the significant high risk of death from COVID-19 in people with Parkinson’s disease is specific to the disease itself or driven by other concomitant and known risk factors such as comorbidities, age, and frailty remains unclear.ObjectiveTo investigate clinical profiles and outcomes of people with Parkinson’s disease and atypical parkinsonian syndromes who tested positive for COVID-19 in the hospital setting in a multicentre UK-based study.MethodsA retrospective cohort study of Parkinson’s disease patients with a positive COVID-19 test admitted to hospital between February 2020 and July 2021. An online survey was used to collect data from clinical care records, recording patient, Parkinson’s disease and COVID-19 characteristics. Associations with time-to-mortality and severe outcomes were analysed using either the Cox proportional hazards model or logistic regression models, as appropriate.ResultsData from 552 admissions were collected: 365 (66%) male; median (inter-quartile range) age 80 (74-85) years. The 34-day mortality rate was 38.4%; male sex, increased age and frailty, Parkinson’s dementia syndrome, requirement for respiratory support and no vaccination were associated with increased mortality risk. Community-acquired COVID-19 and co-morbid chronic neurological disorder were associated with increased odds of requiring respiratory support. Hospital-acquired COVID-19 and delirium were associated with requiring an increase in care level post-discharge.ConclusionsThis first, multicentre, UK-based study on people with Parkinson’s disease or atypical parkinsonian syndromes, hospitalised with COVID-19, adds and expands previous findings on clinical profiles and outcomes in this population.

ObjectiveTo evaluate features of Parkinson’s disease (PD) and atypical Parkinson’s syndromes (APS) associated with poor outcome and mortality in people with COVID-19 in a hospital setting.BackgroundPrevious studies have demonstrated increased mortality of COVID-19 in people with PD. However, it is not known whether this is associated with disease-related factors (eg autonomic dysfunc- tion, dysphagia).MethodsAn online survey tool captured anonymised patient data from hospital admission records of people with PD and APS who tested positive for COVID-19 between February 2020 and July 2021. We will use Cox proportional hazards and linear regression models to evaluate which characteristics are associ- ated with mortality, increased care requirement and more severe COVID-19 infection. Models will be adjusted for known associations with poor outcome, such as co-morbidities, age and sex.ResultsData were collected from 556 admissions from 21 UK sites: 66.2% male; median (IQR) age 80 (11) years; median disease duration 5 (7) years. 19.2% were asymptomatic, 28.8% had mild symptoms and 52.5% required respiratory support. 38.3% died within 4 weeks of a positive COVID-19 test. Preliminary Kaplan-Meier curves suggest that co-existing dementia, marked motor fluctuations and more advanced Hoehn and Yahr stage may be associated with 28-day mortality. Full statistical analysis is in progress.ConclusionsIdentification of Parkinson’s features associated with poor in-hospital COVID-19 outcome will allow a more informed discussion relating to individual COVID-19 risk.

Time-to-event, bivariate, semi-competing risk data occur when a terminal event can censor a non-terminal event, but not vice versa. There are potential correlations between these endpoints as they are measured on the same individual. However, traditional methods to estimate the correlations cannot be used directly due to the censoring of time-to-event endpoints. We develop methods using a copula-based approach to study the dependence structures between the two survival endpoints. We use a variety of copulas to estimate the correlation between endpoints and to acknowledge different dependence structures. The estimated association parameter in the copula function is transformed into Spearman's rank correlation coefficient. We conduct a simulation study to evaluate the estimation from the proposed models along with the effects of misspecification of the copula functions and survival distributions. The proposed methods are applied to two real-life data sets.

ObjectivesTo evaluate the influence of external peer reviewer scores on the National Institute for Health Research (NIHR) research funding board decisions by the number of reviewers and type of reviewer expertise.DesignRetrospective analysis of external peer review scores for shortlisted full applications for funding (280 funding applications, 1236 individual reviewers, 1561 review scores).SettingFour applied health research funding programmes of NIHR, UK.Main outcome measuresBoard decision to fund or not fund research applications.ResultsThe mean score of reviewers predicted funding decisions better than individual reviewer scores (area under the receiver operating characteristic (ROC) curve 0.75, 95% CI 0.69 to 0.81 compared with 0.62, CI 0.59 to 0.65). There was no substantial improvement in how accurately mean reviewer scores predicted funding decisions when the number of reviewers increased above 4 (area under ROC curve 0.75, CI 0.59 to 0.91 for four reviewers; 0.80, CI 0.67 to 0.92 for seven or more). Reviewers with differing expertise influenced the board’s decision equally, including public and patient reviewers (area under ROC curves from 0.57, CI 0.47 to 0.66 for health economists to 0.64, CI 0.57 to 0.70 for subject-matter experts). The areas under the ROC curves were quite low when using reviewers’ scores, confirming that boards do not rely solely on those scores alone to make their funding decisions, which are best predicted by the mean board score.ConclusionsBoards value scores that originate from a diverse pool of reviewers. On the basis of independent reviewer score alone, there is no detectable benefit of using more than four reviewer scores in terms of their influence on board decisions, so to improve efficiency, it may be possible to avoid using larger numbers of reviewers. The funding decision is best predicted by the board score.