Introduction

Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is known to be associated with various extra-intestinal manifestations, impacting organ systems beyond the gastrointestinal tract. Identifying comorbidities in IBD and the timing of their development can provide valuable insight into the mechanisms underlying IBD development.

Aims & Methods

We conducted the first population- and disease-wide phenomic association study in IBD, using >6 million ICD-10 coded healthcare contacts from 10 years before and up to 17 years after IBD diagnosis to investigate associations with 1583 diseases. In order to explore co-morbidities with potential aetiological significance with IBD, we additionally assessed the strength of association with all diseases in the pre-diagnosis compared with the post-diagnosis period. To correct for multiple testing, we adjust our significance threshold (p<0.05) with the Bonferroni correction, resulting in an adjusted p-value of 7.90×10^-6, which we refer to as disease-wide statistical significance.

Results

We identified 312 disease associations with disease-wide statisitical significance and 125 of these diseases appear up to 10 years before diagnosis.The risk of immune-mediated diseases and extra-intestinal manifestations are among those diseases increased up to 10 years prior to IBD diagnosis (psoriasis: RR_CD: 2.57, 95% CI: 2.00-3.29; RR_UC: 1.54, 95% CI: 1.25-1.87; enteropathic arthropathies: RR_CD: 3.57, 95% CI: 2.65-4.78; RR_UC: 1.8, 95% CI: 1.38-2.32). This was also the case for gastroenterological and liver disorders (gall stones: RR_CD: 1.82, 95% CI: 1.62-2.04; RR_UC: 1.26, 95% CI: 1.15-1.37; acute pancreatitis: RR_CD: 1.83, 95% CI: 1.30-2.53; RR_UC: 2.27, 95% CI: 1.84-2.79). The risk of cardiometabolic diseases and neuropsychological disorders had increased disease-wide statistical significance both pre- and post-diagnostically, whereas potential sequelae of treatment, such as osteoporosis (HR_CD: 2.56, 95% CI: 2.30-2.86; HR_UC: 1.92, 95% CI: 1.79-2.07) or herpes simplex infections (HR_CD: 4.04, 95% CI: 2.76-5.91; HR_UC: 1.69, 95% CI: 1.2-2.38) were primarily seen post-diagnostically. Of potential aetiological importance to CD, diagnosis with infectious mononucleosis (RR: 1.87, 95% CI: 1.37-2.52) was limited to the pre-diagnostic period.

Conclusion

Our results demonstrate IBD as an essentially multisystemic disease, particularly manifesting as gastrointestinal, metabolic, immune, and neuropsychological disorders, present up to 10 years prior to IBD diagnosis.

Disclosure

AE, GP, and MB report no competing interests.
BV reports research support from AbbVie, Biora Therapeutics, Landos, Pfizer, Sossei Heptares and Takeda; speaker fees from Abbvie, Biogen, Bristol Myers Squibb, Celltrion, Chiesi, Falk, Ferring, Galapagos, Janssen, MSD, Pfizer, R-Biopharm, Takeda, Truvion and Viatris; consultancy fees from Abbvie, Alimentiv, Applied Strategic, Atheneum, Biora Therapeutics, Bristol Myers Squibb, Galapagos, Guidepont, Landos, Mylan, Inotrem, Ipsos, Janssen, Pfizer, Progenity, Sandoz, Sosei Heptares, Takeda, Tillots Pharma and Viatris.
RE reports support from Novo Nordisk.
CWL has acted as a consultant to Abbvie, Janssen, Takeda, Pfizer, Galapagos, Bristol 468 Myers Squibb, B.I., Sandoz, Novartis, GSK, Gilead, ViforPharma, Dr Falk, Trellus Health 469 and Iterative Scopes; he has received speaking fees and travel support from Pfizer, 470 Janssen, Abbvie, Galapagos, MSD, Takeda, Shire, Ferring, Hospira and Dr Falk.

