Analysis of Virologic and Clinical Predictors for Hospitalization, Supported by Machine Learning, in a Phase 3 Molnupiravir COVID-19 Treatment Trial

Machine learning (ML) is increasingly being used in clinical medicine. The objective of this study was to identify factors predictive of hospitalization, with the support of ML, in outpatients with mild-to-moderate COVID-19. Unvaccinated adults with mild-to-moderate COVID-19 were randomized 1:1 to molnupiravir or placebo in MOVe-OUT (NCT04575597). In this post hoc analysis, various factors were evaluated among participants who progressed or did not progress to hospitalization. ML was used as an unbiased method to evaluate multiple factors for their association with hospitalization in placebo-treated participants. Participants who progressed to hospitalization (HP) were more likely versus those who remained nonhospitalized (nonHP) to be male; age ≥ 65 years old; have active cancer, diabetes mellitus, moderate COVID-19, and negative nucleocapsid and neutralizing antibody status at baseline (study intervention Day 1). In HP, baseline mean viral load (VL) was 7.51 vs. 6.83 log10 copies/mL among participants who remained nonHP. Baseline mean CRP was 49.93 in HP vs. 16.77 mg/L in nonHP; postbaseline values increased in HP vs. nonHP. Baseline mean ALC was 1.07 vs. 1.54 × 109/L, respectively; postbaseline values decreased on Days 3 and 5 in HP. ML analysis identified baseline VL, CRP, ALC, calcium, and mean change in CRP, ALC, and LDH as predictors of hospitalization. The ML model supported the initial findings that a higher VL at baseline with an increasing CRP and decreasing ALC may help identify COVID-19 patients at high risk for hospitalization. ML could be utilized to identify biomarkers predictive of hospitalization in future pandemics.