Introduction

Celiac disease (CD) has been associated with reduced quality of life and psychological disorders, among which the most common are anxiety and depression [1, 2]. Several reports have focused on the difficulties of living with CD, particularly regarding its impact on physical, social and emotional factors in adults.

Aims & Methods

The cross-sectional study evaluated the changes in the anxiety and depression of 48 patients with CD. The control group was consisted of 20 people without gastrointestinal diseases. Several validated questionnaires were administered to measure psychological symptoms (the Medical Outcomes Study Questionnaire Short Form 36 Health Survey (MOS SF-36), Hospital Anxiety and Depression Scale (HADS) and State-Trait Anxiety Inventory (STAI)) and adherence to a gluten-free diet (GFD). Based on the results GFD the patients divided into two groups. The first group patients have not been adhered to a GFD and the second group patients have been strictly following a GFD.

Results

To decrease of the level physical and mental components of the quality of life was determined in patients with CD in comparison with the control group (36.1 and 40.0, respectively; p < 0.01). The most of the patients with CD (72.8%) had anxiety reactions. According to HADS anxiety has reached clinically significant level in 43.7% patients with CD. STAI score state anxiety and trait anxiety have been elevated in patients with CD (p <0.01). Its haven`t been significant differences in the frequency and severity of anxiety in patients with CD without and with GFD. Symptoms of depression have been more frequently in patients with CD with GFD, however it’s have not been reached a clinically significant level.

Conclusion

The features of emotional disturbances in patients with CD include the prevalence of the frequency of anxiety over the depression. The results showed no correlation between anxiety and adherence to GFD. The severity of depression increased in patients with GFD. This finding is consistent with Barratt et al.'s finding that difficulty with GPA compliance is a major factor in reduced quality of life [3].

References

1. Zingone F, Swift GL, Card TR, Sanders DS, Ludvigsson JF, Bai JC. Psychological morbidity of celiac disease: A review of the literature. United European Gastroenterol J. 2015 Apr;3(2):136-45. doi: 10.1177/2050640614560786
2. Canova C, Rosato I, Marsilio I, Valiante F, Zorzetto V, Cataudella G, D'Odorico A, Zingone F. Quality of Life and Psychological Disorders in Coeliac Disease: A Prospective Multicentre Study. Nutrients. 2021 Sep 16;13(9):3233. doi: 10.3390/nu13093233
3. Barratt S.M., Leeds J.S., Sanders D.S. Quality of Life in Coeliac Disease is Determined by Perceived Degree of Difficulty Adhering to a Gluten-Free Diet, not the Level of Dietary Adherence Ultimately Achieved. J. Gastrointestin. Liver Dis. 2011;20:241–245

Introduction

Initial reports suggested a high mortality associated with peri-operative COVID-19 infection resulting in delayed surgery and a global change in surgical pathways. The impact of COVID-19 variants and vaccinations on surgical outcomes is yet to be described.

Aims & Methods

We aimed to define the risk of COVID-19 infection in the 30 days following surgery and the associated mortality. With the approval of NHS England we identified all registered adult patients in an OpenSAFELY-TPP primary care general practice within England whilst having an abdominal, obstetric, orthopaedic, thoracic or vascular procedure defined using OPCS codes between 01/02/2020 and 01/03/2022.COVID vaccinations and COVID waves were defined. Crude 30 day risks were calculated using Kaplan Meier curves. A competing risks Cox proportional survival analysis was used to adjust the cumulative risk of COVID-19 infection by the competing risk of death and subsequent non COVID-19 emergency readmissions.
Primary care records managed by the GP software provider TPP were linked to ONS death data, Hospital Episodes Statistics, and Second Generation Surveillance System Public Health England test data through OpenSAFELY. All data were linked, stored and analysed securely within the OpenSAFELY platform: https://opensafely.org/. Data include pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data are included. All code is shared openly for review and re-use under MIT open license https://github.com/opensafely/PostOpCovid. Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared.
This study was approved by the Health Research Authority [REC reference 21/EE/0278] by the East of England Research Ethics Committee.

