A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients

Momeni-Boroujeni, A.; Mendoza, R.; Stopard, I. J.; Lambert, B.; Zuretti, A.

Introduction: As COVID-19 hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve treatment. Our analysis quantifies the risk associated with dynamic clinical measurements taken throughout the course of hospitalization. Methods: We collected data for 553 PCR-positive hospitalized COVID-19 patients. We collected data on patient characteristics and laboratory values. 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. Results: 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, RBC, RDW, protein levels, platelets count, albumin levels and 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. Conclusion: 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.