Exclusive enteral nutrition (EEN) is an established dietary therapy for Crohn's disease but its role in ulcerative colitis remains unclear.

To investigate the efficacy of EEN in adults with active ulcerative colitis and compare variations in treatment protocols, safety, tolerability and adherence.

We conducted a systematic search of MEDLINE, Embase, Cochrane CENTRAL, Emcare, CINAHL, Web of Science and trial registries for articles published from inception until July 21, 2024. We included all experimental and observational studies that described the use of EEN in adults with active ulcerative colitis. This review was registered on PROSPERO (CRD42022319584).

Of 3273 articles screened, we included 10 studies (334 adults). Overall, there was no difference between EEN and comparator for ulcerative colitis remission induction (median follow-up 14 days, risk ratio (RR) 1.15, 95% confidence interval (CI) 0.71-1.85; 2 studies). In acute severe ulcerative colitis, there was no difference between EEN and comparator for corticosteroid failure (RR 0.76, 95% CI 0.48-1.20; 2 studies) or risk of colectomy (RR 0.88, 95% CI 0.51-1.51, n = 2 studies) during index admission. The pooled discontinuation rate was 3% (95% CI 0-10; 9 studies). There was heterogeneity in trial design, methodology and assessment of outcomes.

EEN was well tolerated with low therapy discontinuation in adults with active ulcerative colitis. However, there is insufficient evidence to support the use of EEN as an adjunctive therapy to standard of care. Further, well-designed studies with reproducible methodology and endpoints are necessary to evaluate its effectiveness.

PROSPERO 2022 CRD42022319584.

Inflammatory bowel disease (IBD) is rising at an alarming rate in south Asia and there is a paucity of data on IBD in this region. For this scoping review, we conducted a systematic search to identify all observational and interventional studies on IBD in south Asia. Of 14 924 potentially eligible studies, 524 were included in this scoping review and summarised under the domains of epidemiology, natural history, phenotype and comorbid conditions, therapeutics, and psychosocial health. According to the literature, IBD incidence and prevalence are rising in south Asia and among south Asian immigrants, and the diagnostic rate is higher in men than in women. Genetic predisposition is an important risk factor in south Asia, whereas environmental risk factors are less clear. Delay in diagnosis, although possibly decreasing over time, is common in south Asia and is associated with worse outcomes. There are no clear differences in IBD phenotype and severity in south Asia relative to Europe and North America. Corticosteroids and immunomodulators are the mainstay of treatment in south Asia whereas the use of biologics is less common. Mental health disorders, malnutrition, and reduced quality of life are prevalent in patients with IBD in south Asia, and the use of complementary and alternative medicines among patients is an important consideration. Key knowledge gaps include the paucity of data from countries other than India, prospective, long-term, follow-up studies, and clinical drug trials in south Asia. IBD is a growing challenge in this region and warrants urgent clinical interventions, research, resource allocation, and health policy implementation.

Acute severe ulcerative colitis (ASUC) poses challenges to patient management owing to its high surgical rate. This study aimed to identify predictors of colectomy in patients with ASUC.

This is a systematic review and meta-analysis.

PubMed and Web of Science were searched up to April 2024.

Studies on the predictors of colectomy in adult patients with ASUC were eligible.

Two reviewers independently extracted the data using a prespecified data collection sheet. A qualitative synthesis was performed in tabular form. Random-effect meta-analyses were conducted using OR and 95% CI.

Forty-two studies were included in the systematic review. The reported variables can be categorised into biomarkers, auxiliary examination findings, demographic and clinical characteristics, and drug factors. Through meta-analysis, albumin (OR 0.39 (95% CI 0.26 to 0.59) per 1 g/dL increment, I2=0.0%), high C reactive protein level (2.63 (1.53 to 4.52), I2=29.6%), high erythrocyte sedimentation rate level (2.92 (1.39 to 6.14), I2=0.0%), low haemoglobin level (2.08 (1.07 to 4.07), I2=56.4%), fulfilling the Oxford criteria (4.42 (2.85 to 6.84), I2=0.0%), extensive colitis (1.85 (1.24 to 2.78), I2=47.5%), previous steroids (1.75 (1.23 to 2.50), I2=17.7%) or azathioprine (2.25 (1.28 to 3.96), I2=0.0%) use, and sarcopenia (1.90 (1.04 to 3.45), I2=0.0%) were identified as valuable predictors for colectomy within 1 year. The ulcerative colitis endoscopic index of severity (OR 2.41 (95% CI 1.72 to 3.39), I2=1.5%) was the only predictor found to predict colectomy over 1 year.

