Predictors of Early Failure After Fecal Microbiota Transplantation for the Therapy of Clostridium Difficile Infection: A Multicenter Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».