AUTHOR=Zhang Lijun , Liu Mingbo , Huang Tingting , Zhang He , Huang Chuanfu , Pan Zhenbin , Chen Zhao , Ning Jun , Tang Jiameng 

TITLE=Comparative effectiveness and pharmacological fingerprints of indobufen versus rivaroxaban in patients with chronic kidney disease: a single-center, real-world study

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1694163

DOI=10.3389/fphar.2025.1694163

ISSN=1663-9812

ABSTRACT=IntroductionAntithrombotic management in Chronic Kidney Disease (CKD) is a clinical dilemma. This study aimed to empirically evaluate the “de facto interchangeability” of the antiplatelet indobufen and the anticoagulant rivaroxaban by comparing their real-world effectiveness and safety in hospitalized CKD patients.MethodsIn this retrospective cohort study (2020-2024), we analyzed CKD patients treated with indobufen or rivaroxaban. A multi-stage analysis first used machine learning to assess baseline cohort comparability, overcoming limitations of p-value-based tests. Subsequently, a Linear Mixed Model (LMM), adjusted for confounders including polypharmacy, assessed independent drug effects on in-hospital thrombosis, hemorrhage, and longitudinal laboratory markers.ResultsMachine learning demonstrated the clinical comparability of the indobufen and rivaroxaban cohorts. The incidence of in-hospital thrombosis was numerically lower in the indobufen group (3.65% vs. 7.58%; P = 0.101), while hemorrhage rates were similar (2.19% vs. 2.27%; P = 1). The LMM analysis, beyond verifying indobfen’s expected antiplatelet activity (modulating MPV, PDW), revealed pleiotropic effects (increased prealbumin, HDL-C) and a significant reduction in urine occult blood (P < 0.001), suggesting renal safety. Notably, the model demonstrated that apparent effects on hemoglobin and eGFR were attributable to confounding by co-medications, not a direct drug effect.ConclusionIn this real-world CKD cohort, indobufen and rivaroxaban demonstrated comparable clinical effectiveness and safety. Combining machine learning with longitudinal models helps to statistically adjust for complex confounders like polypharmacy, thereby providing a more robust estimate of a drug’s independent effect.