Results

We identified 1,213,890 patients within the study period who underwent at least one surgical procedure. Following surgery 11,950 (0.9%) patients developed COVID-19 infection within 30 days. Post-operative COVID infections were greatest in wave 4 with 2.5% of patients having a post-operative infection. Adjusting for all factors post-operative COVID infection risk was 4.9 (aHR 4.88 95% CI 4.65-5.12) fold greater in the fourth wave compared to the first wave.
Within 90 days of surgery there were 820 (0.06%) deaths. A recent COVID infection increased crude morality to 1.42%, and a post-operative COVID infection increased crude mortality to 1.82%. The risk of death reduced following a first and second vaccination (aHR 0.11 95% CI 0.09-0.13). A recent COVID infection (7-42 days prior to admission) increased mortality 4 fold (aHR 3.84 95% CI 3.31-4.45) and a post-operative COVID infection increased mortality 8 fold (aHR 8.49 95% CI 7.6-9.48) compared to those without a COVID-19 infection. This increased 90 day mortality risk was highest in the second wave following a post-operative COVID infection, but then decreased to the non-covid baseline for both emergency and elective procedures in the 3^rd and 4^th waves.

Conclusion

The risk of post-operative COVID infection is low but has increased as strains have been become more transmissible. The absolute risk of death following peri-operative COVID-19 infection is greater than those who have not had infection however this risk has become attenuated during the latter waves of the pandemic.

Acknowledgements: We are very grateful for all the support received from the TPP Technical Operations team throughout this work, and for generous assistance from the information governance and database teams at NHS England and the NHS England Transformation Directorate.

Disclosure

None to declare

Introduction

Ulcerative colitis (UC) treatment requires a patient-centric efficacy endpoint to assess treatment benefits, enabling a shift towards a personalized approach. Comprehensive disease control (CDC) is an individual multi-component endpoint based on findings from a patient and expert Delphi consensus on remission assessment in UC. CDC was defined as IBDQ remission, partial Mayo Clinic Score (pMCS) remission, biomarker (faecal calprotectin [FCP]) remission and endoscopic improvement.

Aims & Methods

We evaluated different definitions of CDC, and the contribution of different CDC components to the net treatment benefit (NTB) of filgotinib 200 mg (FIL200), an oral, once-daily, Janus kinase 1 inhibitor approved for UC treatment following the SELECTION trial (NCT02914522). CDC was assessed at week 58 (W58) of SELECTION in adult patients with active UC treated with FIL200 or placebo (PBO). The CDC definitions evaluated were: 4-component CDC (CDC4) with FCP <150 µg/g, CDC4-150; CDC4 with FCP <250 µg/g, CDC4-250; 5-component CDC (with histological remission [CDC5]) with FCP <150 µg/g, CDC5-150; and CDC5 with FCP<250 µg/g, CDC5-250. The proportion of patients achieving each combination of components was evaluated. NTB of FIL200 was assessed for each endpoint using generalized pairwise comparisons and is reported here for CDC4-150 (and excluding different components).

Results

At W58, 21.6%, 26.6%, 19.1% and 22.6% of FIL200-treated patients achieved CDC4-150, CDC4-250, CDC5-150 and CDC5-250, respectively. Among patients who did not achieve CDC4-150, 0.5% of patients did not achieve this composite endpoint because they did not achieve pMCS remission; whereas 5.0%, 10.1% and 6.0% did not do so because they did not achieve IBDQ remission, biomarker remission or endoscopic improvement, respectively. Positive overall NTB of FIL200 versus PBO was demonstrated with all CDC definitions, including those with histological remission (CDC5). For CDC4-150 at W58, excluding pMCS remission did not substantially change the NTB (Δ −0.33; Table) versus excluding IBDQ or biomarker remission, or endoscopic improvement (Δ 3.87, 6.93 and 3.20, respectively). Similar NTB results were seen for all CDC definitions, with or without histological remission.

Conclusion

CDC is a stringent, patient-level, multi-component endpoint achieved by ~1/5 of FIL200-treated patients at W58. While CDC4-150 could be the optimal operational definition, the addition of histological remission resulted in similar stringency, potentially allowing for the use of different CDC definitions based on context or resources. Additional NTB of FIL200 was identified in patients when IBDQ or biomarker remission or endoscopic improvement were excluded. Overall, CDC appears to be a suitable endpoint to assess individual comprehensive benefit in patients with UC. Prospective validation is needed.

Table. NTB with exclusion of different components of the CDC4-150 endpoint.

