Abstract
As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (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. From 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, red blood cells (RBC), red cell distribution width (RDW), protein levels, platelets count, albumin levels and mean corpuscular hemoglobin concentration (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, white blood cells (WBC), platelets, pCO2, RDW, large unstained cells (LUC) count, alkaline phosphatase and albumin. Our 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.
Highlights
As global coronavirus disease (COVID-19) deaths exceed 2.5 million [1], predictors of severe disease and mortality are necessary to inform clinical decisions and guide patient care
We hypothesize that incorporating dynamic changes in laboratory findings will improve the predictive accuracy of prognostic models
In this retrospective cohort study, we calculated the daily mortality risk of patients hospitalized with COVID-19 by developing a Bayesian Markov model that uses patient characteristics, including demographic variables, comorbidities, biomarkers at admission and time-dependent biomarkers
Summary
As global coronavirus disease (COVID-19) deaths exceed 2.5 million [1], predictors of severe disease and mortality are necessary to inform clinical decisions and guide patient care. Efficient COVID-19 transmission, a relatively high infection fatality ratio [2] and underprepared health systems [3] have seen many hospitals exceed capacity [4,5]. Prognostic models are needed to identify the relative importance of different prognostic factors, their impact on mortality risk and to predict the course of infection of hospitalized patients [10]. Since the beginning of the pandemic, a wide variety of prognostic models have been developed [11,12]. Such models have identified novel predictors of mortality, including a range of socioeconomic variables, demographic variables and biomarkers. Laboratory markers are indicative of certain pathologies associated with mortality: including (i) abnormal inflammatory markers (elevated C-reactive protein, ferritin, lactate dehydrogenase and procalcitonin, and lymphopenia) [16], (ii) myocardial injury biomarkers (elevated troponin) [17,18], biomarkers of acute respiratory distress syndrome (ARDS) (hypoxaemia and hypercapnia) [19], (iii) coagulopathy markers (elevations in D-dimer, thrombocytopenia, and prolonged prothrombin time) [20,21,22]
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