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Record W2396558681 · doi:10.1038/ajg.2016.180

Predictors of Early Failure After Fecal Microbiota Transplantation for the Therapy of Clostridium Difficile Infection: A Multicenter Study

2016· article· en· W2396558681 on OpenAlexaff
Monika Fischer, Dina Kao, Shama R. Mehta, Tracey Martin, Joseph Dimitry, Ammar Hassanzadeh Keshteli, Gwendolyn K. Cook, Emmalee Phelps, Brian Sipe, Huiping Xu, Colleen Kelly

Bibliographic record

VenueThe American Journal of Gastroenterology · 2016
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of Alberta
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineClostridium difficileInternal medicineDiarrheaCohortInflammatory bowel diseaseCohort studyGastroenterologySurgeryDiseaseAntibiotics

Abstract

fetched live from OpenAlex

OBJECTIVES: Fecal microbiota transplant (FMT) is a highly efficacious treatment for recurrent or refractory Clostridium difficile infection (CDI); however, 10-20% of patients fail to achieve cure after a single FMT. The aim of this study was to identify risk factors associated with FMT failure and to develop and validate a prediction model for FMT failure. METHODS: Patient characteristics, CDI history, FMT characteristics, and outcomes data for patients treated between 2011 and 2015 at three academic tertiary referral centers were prospectively collected. Early FMT failure was defined as non-response or recurrence of diarrhea associated with positive stool C. difficile toxin or PCR within 1 month of FMT. Late FMT failure was defined as recurrence of diarrhea associated with positive stool C. difficile toxin or PCR between 1 and 3 months of the FMT. Patient data from two centers were used to determine independent predictors of FMT failure and to build a prediction model. A risk index was constructed based on coefficients of final predictors. The patient cohort from the third center was used to validate the prediction model. RESULTS: Of 328 patients in the developmental cohort, 73.5% (N=241) were females with a mean age of 61.4±19.3 years; 19.2% (N=63) had inflammatory bowel disease (IBD), and 23.5% (N=77) were immunocompromised. The indication for FMT was recurrent CDI in 87.2% (N=286) and severe or severe-complicated in 12.8% (N=42). FMT was performed as an inpatient in 16.7% (N=54). The stool source was patient-directed donors in 40% (N=130) of cases. The early FMT failure rate was 18.6%, and the late failure rate was 2.7%. In the multivariable analysis, predictors of early FMT failure included severe or severe-complicated CDI (odds ratio (OR) 5.95, 95% confidence interval (CI): 2.26-15.62), inpatient status during FMT (OR 3.78, 95% CI: 1.55-9.24), and previous CDI-related hospitalization (OR 1.43, 95% CI: 1.18-1.75); with each additional hospitalization, the odds of failure increased by 43%. Risk scores ranged from 0 to 13, with 0 indicating low risk, 1-2 indicating moderate risk, and ≥3 indicating high risk. In the developmental cohort, early FMT failure rates were 5.6% for low risk, 12.7% for moderate risk, and 41% for high-risk patients. Of 134 patients in the validation cohort, 57% (N=77) were females with a mean age of 66±18.1 years; 9.7% (N=13) had IBD, and 17.9% (N=24) were immunocompromised. The early FMT failure rate at 1 month was 19.4%, with an additional 3% failing by 3 months. In the validation cohort, FMT failure rates were 2.1% for low risk, 16.1% for moderate risk, and 35.7% for high risk patients. The area under the receiver operating characteristic curve (AUROC) for FMT failure was 0.81 in the developmental cohort and 0.84 in the validation cohort. CONCLUSIONS: Severe and severe-complicated indication, inpatient status during FMT, and the number of previous CDI-related hospitalizations are strongly associated with early failure of a single FMT for CDI. The novel prediction model has good discriminative power at identifying individuals who are at high risk of failure after FMT therapy and may assist the treating physician in subsequent management plans.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.203
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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 teacher head, 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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Citations125
Published2016
Admission routes1
Has abstractyes

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