Introduction

Cardiac septal ocludder device is an off-label device for closure of gastrointestinal fistulas and should be considered for chronic refractory fistulas to conventional endoscopic treatments.

Aims & Methods

We describe the off-label use of a cardiac septal defect occluder (CSDO, Amplatzer™, Abbott) for endoscopic closure of a lower gastrointestinal tract fistula between the rectum and post-cystectomy surgical site.

Results

An 84-year-old male with medical history of T1 bladder cancer underwent radical cystectomy complicated with surgical site abscess. Abdominal CT revealed free gas in surgical site and a rectum wall defect suggestive of fistula. Despite conservative treatment, patient persisted with penial and anal discharge. Patient was referred for endoscopic closure. Colonoscopy showed a 6mm fistulous tract between the cystectomy surgical site and the rectum wall. Two initial closure attempts with 12/6 over-the-scope clips (OTSC) after margin epithelial ablation were tried (second attempt with combined placement of a detachable snare below the OTSC), each 3 months apart. Despite initial technical and clinical success, fistula recurred after spontaneous detachment of OTSCs. Endoscopic
internal drainage with a double pigtail stent was also tried, however, fistula persisted after distal spontaneous migration 2 months later. Considering fistula persistence, patient was proposed for closure with a CSDO. After fluoroscopic characterization of the defect (surgical site: 20x25mm, fistulous tract length: 4mm: fistula orifice diameter: 3mm), a 16/4/12mm Amplatzer™ CSDO was chosen. After placement of a 0.035’AmplatzerTM guidewire from the rectum into the surgical site, guided by a 5.4-Fr angiography catheter, a delivery sheath was then advanced into the surgical site over the guidewire. After mounting the CSDO on the loading device, it was advanced inside the delivery sheath, and correctly deployed with the proximal flange in the surgical site and distal flange in the rectum. Patient remains well, without clinical recurrence.

Conclusion

To date, only 4 cases of lower gastrointestinal fistulas closure with CSDO, all rectovaginal fistulas, have been reported. To the best of our knowledge, this is the first case of endoscopic closure of a fistula between rectum and surgical site.

Introduction

Up to 50% of patients (pts) with inflammatory bowel disease have one or more extraintestinal manifestations (EIMs), which are independent risk factors for poor quality of life and greater disability.¹ Here, we evaluated EIM resolution in patients (pts) with moderate to severe Crohn’s disease (CD) who received risankizumab (RZB), an anti-IL-23p19 monoclonal antibody, versus (vs) placebo (PBO) during the ADVANCE and MOTIVATE induction and FORTIFY maintenance studies.^2,3

Aims & Methods

In this post-hoc analysis of pooled data from ADVANCE and MOTIVATE, data collected from an EIM form, used by investigators to record the presence of 16 pre-specified EIMs (yes/no), were examined. EIM presence at induction baseline (BL), resolution (see Table footnote for definition) at wk 12 with intravenous (IV) RZB 600 mg vs PBO in ADVANCE+MOTIVATE, and maintained resolution at FORTIFY wk 52 among pts treated with subcutaneous (SC) RZB 180 mg or 360 mg vs PBO (withdrew RZB) were assessed.³ Statistical analysis methods are provided in Table footnotes. Nominal P-values for pairwise treatment comparisons are shown.

