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IntroductionIn 2020, the UK Health Security Agency (UKHSA) established a large-scale testing programme to rapidly identify individuals in England who were infected with SARS-CoV-2 and had COVID-19. This comprised part of the UK governments COVID-19 response strategy, to protect those at risk of severe COVID-19 disease and death and to reduce the burden on the health system. To assess the success of this approach, UKHSA commissioned an independent evaluation of the activities delivered by the NHS testing programme in England. The primary purpose of this evaluation is to capture key learnings from the rollout of testing to different target populations via various testing services between October 2020 and March 2022 and to use these insights to formulate recommendations for future pandemic preparedness strategy. Methods and analysisThe proposed study involves a stepwise mixed-methods approach, aligned with established methods for the evaluation of complex interventions in health, with retrospective and prospective components. A bottom-up approach will be taken, focusing on each of nine population-specific service settings. We will use a Theory of Change to understand the causal pathways and intended and unintended outcomes of each service, also exploring the effect of context on each individual service settings intended outcomes. Subsequently, the insights gained will be synthesised to identify process and outcome indicators to evaluate how the combined aims of the testing programme were achieved. A forward-looking, prospective component of this work will aim to inform testing strategy in preparation for future pandemics, through a participatory modelling simulation and policy analysis exercise. DisclaimerThis is a provisional draft protocol that represents research in progress. This research was commissioned and funded by UKHSA, to be performed between August 2022 and March 2023. The scope and depth of testing services and channels covered by this research were pre-agreed with UKHSA and are limited to the availability and provision of data available at the time this protocol was written. Ethics and disseminationFindings arising from this evaluation will be used to inform lessons learnt and recommendations for UKHSA on appropriate pandemic preparedness testing programme designs; findings will also be disseminated in peer-reviewed journals and at academic conferences. Strengths and limitations of the studyO_LIStrengths of this mixed-methods evaluation protocol include the use of theory-based, complex evaluation approaches and an iterative and participatory approach with the stakeholder (UKHSA) to the evaluation process and prospective modelling. C_LIO_LIGiven the scale and complexity of the COVID-19 testing response in England, there is a scarcity of previous relevant research into this, either in England or appropriate international comparators, warranting the mixed-methods evaluation approach we are adopting. C_LIO_LIThis is the first national-scale evaluation of the testing response to COVID-19 in England to incorporate most service settings, a programme which formed an integral part of the UK pandemic response strategy. The approach proposed could be applied to the evaluation of pandemic responses in other contexts or to other types of interventions. C_LIO_LIWhereas most complex interventions are accompanied by a prospective evaluation design initiated at the time of the intervention or earlier, this study predominantly comprises a retrospective evaluation and is therefore limited by the quality of existing research and the data available to the research team at the time of conducting the evaluation within the specified period allocated by UKHSA. C_LI
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A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patients hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.
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IntroductionAs COVID-19 hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve treatment. Our analysis, to our knowledge, is one of the first to quantify the risk associated with dynamic clinical measurements taken throughout the course of hospitalization. MethodsWe collected data for 553 PCR-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients laboratory values. ResultsFrom the set of factors available upon admission, the Markov model determined that age >80 years, history of coronary artery disease and chronic obstructive pulmonary disease increased mortality risk. The lab values upon admission most associated with mortality included neutrophil percentage, RBC, RDW, protein levels, platelets count, albumin levels and MCHC. Incorporating dynamic changes in lab values throughout hospitalization lead to dramatic gains in the predictive accuracy of the model and indicated a catalogue of variables for determining high-risk patients including eosinophil percentage, WBC, platelets, pCO2, RDW, LUC count, alkaline phosphatase and albumin. ConclusionOur prognostic model highlights the nuance of determining risk for COVID-19 patients and indicates that, rather than a single variable, a range of factors (at different points in hospitalization) are needed for effective risk stratification.