Disclosure

We thank Katrien Van Beneden for her contribution to conceptualization, investigation and data interpretation.
The SELECTION trial was sponsored by Gilead Sciences, Inc. (Foster City, CA, USA). Galapagos NV (Mechelen, Belgium) was a collaborator for the SELECTION trial and funded this analysis.
Medical writing support for the development of this abstract was provided by Hannah Stephens, PhD, of PharmaGenesis London, London, UK, and was funded by Alfasigma S.p.A.
VV and AO are employees of Alfasigma S.p.A at the time of abstract submission
SS has received personal fees from AbbVie, Amgen, Arena Pharmaceuticals, Biogen, Bristol Myers Squibb, Celgene, Celltrion, Dr Falk Pharma, Eli Lilly, Ferring Pharmaceuticals, Fresenius Kabi, Galapagos/Gilead Sciences, I-Mab, Janssen Pharmaceuticals, MSD, Mylan, Pfizer, Protagonist, Provention Bio, Sandoz/Hexal, Takeda, Theravance Biopharma and UCB.
BGF has received grants and personal fees from AbbVie, Amgen, AstraZeneca, Bristol Myers Squibb, Janssen Biotech/Centocor, Johnson & Johnson/Janssen, Pfizer, Receptos and Takeda; and personal fees from Ablynx, ActoGeniX, Akebia Therapeutics, Inc., Allergan, Atlantic Pharmaceuticals, Avaxia Biologics, Inc., Avir Pharma, Baxter Healthcare Corporation, Biogen Idec, BiomX Israel, Boehringer Ingelheim, Boston Pharmaceuticals, Calypso Biotech, Celgene, Elan/Biogen, Eli Lilly, enGene, Ferring Pharmaceuticals, Galapagos, Genentech/Roche, gIcare Pharma, Gilead Sciences, Given Imaging, Gossamer Bio, GSK, Inception IBD, Inc., Ironwood Pharmaceuticals, Japan Tobacco Company, Kyowa Hakko Kirin Co. Ltd, Lexicon Pharmaceuticals, Lycera, Mesoblast, Millennium Pharmaceuticals, MSD, Nestlé, Nextbiotix, Novartis, Novo Nordisk, Par’Immune, Progenity, Prometheus, Protagonist, Qu Biologics, Q32 Bio (formerly AdMIRx, Inc.), Salix Pharmaceuticals, Shire, Sienna Biopharmaceuticals, Sigmoid Pharma, Synergy Pharma, Teva Pharmaceuticals, TiGenix, Tillotts Pharma, UCB, Vertex Pharmaceuticals, VHsquared, Vivelix Pharmaceuticals, Wyeth, Zealand Pharma and Zyngenia.
SV receives financial support for research from AbbVie, Galapagos, Johnson & Johnson, Pfizer and Takeda; speaker and/or consultancy fees from AbbVie, Abivax, AbolerIS Pharma, AgomAb Therapeutics, Alimentiv, Arena Pharmaceuticals, AstraZeneca, Bior Therapeutics (formerly Progenity), Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Cytoki Pharma, Dr Falk Pharma, Eli Lilly, Ferring Pharmaceuticals, Galapagos, Genentech-Roche, Gilead Sciences, GSK, Hospira, IMIDomics, Janssen Pharmaceuticals, Johnson & Johnson, Materia Prima, Mestag Therapeutics, MiroBio, Morphic, MRM Health, MSD, Mundipharma, Pfizer, ProDigest, Prometheus, Robarts Clinical Trials, Surrozen, Takeda, Theravance Biopharma, Tillotts Pharma AG, VectivBio, Ventyx Biosciences and Zealand Pharma.
SD reports personal fees from AbbVie, Allergan, Amgen, AstraZeneca, Athos Therapeutics, Biogen, Boehringer Ingelheim, Celgene, Celltrion, Eli Lilly, Enthera, Ferring Pharmaceuticals, Gilead Sciences, Hospira, Inotrem, Janssen Pharmaceuticals, Johnson & Johnson, MSD, Mundipharma, Mylan, Pfizer, Roche, Sandoz, Sublimity Therapeutics, Takeda, TiGenix, UCB and Vifor Pharma.
MF is an employee and shareholder of Galapagos NV.
VV and AO are former employees and shareholders of Galapagos NV, and employees of Alfasigma. S.p.A.
LP-B has received consulting fees from Abivax, AbbVie, Alimentiv, Alma Bio Therapeutics, Amgen, Applied Molecular Transport, Arena Pharmaceuticals, Biogen, Bristol Myers Squibb, Celltrion, CONNECT Biopharma, Cytoki Pharma, Eli Lilly, Enthera, Ferring Pharmaceuticals, Fresenius Kabi, Galapagos, Genentech, Gilead Sciences, Gossamer Bio, GSK, HAC Pharma, IAG Image Analysis, Index Pharmaceuticals, Inotrem, Janssen Pharmaceuticals, Medac, Mopac, Morphic, MSD, Norgine, Novartis, OM Pharma, ONO Pharma, OSE Immunotherapeutics, Pandion Therapeutics, Par’Immune, Pfizer, Prometheus, Protagonist, Roche, Roivant, Samsung, Sandoz, Sanofi, Takeda, Theravance Biopharma, Thermo Fisher Scientific, TiGenix, Tillotts Pharma, VectivBio, Ventyx, Viatris, Vifor Pharma and Ysopia; grants from Celltrion, Fresenius Kabi and Takeda; and speaking fees from AbbVie, Amgen, Arena Pharmaceuticals, Biogen, Celltrion, Eli Lilly, Ferring Pharmaceutics, Galapagos, Genentech, Gilead Sciences, Janssen Pharmaceuticals, Medac, MSD, Pfizer, Sandoz, Takeda, Tillotts Pharma, Viatris and Vifor Pharma.

