PMC:7116472 / 2768-5133 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7116472","sourcedb":"PMC","sourceid":"7116472","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7116472","text":"Introduction\nDisease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a high mortality rate with deaths predominantly caused by respiratory failure.1 As of 1 September 2020, over 25 million people had confirmed coronavirus disease 2019 (covid-19) worldwide and at least 850 000 people had died from the disease.23 As hospitals around the world are faced with an influx of patients with covid-19, there is an urgent need for a pragmatic risk stratification tool that will allow the early identification of patients infected with SARS-CoV-2 who are at the highest risk of death to guide management and optimise resource allocation.\nPrognostic scores attempt to transform complex clinical pictures into tangible numerical values. Prognostication is more difficult when dealing with a severe pandemic illness such as covid-19 because strain on healthcare resources and rapidly evolving treatments alter the risk of death over time. Early information has suggested that the clinical course of a patient with covid-19 is different from that of pneumonia, seasonal influenza, or sepsis.4 Most patients with severe covid-19 have developed a clinical picture characterised by pneumonitis, profound hypoxia, and systemic inflammation affecting multiple organs.1\nA recent review identified many prognostic scores used for covid-19,5 which varied in their setting, predicted outcome measure, and the clinical parameters included. The large number of risk stratification tools reflects difficulties in their application, with most scores showing moderate performance at best and no benefit to clinical decision making.67 Many novel covid-19 prognostic scores have been found to have a high risk of bias, which could reflect development in small cohorts, and many have been published without clear details of model derivation and testing.5 Therefore, a risk stratification tool within a large national cohort of patients admitted to hospital with covid-19 is needed with clear development and validation details.\nOur aim was to develop and validate a pragmatic, clinically relevant risk stratification score that uses routinely available clinical information at hospital presentation to predict in-hospital mortality in patients admitted to hospital with covid-19. We then aimed to compare this score with existing prognostic 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