Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction scorepreprint
Аннотация: ABSTRACT Introduction Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of pre-hospital SARS-CoV-2 testing, is lacking. Methods Multivariate regression with bootstrapping was used to identify independent mortality predictors in a derivation cohort of COVID-19 patients. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14,231) and a smaller cohort from Aintree (N=290). Results 983 patients (median age 70, IQR 53-83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a 5-predictor score termed SOARS ( S pO2, O besity, A ge, R espiratory rate, S troke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0-1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%). Conclusion The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in pre-admission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
Год издания: 2020
Авторы: Felix Chua, Rama Vancheeswaran, Adrian Draper, Tejal Vaghela, Matthew Knight, Rahul Mogal, Jaswinder Singh, Lisa Spencer, Erica Thwaite, Harry Mitchell, Sam Calmonson, Noor Mahdi, Shershah Assadullah, Matthew Leung, Aisling O’Neill, Chayya Popat, Radhika Kumar, Thomas J. Humphries, Rebecca Talbutt, Sarika Raghunath, Philip L. Molyneaux, Miriam Schechter, Jeremy Lowe, Andrew Barlow
Издательство: Cold Spring Harbor Laboratory
Источник: medRxiv (Cold Spring Harbor Laboratory)
Ключевые слова: COVID-19 Clinical Research Studies, COVID-19 diagnosis using AI, Sepsis Diagnosis and Treatment
Открытый доступ: green