Successful Bacterial Engraftment Identified by Next-Generation Sequencing Predicts Success of Fecal Microbiota Transplant for <i>Clostridioides difficile</i>
Bibliographic record
Abstract
BACKGROUND: infection (CDI), is dependent on successful engraftment (incorporation) of donor stool. We present a method for evaluating engraftment success based on next-generation sequencing (NGS)-based profiling of bacterial strains present in donor and recipient stool, and we suggest its potential to guide treatment decisions. METHODS: Bacterial strains in stool samples from three patients from the clinic and one donor were analyzed via NGS and metagenomic sequencing, before and 1 month after FMT for CDI. The similarity of strains present was assessed via relative abundance, principal component analysis, Shannon and Simpson diversity indexes, and Bray-Curtis dissimilarity matrix. A positive outcome was successful engraftment, where the post-FMT sample closely resembled that of the donor and CDI was cured. RESULTS: Patients (Pts.) 1 and 2, but not Pt. 3's stool samples closely resembled the donor specimen post-FMT. Noteworthy, Pt. 3 pre-FMT sample was less similar to the donor than that of Pts. 1 and 2. All methods of assessing similarity and dissimilarity used yielded virtually identical conclusions. Pts. 1 and 2 which closely resembled donor specimen, eradicated CDI giving a surrogate objective measure of engraftment. CONCLUSIONS: Success of engraftment in FMT can be assessed using NGS and metagenomic analysis and parallels success in curing CDI of the microbiome. The statistical methods we present here are reliable and consistent for such purposes. The dissimilarity of Pt. 3 to the donor combined with the failure of engraftment and failure to cure CDI in Pt. 3 suggests that FMT success may be predictable by comparing pre-FMT samples to donor. There is no clinical trial registry listing this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".