Results

Previously, EIM resolution at wk 12 in pts with EIMs at BL was evaluated as a ranked secondary endpoint in ADVANCE and MOTIVATE.² A greater proportion of pts achieved EIM resolution at wk 12 with IV RZB 600 mg vs PBO in ADVANCE (38.1% vs 20.5%, nominal P-value = 0.02), but not MOTIVATE (29.5% vs 23.7%, nominal P-value = 0.38). In this pooled analysis, at BL, 323 EIMs were reported for 240 pts in the IV RZB 600 mg arm, and 221 EIMs were reported for 161 pts in the PBO arm; approximately 45% of pts in each arm had ≥1 EIM at BL. The most frequently reported EIMs at BL were peripheral arthropathy (IV RZB 600 mg, 64% [103/161]; PBO, 70.4% [169/240]), anaemia (IV RZB 600 mg, 36% [58/161]; PBO, 28.8% [69/240]), and axial arthropathy (IV RZB 600 mg, 16.1% [26/161]; PBO, 19.2% [46/240]). Together, these EIMs accounted for most reported EIMs across the IV RZB 600 mg (87.9% [284/323]) and PBO (84.6% [187/221]) arms; the incidence of other EIMs were too low for clinically meaningful interpretation (see Table footnotes). A greater proportion of pts achieved resolution of any EIM at wk 12 vs PBO (P = 0.031) with IV RZB 600 mg (Table). A significantly greater proportion of pts with anaemia or axial arthropathy, but not peripheral arthropathy, at BL achieved resolution of the respective EIM at wk 12 with IV RZB 600 mg vs PBO (anaemia: RZB 600 mg, 26/69 [37.4%]; PBO, 13/58 [22.4%]; adjusted (adj) difference 14.4%, P = 0.049; axial arthropathy: RZB 600 mg, 23/46 [50%]; PBO, 8/26 [30.8%]; adj difference 22.6%, P = 0.042; peripheral arthropathy: RZB 600 mg, 67/169 [39.8%]; PBO, 32/103 [31.2%]; adj difference 6.8%, P = 0.236). With SC RZB 360 mg compared to PBO, a greater proportion of pts with EIMs at BL achieved resolution of EIMs at wk 52 (P = 0.005), and a greater proportion of pts who achieved resolution of any EIM at wk 12 maintained resolution at wk 52 (P=0.014) (Table).

Conclusion

References

1. Marinelli C, et al. Gastroenterology Research and Practice. 2019 Aug 21;2019:1–10.
2. D’Haens G, et al. The Lancet. 2022 May;399(10340):2015–30.
3. Ferrante M, et al. The Lancet. 2022 May;399(10340):2031–46.

Disclosure

Subrata Ghosh: SG has received consulting fees from AbbVie, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Gilead, Galapagos, Janssen, Pfizer, and Receptos; speaker fees from AbbVie, Falk Pharma, Pfizer, Ferring, Janssen, Shield, and Takeda
Marc Ferrante: Research grants from: AbbVie, Amgen, Biogen, EG, Janssen, Pfizer, Takeda and Viatris; Consultancy fees from: AbbVie, Agomab Therapeutics, Boehringer Ingelheim, Celgene, Celltrion, Eli Lilly, Janssen-Cilag, Medtronic, MSD, Pfizer, Regeneron, Samsung Bioepis, Sandoz, Takeda and ThermoFisher; Speakers’ fees from: AbbVie, Amgen, Biogen, Boehringer Ingelheim, Falk, Ferring, Janssen-Cilag, Lamepro, MSD, Pfizer, Sandoz, Takeda, Truvion Healthcare and Viatris
Britta Siegmund: Consultant for AbbVie, Arena, BI, BMS, Celgene, Falk, Galapagos, Janssen, Lilly, Pfizer, Prometheus, and Takeda; and received speaker’s fees from AbbVie, CED Service GmbH, Falk, Ferring, Janssen, Novartis, Pfizer, and Takeda
Takehiro Arai: Nothing to disclose
Walter Reinisch WR has served as a speaker for AbbVie, Celltrion, Falk Pharma GmbH, Ferring, Janssen, Galapagos Medice, MSD, Roche, Pfizer, Pharmacosmos, Shire, Takeda, Therakos, as a consultant for AbbVie, Amgen, AOP Orphan, Arena Pharmaceuticals, Astellas, Astra Zeneca, Bioclinica, Boehringer Ingelheim, Bristol Myers Squibb, Calyx, Celgene, Celltrion, Eli Lilly, Falk Pharma GmbH, Ferring, Galapagos, Gatehouse Bio Inc., Genentech, Gilead, Grünenthal, ICON, Index Pharma, Inova, Janssen, Landos Biopharma, Medahead, MedImmune, Microbiotica, Mitsubishi Tanabe Pharma Corporation, MSD, Novartis, OMass, Otsuka, Parexel, Periconsulting, Pharmacosmos, Pfizer, Protagonist, Provention, Quell Therapeutics, Sandoz, Seres Therapeutics, Setpointmedical, Sigmoid, Sublimity, Takeda, Teva Pharma, Therakos, Theravance, Zealand, as an advisory board member for AbbVie, Amgen, Astra Zeneca, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Galapagos, Janssen, Mitsubishi Tanabe Pharma Corporation, MSD, Pharmacosmos, Pfizer, Sandoz, Takeda, and has received research funding from AbbVie, Janssen, MSD, Sandoz, Sanofi, Takeda.
David Hudesman Consultant: AbbVie, BMS, Fresenius Kabi, Janssen, Pfizer, Prometheus, Takeda, UCB
Research Support: Janssen and Pfizer
Edouard Louis: Research grant: Janssen, Pfizer, Ferring, Falk, AbbVie and Takeda; educational grant: AbbVie, Janssen, Fresenius-Kabi and Takeda; speaker fees: AbbVie, Falk, Ferring, Janssen, Pfizer, Galapagos, BMS and Takeda; advisory board: Abbvie, Celgene, Ferring, Janssen, BMS, Pfizer, and Takeda, Galapagos, Gilead, Arena, Eli Lilly; consultant: Abbvie
Naomi Martin, Joanne Rizzo,Kristina Kligys, Yafei Zhang, Javier Zambrano, and Jasmina Kalabic are AbbVie employees and may own AbbVie stock and/or options.

