S2123 Fecal Microbiota Transplantation for Recurrent Clostridioides difficile Infection: A Meta-Analysis of Randomized Controlled Trials

Introduction: Recurrent Clostridioides difficile infection (rCDI) poses a substantial burden on patients and healthcare systems due to high recurrence rates, increasing antimicrobial resistance, and limited long-term success with conventional antibiotic therapies. Fecal microbiota transplantation (FMT) has gained attention as a novel approach to restore gut microbiome balance and prevent relapse. However, conflicting results from recent randomized controlled trials (RCTs) have created uncertainty regarding its long-term prognostic value in clinical practice. Methods: A meta-analysis was conducted to address this gap, we aimed to evaluate the prognostic effectiveness of FMT in reducing recurrence rates of rCDI through a systematic review and meta-analysis of eligible RCTs published between 2017 and 2024. Literature searches were conducted using PubMed, Google Scholar, and PLOS One. Inclusion criteria required adult patients with ≥2 rCDI episodes and a clearly defined FMT intervention arm. Four RCTs met inclusion criteria. Results: Data from 579 participants were analyzed using RevMan software. The primary outcome was reduction in rCDI recurrence, calculated via pooled odds ratios (ORs) with 95% confidence intervals (CIs). Heterogeneity was assessed using the I² statistic. The pooled OR was 0.72 (95% CI: 0.53–0.99), indicating a favorable effect of FMT compared to placebo or standard therapy. However, individual trial results varied: 2 studies showed clear benefit (OR < 1), while 2 were inconclusive (OR ≈1). Heterogeneity across studies was moderate to substantial (I² = 69%), potentially due to differences in sample size, FMT delivery routes, or population characteristics. Conclusion: This study supports FMT as a promising therapy for rCDI recurrence prevention, but current evidence remains mixed. Larger, standardized trials are essential to determine its definitive clinical utility and guide integration into treatment algorithms.