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Record W2561506493 · doi:10.1093/ofid/ofw172.1668

Safety and Efficacy of Fecal Microbiota Transplantation for Recurrent Clostridium difficile Infection From An International Public Stool Bank: Results From a 2050-Patient Multicenter Cohort

2016· article· en· W2561506493 on OpenAlexaff
Majdi Osman, Kelsey OʼBrien, Zachery Stoltzner, Kelly Ling, Emily Koelsch, Nancy Dubois, Adila Khoiri, Kanchana Amaratunga, Mark Smith, Zain Kassam

Bibliographic record

VenueOpen Forum Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsClostridium difficileFecal bacteriotherapyMedicineFecesC difficileCohortTransplantationInternal medicineCohort studyClostridioidesIntensive care medicineMicrobiologyAntibioticsBiology

Abstract

fetched live from OpenAlex

Background. Clostridium difficile infection (CDI) is a public health threat, and fecal microbiota transplantation (FMT) appears to be an effective therapy. Recently, universal stool banks have emerged to enable safe and seamless access to FMT. However, there is a paucity of real-world safety and efficacy data from stool banks. Methods. Quality assurance data on CDI classification, FMT delivery modality, and clinical efficacy were consecutively collected from 482 healthcare facilities across 50 US states and 7 countries between 16 January 2014 and 12 April 2016. The primary outcome was physician-reported clinical cure as per standard of care follow-up. Safety data were assessed through mandatory adverse event (AE) reporting. Descriptive statistics and chi-square analyses for binomial variables were conducted. Results. Among consecutively collected reports, complete safety and efficacy data was returned for 2050 patients, which were included in these analyses. Overall, the clinical cure rate from physician-reported data across all delivery modalities and CDI patient populations was 84.0%. The most common indication for FMT, recurrent CDI, had an 87.0% (1150/1322) clinical cure rate by lower gastrointestinal (GI) delivery (figure 1). Across the entire cohort, 85.0% (n = 1742) of patients were treated with 250-mL lower GI delivery fecal microbiota preparation and 15.0% used 30-mL upper GI delivery fecal microbiota preparation. FMT by colonoscopy (85.8% clinical cure, n = 1441) was superior to that by upper endoscopy (74.1% clinical cure, n = 201) (P < 0.01). The relationship between fecal preparation type and efficacy was statistically significant (P < 0.05), with 85.1% efficacy for 250-mL preparations and 77.9% efficacy for 30-mL preparations. CDI classification had a statistically significant impact on the rate of clinical efficacy (figure 1). From a safety perspective, 42 AEs were reported; however, no AE was determined to be definitely related to FMT, 3 were possibly related to FMT, and 39 were not related based on National Institutes of Health criteria. Conclusion. To our knowledge, this is the largest FMT study reported, and the results suggest that in a large, real-world patient cohort that includes severe and refractory CDI patients, FMT from a public stool bank can be a safe and effective treatment for CDI not responsive to standard therapy. Disclosures. M. Osman, OpenBiome: Employee, Salary; K. O'Brien, OpenBiome: Employee, Salary; Z. Stoltzner, OpenBiome: Employee, Salary; K. Ling, OpenBiome: Employee, Salary; E. Koelsch, OpenBiome: Employee, Salary; N. Dubois, OpenBiome: Employee, Salary; M. Smith, OpenBiome: Employee, Salary; Finch: Board Member, Employee and Shareholder, Equity and Salary; Z. Kassam, OpenBiome: Employee, Salary; Finch Therapeutics: Consultant and Shareholder, Equity and Research support

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.308
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations26
Published2016
Admission routes1
Has abstractyes

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