Introduction

There are no prediction models for a diagnosis of inflammatory bowel disease (IBD) in primary care. Our aim was to develop an IBD risk prediction tool to reduce the length of time patients have undiagnosed IBD symptoms and improve IBD clinical outcomes.

Aims & Methods

We developed and internally validated a risk prediction tool for the diagnosis of IBD, ulcerative colitis (UC) and Crohn’s disease (CD).
A population-based retrospective open cohort study using Clinical Practice Research Datalink (CPRD) database was undertaken between 1st January 2010 and 31st December 2019 of all the patients aged 18 years or older. Patients were followed from first presentation with lower gastrointestinal (GI) symptoms potentially related to IBD to IBD diagnosis. Candidate predictors were chosen based on clinical and substantive knowledge. Cox proportional hazards regression with backward elimination was used for model development of IBD, UC and CD risk prediction models. The predictive performance of the models was assessed using discrimination (C-statistic). Internal validation using 1000 bootstrap sampling repetitions was used to estimate the optimism-corrected measures of predictive performance.

Results

Of 2,056,398 patients with lower GI symptoms: 11,192 patients had an IBD diagnosis (7033 UC (62.8%) and 4138 CD (37.0%)). The final IBD model included demographic factors (age, sex, ethnicity, smoking, index of multiple deprivation, body mass index, Charlson comorbidity index, loperamide use) co-existing conditions (anxiety, depression, irritable bowel syndrome (IBS), haemorrhoids), extraintestinal (EIM) manifestations (mouth ulcers, ophthalmic, primary sclerosing cholangitis, dermatological), laboratory investigations (low haemoglobin (Hb), low Mean Corpuscular volume (MCV), low albumin, low ferritin, raised C-reactive protein (CRP), raised Erythrocyte Sedimentation Rate (ESR), raised calprotectin level). The UC model included all above except mouth ulcers, IBS, ophthalmic EIM. Depression and ferritin were excluded in the CD model though low vitamin B12 level was included. The risk score for each patient was the sum of individual risk factors. In the development dataset, models showed moderate discrimination. For the IBD prediction tool: C-statistic 0.71 (95% CI 0.70-0.71),UC prediction tool: C-statistic 0.70 (95% CI 0.69-0.70) and CD prediction tool: C-statistic 0.75 (95% CI 0.74-0.76). The C-statistics of the models showed that the IBD, UC and CD prediction tools reliably differentiated patients with and without IBD,UC and CD respectively.

Conclusion

A risk score of patient demographics, symptoms and investigations performs well for IBD,UC and CD and may help in prioritising suspected IBD referrals in symptomatic subjects in primary care.

Disclosure

No conflict of interest to declare

Introduction

Glucagon-like-peptide-1 receptors agonists (GLP1-RA) are approved for treatment of diabetes mellitus (DM) and weight reduction.

Aims & Methods

We aimed to investigate the association between GLP1-RA use and poor preparation of colon.
Methods: A multicenter, retrospective study was conducted, including all colonoscopies performed across seven gastroenterology departments from 2016 to 2022. Patients who had undergone colon surgery or had conditions such as Parkinson's disease, cerebrovascular accidents (CVA), scleroderma, amyloidosis, gastroparesis, or those taking narcotic or anti-cholinergic drugs, as well as those with incomplete preparation data, were excluded from the study. The rates of "poor preparation" were collected.

