UEG's content platform for gastroenterology education bringing recordings, courses, guidelines, and more together in one place.

Re-live UEG Week 2026 on Gutflix Gut Guide online What's new on Gutflix

Trending on Gutflix

Rate for better recommendations!

Your feedback helps us show you more of what you like and less of what you dislike!

More of this!

We will show you more similar content.

Not for me!

We won't suggest this again.

Bookmark!

Save this content for later!

New to myUEG? Create an account! Log In with myUEG account

UEG Poster

Standard Poster

This content is part of Gutflix. Log in with your myUEG account, or create one free, to watch it.

Not sure what you can access? Learn more about account types.

A NOVEL MRI-BASED RISK ASSESSMENT MODEL ACCURATELY PREDICTS ANTI-TNF TREATMENT FAILURE OF PERIANAL FISTULAS IN PATIENTS WITH CROHN’S DISEASE: A MULTICENTER COHORT STUDY

Jeffrey McCurdy 1, Javeria Munir 1, Simon Parlow 1, Jacqueline Reid 2, Russell Yanofsky 3, Talal Alenezi 3, Joseph Meserve 4, Brenda Becker 5, Ranjeeta Mallick 1, Tim Ramsay 1, Greg Rosenfeld 6, Talat Bessissow 3, Ali Bessissow 3, Vipul Jairath 7, Siddharth Singh 4, David Bruining 5, Blair Macdonald 1

Affiliations

1 University of Ottawa, Ottawa, Canada

2 UBC, Vancouver, Canada

3 McGill University, Montreal, Canada

4 UCSD, San Diego, United States

5 Mayo Clinic Rochester, Rochester, United States

6 UBD, Vancouver, Canada

7 Alimentiv Inc., London, Canada

Summary

AI Generated

Summary is not available for this content yet.

Download PDF

Was this helpful?

Thanks for your feedback.

This summary was generated by an AI large language model based on the content transcript. It is for informational purposes only and should not be considered a substitute for clinical judgment. Always rely on your professional expertise and the full clinical context when making clinical decisions.

Abstract

Introduction

Clinical and radiologic factors associated with perianal fistula (PAF) healing in Crohn’s disease (CD) are poorly understood. We aimed to create a risk assessment model (RAM) to predict anti-TNF treatment failure in patients with PAF.

Aims & Methods

We performed a multicenter retrospective cohort study between 2005 and 2021. We included biologic naïve adults (>17 years) with CD who initiated anti-TNF therapy for active PAF after undergoing an MRI pelvis. Our primary outcome was treatment failure at 6 months defined clinically as ongoing PAF drainage. Pre-treatment MRI studies were prospectively re-evaluated centrally by radiologists blinded to clinical outcomes. Variables with p-values <0.1 on univariable analysis were included in our overall RAM and a simplified version consisting of MRI variables alone. Model performance was determined by calculating the area under the receiver operating characteristics curve (AUC) and internally validated using bootstrap methods. Our RAMs were compared with MAGNIFI-CD and modified Van Assche MRI scores (mVAS) by the De Long test. Aggregate scores from weighted variables from our simplified model were divided into tertiles to predict low, moderate and high risk of anti-TNF treatment failure.

Results

Our analysis included 221 patients: 133 (60%) males; mean age 29 years (SD, 21-42); 158 (71%) treated with infliximab and 70 (32%) treated with adalimumab. Treatment failure occurred in 102 (46%) patients. Our overall RAM included 3 clinical characteristics (age at fistula diagnosis, time to initiate biologic therapy and current smoking) and 8 MRI characteristics (non-inter/transsphincteric fistula anatomy, fistula length >4.3cm, primary tracts >1, secondary tracts >1, number of external openings, tract hyperintensity on T1 weighted imaging, horseshoe anatomy, and collections >1.3 cm). Our adjusted overall RAM and simplified MRI version were accurate in predicting fistula healing (AUC (95% CI), 0.75 (0.69-0.82) and 0.73 (0.66-0.79) respectively) (Table 1). The overall and simplified MRI RAMs performed significantly better than MAGNIFI-CD (p = 0.0002 and 0.0005) and the mVAS (p <0.0001 and <0.0001) respectively at predicting anti-TNF treatment failure. Aggregate scores for the simplified MRI RAM ranged between 0-30. Scores of 0-10, 11-20 and 21-30 were associated with treatment failure in 19%, 45% and 71% of patients respectively.

Table 1: Discriminatory capacity of risk assessment models for predicting anti-TNF treatment failure of perianal fistulas
Model	AUC (95% CI)	Bootstrap validation C-statistic (RMSE)
Overall model	0.75 (0.69-0.82)	0.67 (0.07)
MRI model	0.73 (0.66-0.79)	0.65 (0.07)
MAGNIFI-CD	0.61 (0.53-0.68)	NA
Modified Van Assche score	0.58 (0.51-0.66)	NA

Conclusion

In our multicenter study, we developed RAMs consisting of clinical and/or radiologic variables that can accurately predict treatment failure in patients with PAF. These findings may have important implications in clinical study design to help balance cohorts, and in clinical practice to help set realistic expectations of PAF healing for patients and providers.

Disclosure

Jeffrey McCurdy has received consultancy fees and/or speaker honoraria from Janssen, Abbvie, BMS, Fresenius Kabi, Takeda, and Pfizer.

Conference

UEG Week Copenhagen 2023

Topics

IBD

Submission format

Abstract

Session

What else is cooking in IBD? (Posters)

Citation

United European Gastroenterology Journal 2023; 11 (Supplement 8)

Published

2023

More Like This:

Improving patient compliance to replacement or removal of a plastic biliary stent inserted by ERCP

Eisa Hag

Active surveillance after chemoradiation or standard surgery?

Bas P.L. Wijnhoven

Luminal mycobiome: Potential implications in cancerogenesis

Alexander Link

A NOVEL MRI-BASED RISK ASSESSMENT MODEL ACCURATELY PREDICTS ANTI-TNF TREATMENT FAILURE OF PERIANAL FISTULAS IN PATIENTS WITH CROHN’S DISEASE: A MULTICENTER COHORT STUDY

Siddharth Singh

Advanced surgery for advanced cancer?

Katharina Beyer

RESPONSE TO TOFACITINIB IN ULCERATIVE COLITIS PREDICTED BY A MACHINE-LEARNING ALGORITHM; A MULTI-OMICS APPROACH

Leonie de Vries

Importance 1	Transcriptome (tissue) HBQ1	Transcriptome (blood) SYNCRIP	Proteome Hsp90 co-chaperone Cdc37	Microbiome *Lachnospiraceae UCG-004*
2	RBM23	RPL10	Gamma-glutamyl hydrolase	*Dorea*
3	SDC4	DOK3	Nidogen-1	Blautia
4	TAF7	MTDH	Progranulin	Anaerostipes
5	USP53	ERLEC1	Aggrecan core protein	Anaerostipes
6	VCPIP1	HMGN4	Isopentenyl-diphosphate delta-isomerase 2	Blautia
7	DUSP16	RFK	Tumor necrosis factor ligand superfamily member 6	Anaerostipes
8	MTMR10	FAM162A	Mucin-16	Agathobacter
9	DOCK1	EIF2S1	Protein amnionless	Streptococcus
10	C7orf49	RASSF7	Cathepsin D	Lachnospiraceae NK4A136 group