Microbiota-Based Therapies for Recurrent Clostridium difficile Infection: A Systematic Review of Their Efficacy and Safety
Bibliographic record
Abstract
infection (RCDI) remains a significant clinical challenge, with high recurrence rates following standard antibiotic therapy. Emerging evidence supports the role of fecal microbiota transplant (FMT) and standardized microbiome therapeutics (e.g., SER-109, RBX2660) in gut microbiota restoration and recurrence prevention. This systematic review evaluates the effectiveness and safety of these approaches in comparison to traditional therapies. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, we searched the databases PubMed/MEDLINE, ScienceDirect, Cochrane Library, Europe PubMed Central (Europe PMC), ClinicalTrials.gov, Google Scholar, and Elicit AI for studies published between January 2015 and May 2025. Eligible studies included randomized controlled trials (RCTs), observational studies, and case series assessing FMT in adults with rCDI. The risk of bias was assessed using the Cochrane Risk of Bias 2.0 tool (RoB 2) for RCTs and the Newcastle-Ottawa Scale (NOS) for cohort studies. Seven studies (six RCTs, one cohort; N=1,030 patients) were included. FMT demonstrated superior efficacy compared to antibiotics/placebo, with clinical cure rates ranging from 70% to 91% (versus 23% to 62%). Donor FMT outperformed autologous FMT (90.9% vs. 62.5%, p = 0.042) and standard therapies (71% resolution vs. 33% fidaxomicin/19% vancomycin, p < 0.01). Microbiota-based therapies (SER-109, RBX2660) demonstrated comparable efficacy (RRR up to 68%). Safety profiles were favorable, with predominantly mild gastrointestinal events and no increased risk detected for the specific outcomes measured over a five-year follow-up period. Heterogeneity existed in administration routes (colonoscopy/capsules) and donor material (fresh/frozen). FMT and standardized microbiome therapies are highly effective for treating rCDI, demonstrating robust short-term efficacy and favorable long-term safety. Donor-derived interventions and pharmaceutical-grade products (SER-109, RBX2660) represent promising alternatives to traditional antibiotics, particularly in recurrent or refractory cases. Future research should aim to standardize protocols and include more high-risk populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".