Results

4,104 patients treated with GLP-1 receptor agonists (GLP-1 RA) (age 60 ± 0.2 years, 55.6% females) to 294,643 patients not treated with GLP1 RA (age 56.06 ± 13 years, 52.6% females), these patients were undergoing their first colonoscopy. The study found that 409 (10%) of GLP1 RA patients had poor colonic preparation, compared to 16,811 (5.7%) of patients not treated with GLP1 RA. Subgroup analysis revealed that 173 (8.3%) non-diabetic patients treated with GLP1 RA had poor colonic preparation, compared to 14,810 (5.4%) non-diabetic patients not treated with GLP1 RA (p<0.001). Among diabetic patients, 236 (11.7%) treated with GLP1 RA had poor colonic preparation, compared to 2,001 (9.7%) diabetic patients not treated with GLP1 RA (p=0.004). Multivariate analysis showed that both diabetes mellitus (DM) and treatment with GLP-1 RA were independent risk factors for poor colonic preparation, with odds ratios of 1.7 (p<0.001) and 1.3 (p<0.001), respectively.

Conclusion

GLP-1 RA among diabetes and non-diabetic have an impact on colonic preparation, indicating that these patients may require special consideration and tailored recommendations for their colonoscopy preparation. To establish the most effective approach for these patients, prospective studies are needed.

Introduction

Making an Inflammatory Bowel Disease (IBD) diagnosis can be challenging as symptoms may mimic more common conditions such as irritable bowel syndrome or haemorrhoids. This can lead to delays in IBD diagnosis, which can be associated with the need for more complex treatment or surgery.

Aims & Methods

This study aims to quantify the time to diagnosis for IBD and examine associations with patient characteristics and faecal calprotectin (FC) and its impact on surgery and emergency hospital admissions.
A retrospective cohort study was conducted using Clinical Practice Research Datalink (CPRD AURUM) from 1st Jan 2007 to 31st Dec 2019. Adult patients were followed from when they first presented with symptoms, signs, test results or alternative diagnosis potentially related to IBD in primary care in the 3 year period prior to IBD diagnosis. The association between patient level factors and FC with the time to IBD diagnosis were examined using logistic regression analysis. We examined changes in time to IBD diagnosis over the 10 year study period. The association of time to IBD diagnosis with IBD related surgeries and emergency admissions was also examined.

Results

Of 28,092 IBD patients: 60.0% ulcerative colitis (UC) and 40.0% Crohn’s disease (CD). Median age 43(IQR 30-58) years; 51.9% female. Median time to diagnosis 15.6 (IQR 4.3- 28.1) months; UC 13.8 (3.2-27.1) months and CD 17.8 (6.5-29.2) months.
Factors associated with over 12 months to IBD diagnosis: female ((OR 1.64(95%CI 1.56-1.72)); Black (1.45(1.20-1.76)) or Asian ethnicity (1.55(1.40-1.72); older age compared to 18-30 years (>70 1.14(1.03-1.27)); obesity (1.09(1.01-1.17)); smoking (1.13(1.06-1.20)); lower socioeconomic status (1.15 (1.06-1.25)); anxiety (1.15(1.07-1.24)); depression (1.19(1.11-1.28)); non-steroidal anti-inflammatory drugs (NSAIDs) (1.45(1.33-1.59)); loperamide (1.56 (1.31-1.86)) and increasing comorbidity Charlson score >2 (1.46(1.32-1.61)).
Only 11.3% of IBD patients had FC testing. Time to diagnosis from FC date was median 4.0 (IQR 1.8- 8.8) months. Higher FC levels were associated with decreasing time to IBD diagnosis: 500-1000 (0.76 (0.66-0.89)),>1000 (0.61(0.51-0.72)).
No improvement in time to IBD diagnosis was observed over the last 10 years: 2010-2011 (14.4 (3.6 -27.6) months) compared to 2018-2019 median (15.6 (4.8-28.8) months).
Patients with time to diagnosis in highest quartile were at lower risk of surgery within a year of diagnosis (HR 0.91(0.81-1.03) but had more IBD related emergency admissions after diagnosis (IRR 1.06 (1.01-1.11).

Conclusion

Longer times to diagnoses were associated with females, minority ethnicities, increasing age, increasing comorbidity, deprivation, obesity, anxiety, depression, NSAIDs and loperamide use. Faecal calprotectin testing reduced time to diagnoses. Higher number of IBD related emergency admissions were observed in patients with a prolonged time to diagnosis.

Disclosure

No conflict of interest to declare