Introduction

For training deep learning algorithms in medical imaging, application-specific data is often scarce. Deep learning systems are therefore generally pretrained on large publicly available labeled data sets of general imagery, unrelated to the envisioned application, to have the algorithm learn basic features from these widely available data followed by refinement training on the generally scarce application-specific images. Pretraining might be more effective if the images for pretraining resemble the envisioned application, i.e., domain-specific pretraining.

Aims & Methods

We investigated if pretraining on general endoscopic imagery results in a better performance of five existing AI systems with an application in gastro-intestinal endoscopy, compared to current state-of-the art pretraining approaches (i.e., supervised pretraining with ImageNet and semi-weakly supervised pretraining with the Billion-scale data set). Our group has created an endoscopy-specific dataset called GastroNet for pretraining deep learning systems in endoscopy. GastroNet consists of 5,084,494 endoscopic images retrospectively collected between 2012 and 2020 in seven Dutch hospitals. We created four pretrained models: one using GastroNet and three using ImageNet and/or the Billion-scale data set. The pretraining method was either supervised, self-supervised, or semi-weakly supervised. The pretrained models were subsequently trained towards five independent, commonly used applications in GI endoscopy, using their original application-specific datasets. Outcome parameters were: 1) classification and/or localization performance of the five trained applications; 2) change in performance when the number of available application-specific training data was reduced, to investigate a possible difference in performance drop for the different pretrained models.

Results

Overall, the domain-specific pretrained model resulted in a statistically superior performance for the five different GI applications. More detailed results are presented in Table 1. The superiority was also reflected in a smaller drop in performance when the number of application-specific training data were reduced artificially.

Table 1. Overview of performance of the five different application-specific data sets using four different pretrained models. Cells highlighted in green represent the highest scoring pretrained model per application-specific data set.

Conclusion

Domain-specific pretraining, using unlabeled general endoscopic images, is superior to current state-of-the-art pretraining approaches for developing deep learning algorithms in GI endoscopy. It also allows more effective use of the generally scarce application-specific endoscopy images. These findings might cause a paradigm shift in the development of AI systems in endoscopy.