Identification of these predictors may facilitate risk stratification of patients with ASUC, drive personalised treatment and reduce the need for colectomy.

The Oxford and Swedish indexes were developed to predict in-hospital colectomy in acute severe ulcerative colitis (ASUC), but not long-term prediction, and all these indexes were based on Western data. Our study aimed to analyze the predictors of colectomy within 3 years of ASUC in an Indian cohort and derive a simple predictive score.

A prospective observational study was conducted in a tertiary health care center in South India over a period of 5 years. All patients admitted with ASUC were followed up for a period of 24 months after the index admission, to look for progression to colectomy.

A total of 81 (47 male) patients were included in the derivation cohort. Fifteen (18.5%) patients required colectomy during a follow-up period of 24 months. On regression analysis, C-reactive protein (CRP) and serum albumin were independent predictors of 24-month colectomy. The CRAB (CRP + AlBumin) score was obtained by multiplying coefficient of beta to albumin and CRP (CRAB score = CRP x 0.2 - Albumin x 0.26). The CRAB score demonstrated an AUROC of 0.923 and a score of >0.4 with a sensitivity of 82% and specificity of 92% for the prediction of 2-year colectomy following ASUC. The score was validated in a validation cohort of 31 patients, and at >0.4, the score had a sensitivity of 83% and a specificity of 96% in predicting colectomy.

CRAB score is a simple prognostic score that can predict 2-year colectomy in ASUC patients with high sensitivity and specificity.

This study aimed to explore the effectiveness of predicting disease activity in patients with inflammatory bowel disease (IBD), using machine learning (ML) models. A retrospective research was undertaken on IBD patients who were admitted into the First Affiliated Hospital of Wenzhou Medical University between September 2011 and September 2019. At first, data were randomly split into a 3:1 ratio of training to test set. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to reduce the dimension of variables. These variables were used to generate seven ML algorithms, namely random forests (RFs), adaptive boosting (AdaBoost), K-nearest neighbors (KNNs), support vector machines (SVMs), naïve Bayes (NB), ridge regression, and eXtreme gradient boosting (XGBoost) to train to predict disease activity in IBD patients. SHapley Additive exPlanation (SHAP) analysis was performed to rank variable importance. A total of 876 participants with IBD, consisting of 275 ulcerative colitis (UC) and 601 Crohn's disease (CD), were retrospectively enrolled in the study. Thirty-three variables were obtained from the clinical characteristics and laboratory tests of the participants. Finally, after LASSO analysis, 11 and 5 variables were screened out to construct ML models for CD and UC, respectively. All seven ML models performed well in predicting disease activity in the CD and UC test sets. Among these ML models, SVM was more effective in predicting disease activity in the CD group, whose AUC reached 0.975, sensitivity 0.947, specificity 0.920, and accuracy 0.933. AdaBoost performed best for the UC group, with an AUC of 0.911, sensitivity 0.844, specificity 0.875, and accuracy 0.855. ML algorithms were available and capable of predicting disease activity in IBD patients. Based on clinical and laboratory variables, ML algorithms demonstrate great promise in guiding physicians' decision-making.

Acute severe ulcerative colitis (ASUC) is a life-threatening medical emergency with considerable morbidity (30% to 40%). Patients with ASUC require hospitalization for prompt medical treatment, and colectomy is considered if medical therapy fails. Corticosteroids remain the primary initial therapy, although one-third of patients do not respond to treatment. Clinical data have indicated that cyclosporine, tacrolimus, and infliximab can be used to treat patients with ASUC who do not respond to intravenous corticosteroids. The effectiveness and safety of sequential therapy have recently been reported; however, the data are not convincing. Importantly, timely decision-making with rescue therapy or surgical treatment is critical to manage ASUC without compromising the health or safety of the patients. In addition, risk stratification and the use of predictive clinical parameters have improved the clinical outcome.of ASUC. Multidisciplinary teams that include inflammatory bowel disease experts, colorectal surgeons, and other medical staff contribute to the better management of patients with ASUC. In this review, we introduce current evidence and present a clinical approach to manage ASUC.

Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time.

On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research.

Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation.

Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.

