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

ABSTRACT:

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

SUBMITTER: Chang E 

PROVIDER: S-EPMC12819291 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature