COVID-19 HYPERINFLAMMATION CAN BE PREDICTED USING ROUTINE CLINICAL LABORATORY MARKERS

ABSTRACT

Background/Aims Since early in the COVID-19 pandemic, there has been interest in the concept that some morbidity and mortality may be due to excessive inflammation. Several definitions of COVID-19 hyperinflammation COV-HI) have been proposed, including Manson criteria (C-reactive protein, CRP ≥150mg/L or doubling above 50mg/L in 24 hours and/or ferritin 1500ug/L);and Webb criteria (includes CRP ≥150mg/L or ferritin ≥750ug/L). A consistent finding has been worse outcomes. Little is known regarding the underlying pathologies separating these patients from others. Aim To investigate whether machine learning using standard laboratory features can identify a distinguishing 'COV-HI signature'. Methods A database of daily clinical and laboratory features was collected from 611 patients admitted to hospital with confirmed COVID-19 during the first wave of community-acquired infection at University College London Hospitals, Sheffield Teaching Hospitals, Newcastle upon Tyne Hospitals and Royal Wolverhampton. All data prior to mechanical ventilation were interrogated. Patients were categorised as COV-HI based on Webb thresholds (CRP >150 mg/L or ferritin ≥750ug/L). Laboratory features (peak or nadir depending on recognised predictors of illness severity) included minimum lymphocyte count 10