Colectomy risk after acute severe ulcerative colitis (ASUC) has not been compared between Eastern and Western countries. We compared the 1-year colectomy risk after ASUC between Korea and the USA.

Data on patients admitted for ASUC to five tertiary referral hospitals in Korea and Mount Sinai Hospital, New York, the USA, between January 2015 and January 2019 were reviewed retrospectively. For comparability between groups, a 1:1 propensity score matching (PSM) was performed. The primary outcome was colectomy, and secondary outcomes were mortality, readmission, and venous thromboembolism (VTE) within 1-year of the index admission for ASUC. The risk of each outcome was compared using Cox proportional hazards model in pre-matched cohort and Kaplan-Meier analysis with log-rank test in post-matched cohort.

290 ASUC patients were included in the study (121 Korea, 169 the USA). After PSM, 56 patients were selected in each group with no significant differences in baseline variables. At 1 year after ASUC, colectomy was less common in Korea than in the USA [3 (5.4%) vs. 24 (42.9%), p < 0.001]. The cumulative colectomy risk was significantly higher in the USA than in Korea in pre-matched cohort [adjusted hazard ratio 7.89, 95% confidence interval 3.23 to 19.22] and in post-matched cohort (log-rank p < 0.001), while there was no difference in cumulative risk of mortality, readmission, and VTE.

Colectomy risk within 1 year of ASUC is significantly higher in the USA than in Korea. We observed no differences in mortality, readmission, and VTE between the two groups.

This is the second of a series of two articles reporting the European Crohn's and Colitis Organisation [ECCO] evidence-based consensus on the management of adult patients with ulcerative colitis [UC]. The first article is focused on medical management, and the present article addresses medical treatment of acute severe ulcerative colitis [ASUC] and surgical management of medically refractory UC patients, including preoperative optimisation, surgical strategies, and technical issues. The article provides advice for a variety of common clinical and surgical conditions. Together, the articles represent an update of the evidence-based recommendations of the ECCO for UC.

Hyperbaric oxygen therapy (HBOT) improves short-term outcomes for ulcerative colitis (UC) patients hospitalized for acute flares. Longer-term impacts and cost-effectiveness are unknown.

We compared disease outcomes and cost-effectiveness of HBOT in addition to standard of care versus standard of care alone for UC patients hospitalized for acute flares using a microsimulation model. Published literature was used for transition probabilities, costs, and quality-adjusted life year (QALY) estimates. We modeled 100,000 individuals in each group over a 5-year horizon and compared rates of re-hospitalization, rescue medical therapy, colectomy, death, and cost-effectiveness at a willingness-to-pay of $100,000/QALY. Probabilistic sensitivity analyses were performed with 500 samples and 250 trials, in addition to multiple microsimulation sensitivity analyses.

The use of HBOT at the time of index hospitalization for an acute UC flare is projected to reduce the risk of re-hospitalization, inpatient rescue medical therapy, and inpatient emergent colectomy by over 60% (p < 0.001) and mortality by over 30% (p <0.001), during a 5-year horizon. The HBOT strategy costs more ($5600 incremental cost) but also yielded higher QALYs (0.13 incremental yield), resulting in this strategy being cost-effective ($43,000/QALY). Results were sensitive to HBOT costs and rates of endoscopic improvement with HBOT. Probabilistic sensitivity analyses observed HBOT to be more cost-effective than standard of care in 95% of iterations.

The use of HBOT to optimize response to steroids during the index hospitalization for an acute UC flare is cost-effective and is projected to result in significant reductions in disease-related complications in the long term.

In this study we present the evidence based management for patients hospitalized for UC with a special focus on the synergic approach of the two key actors of the inflammatory bowel disease multidisciplinary team (IBD-MDT): gastroenterologist and surgeon.

Focused treatment by a specialized IBD-MDT and early involvement of the colorectal surgeon in the management of hospitalized ulcerative colitis patients is advocated. The colectomy rate has not changed over the years. Moreover delayed surgery after admission is burden by increase complication and mortality. Thus, it is pivotal to identify the patients who are likely to undergo surgery, by mean of predictors of outcome, and to not prolong ineffective medical treatment. The perfect timing based on clinical close monitoring is crucial. Up to 25% of patients with ulcerative colitis (UC) may require hospitalization. The aim of admission is to evaluate severity of the disease, exclude infections and establish proper treatment while monitoring the response. During admission, the patient has to be closely observed for the possible development of toxic megacolon or perforation, which should prompt emergency colectomy. Up to 30% of UC patients will fail to respond to initial intravenous corticosteroid. Non responder or partial responder to medical therapy should be evaluated for timely surgery or could be considered for rescue medical therapy.

