Abstract
BackgroundA major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19.MethodsProspective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or transfer to ICU and/or respiratory failure (PaO2/FiO2 ratio < 200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age > 65 years, dyspnoea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) was proposed. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values.ResultsA total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. The proposed model showed an AUC of 0.73 (95% CI 0.66–0.81) for predicting disease progression by day-11.ConclusionAn easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate clinicians’ decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.
Highlights
The global Coronavirus disease 2019 (COVID-19) pandemic is challenging healthcare systems worldwide [1]
Up to January 2021, the hospital and intensive care unit (ICU) occupancy and the new admission due to COVID-19 increased in several European countries, reaching 82% of the peak ICU occupancy observed during the pandemic [5]
This study aims at identifying a panel of clinical and laboratory parameters that could support the prediction of disease progression based on symptoms onset in hospitalized patients with COVID-19
Summary
The global Coronavirus disease 2019 (COVID-19) pandemic is challenging healthcare systems worldwide [1]. Up to January 2021, the hospital and intensive care unit (ICU) occupancy and the new admission due to COVID-19 increased in several European countries, reaching 82% of the peak ICU occupancy observed during the pandemic [5]. Since the beginning of the pandemic, several prognostic predictive models combining various clinical and laboratory parameters have been developed to estimate risk of patients experiencing poor outcomes. A major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19
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