Machine learning-based approach to guide the choice between baricitinib and tocilizumab in critical COVID-19 pneumonia treatment: a retrospective cohort study

Introduction Clear guidance on choosing baricitinib (BCT) or tocilizumab (TCZ) for critical COVID-19 pneumonia remains limited. We developed machine-learning (ML) models to inform immunomodulator selection. Methods We curated clinical data from patients with critical COVID-19 pneumonia admitted between January 2020 and June 2024. Development cohort ( n = 390) was split 4:1 into training and validation sets, with Day-90 mortality as endpoint. For each therapy, patients were labeled high risk when model-predicted mortality exceeded F1-optimized thresholds. External validation used a test cohort ( n = 95). A combinatorial risk stratification assigned patients to four groups: I (low risk for both), II (low risk for TCZ, high risk for BCT), III (high risk for TCZ, low risk for BCT), and IV (high risk for both). Survival was compared for TCZ- and BCT-treated patients within each group. Results TCZ and BCT models achieved ROC-AUCs of 0.81 and 0.84, with test accuracies of 0.67 and 0.77, respectively. In test cohort, survival differed significantly between high- and low-risk strata for each agent. In Group II, mortality was significantly higher with BCT than TCZ (hazard ratio (HR) 2.32, p = 0.032); in Group III, mortality was significantly higher with TCZ than BCT (HR 3.34, p < 0.001). Model-guided selection would have changed therapy in 13.2% (65/492) of patients; as the models are prognostic rather than causal, any survival benefit from the alternative agent remains hypothetical. Conclusion ML models may support treatment selection between BCT and TCZ in patients with critical COVID-19 pneumonia. Prospective studies are warranted to assess whether model-guided choices improve survival and to validate generalizability across clinical settings.