There is consensus on the criteria used to define acute severe ulcerative colitis (ASUC) and on patient management, but it has been a challenge to identify patients at risk for colectomy based on data collected at hospital admission. We aimed to develop a system to determine patients' risk of colectomy within 1 y of hospital admission for ASUC based on clinical, biomarker, and endoscopy data.

We performed a retrospective analysis of consecutive patients with ASUC treated with corticosteroids, ciclosporin, or tumor necrosis factor (TNF) antagonists and admitted to 2 hospitals in France from 2002 through 2017. Patients were followed until colectomy or loss of follow up. A total of 270 patients with ASUC were included in the final analysis, with a median follow-up time of 30 months (derivation cohort). Independent risk factors identified by Cox multivariate analysis were used to develop a system to identify patients at risk for colectomy 1 y after ASUC. We developed a scoring system based on these 4 factors (1 point for each item) to identify high-risk (score 3 or 4) vs low-risk (score 0) patients. We validated this system using data from an independent cohort of 185 patients with ASUC treated from 2006 through 2017 at 2 centers in France.

In the derivation cohort, the cumulative risk of colectomy was 12.3% (95% CI, 8.6-16.8). Based on multivariate analysis, previous treatment with TNF antagonists or thiopurines (hazard ratio [HR], 3.86; 95% CI, 1.82-8.18), Clostridioides difficile infection (HR, 3.73; 95% CI, 1.11-12.55), serum level of C-reactive protein above 30 mg/L (HR, 3.06; 95% CI, 1.11-8.43), and serum level of albumin below 30 g/L (HR, 2.67; 95% CI, 1.20-5.92) were associated with increased risk of colectomy. In the derivation cohort, the cumulative risks of colectomy within 1 y in patients with scores of 0, 1, 2, 3, or 4 were 0.0%, 9.4% (95% CI, 4.3%-16.7%), 10.6% (95% CI, 5.6%-17.4%), 51.2% (95% CI, 26.6%-71.3%), and 100%. Negative predictive values ranged from 87% (95% CI, 82%-91%) to 92% (95% CI, 88%-95.0%). Findings from the validation cohort were consistent with findings from the derivation cohort.

We developed a scoring system to identify patients at low-risk vs high-risk for colectomy within 1 y of hospitalization for ASUC, based on previous treatment with TNF antagonists or thiopurines, C difficile infection, and serum levels of CRP and albumin. The system was validated in an external cohort.

Intravenous corticosteroids are the mainstay of therapy for acute severe ulcerative colitis (ASUC), but 30%-40% of patients fail to respond.

To investigate the effectiveness of exclusive enteral nutrition (EEN) as adjunctive therapy to intravenous corticosteroids in patients with ASUC.

This was an open-label randomised controlled trial, in which patients who were admitted with ASUC between August 2018 and May 2020 were randomised 1:1 to EEN or standard of care (SOC). Patients on EEN received a semi-elemental formula for 7 days along with SOC. The primary outcome was corticosteroid failure, defined by the need for salvage medical therapy or colectomy. Faecal microbial analysis was performed on day 1 and day 7 by 16s ribosomal RNA sequencing in some patients.

Of 62 patients (mean age 35.3 ± 12.1 years, 40% male), 32 were randomised to EEN and 30 to SOC. Corticosteroid failure was lower on EEN compared to SOC (intention-to-treat analysis 25% vs 43%, P = 0.051; per protocol analysis 19% vs 43%, P = 0.04), without any difference in colectomy rate (9% vs 13%; P = 0.41). Patients on EEN had a shorter hospital stay [median (range) 10 (8-17) vs 13 (8-24) days; P = 0.04], higher day 7 albumin level (34 ± 4 vs 29 ± 3 g/L, P < 0.01), greater reduction in serum C-reactive protein and faecal calprotectin levels (both P = 0.04) and a lower composite outcome of colectomy/hospitalisation at 6 months (16% vs 39%; P = 0.045) compared to SOC. Patients on EEN showed increased abundance of Erysipelotrichaceae on day 7, with reduced Bifidobacterium and Veillonellaceae compared to SOC.

EEN for 7 days may augment corticosteroid responsiveness in patients with ASUC. (REF/2018/05/019844; CTRI/2020/06/025989).

Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.

Ulcerative colitis and Crohn's disease are the main entities of inflammatory bowel disease characterized by chronic remittent inflammation of the gastrointestinal tract. The incidence and prevalence are on the rise worldwide, and the heterogeneity between patients and within individuals over time is striking. The progressive advance in our understanding of the etiopathogenesis coupled with an unprecedented increase in therapeutic options have changed the management towards evidence-based interventions by clinicians with patients. This guideline was stimulated and supported by the Emirates Gastroenterology and Hepatology Society following a systematic review and a Delphi consensus process that provided evidence- and expert opinion-based recommendations. Comprehensive up-to-date guidance is provided regarding diagnosis, evaluation of disease severity, appropriate and timely use of different investigations, choice of appropriate therapy for induction and remission phase according to disease severity, and management of main complications.

About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options.

Data of 263 patients (mean age 37.0 ± 14.0-years, 176, 77% male) with acute severe UC admitted during a 12-year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN).

Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C-reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower.

It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

In this study, we present the evidence-based management for patients hospitalized for ulcerative colitis (UC) with a special focus on the synergic approach of the two key actors of the inflammatory bowel disease multidisciplinary team (IBD-MDT): gastroenterologist and surgeon.

Focused treatment by a specialized IBD-MDT and early involvement of the colorectal surgeon in the management of hospitalized UC patients is advocated. The colectomy rate has not changed over the years. Moreover, delayed surgery after admission is burden by increase complication and mortality rates. Thus, it is pivotal to identify the patients who are likely to undergo surgery, by mean of predictors of outcome, and not to prolong ineffective medical treatment. The perfect timing based on clinical close monitoring is crucial. Up to 25% of patients with ulcerative colitis (UC) may require hospitalization. The aim of admission is to evaluate severity of the disease, exclude infections and establish proper treatment while monitoring the response. During admission, the patient has to be closely observed for the possible development of toxic megacolon or perforation, which should prompt emergency colectomy. Up to 30% of UC patients will fail to respond to initial intravenous corticosteroid. Non-responder or partial responder to medical therapy should be evaluated for timely surgery or could be considered for rescue medical therapy.

Acute severe ulcerative colitis is a life-threatening medical emergency, which can be associated with significant morbidity and is preventable through prompt and effective management. Corticosteroids remain the cornerstone of initial therapy, although a third of patients will not respond. Further management hinges on timely decisions with use of rescue therapy with ciclosporin or infliximab, without compromising the health or safety of the patient, or timely surgery. Although such patients need specialist care, it is imperative that emergency care physicians are aware of the important principles of management of this condition to achieve successful outcomes. Risk stratification and the use of predictive models using clinical parameters have reduced the morbidity associated with this condition.We discuss current evidence and present a clinical approach to clinicians involved in the emergency care of patients with acute severe ulcerative colitis in this review.

Requirement for hospitalization in ulcerative colitis (UC) is a marker of severity of disease. However, the paradigm of when to escalate therapy in such patients and the benefits of early immunomodulator initiation is less well established.

To examine the benefits of early therapy escalation in immunosuppression-naïve patients hospitalized with severe ulcerative colitis responsive to steroids.

We identified hospitalized UC patients who were immunosuppression naïve at index hospitalization and responded to intravenous steroids, not requiring medical or surgical rescue therapy. The 'therapy escalated' group comprised of those who were initiated on immunomodulators within 3 months of hospitalization. The need for colectomy at 12 months was compared to the 'not escalated' group who remained on non-immunosuppressive therapy.

Among 133 immunosuppressive naïve patients hospitalized for ulcerative colitis, 13 (9.8%) who responded to intravenous steroids and did not require rescue therapy underwent colectomy by 1 year. Among 123 patients who escalated to either immunomodulators (n = 46, 37%) or remained on non-immunosuppressive therapy (92% on 5-ASA), there was no difference in the need for colectomy at 1 year (10.8 vs. 7.8%; multivariate OR 1.29, 95% CI 0.35-4.74). There was also no difference in the time to colectomy between the two groups (p = 0.55).

Immunosuppression-naïve ASUC patients who respond to intravenous steroids remain at risk for colectomy. Immunomodulator initiation by 3 months did not reduce risk of colectomy at 1 year. There is an important need for prospective studies identifying thresholds for therapy escalation in UC.