How disease extent can be included in the endoscopic activity index of ulcerative colitis: the panMayo score, a promising scoring system

Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE): An Artificial Intelligence Scoring System for a Spatial Assessment of Disease Severity in Ulcerative Colitis

BACKGROUND AND AIMS

Validated scoring methods such as the Mayo Clinic Endoscopic Subscore (MCES) evaluate ulcerative colitis (UC) severity at the worst colon segment, without considering disease extent. We present the Ulcerative Colitis Severity Classification and Localized Extent (UC-SCALE) algorithm, which provides a comprehensive and automated evaluation of endoscopic severity and disease extent in UC.

METHODS

Ulcerative Colitis Severity Classification and Localized Extent consists of 3 main elements: (1) a quality filter selecting readable images (frames) from colonoscopy videos, (2) a scoring system assigning an MCES to each readable frame, and (3) a camera localization algorithm assigning each frame to a location within the colon. Ulcerative Colitis Severity Classification and Localized Extent was trained and tested using 4326 sigmoidoscopy videos from phase III Etrolizumab clinical trials.

RESULTS

The high agreement between UC-SCALE and central reading at the level of the colon section (𝜅 = 0.80), and the agreement between central and local reading (𝜅 = 0.84), suggested a similar inter-rater agreement between UC-SCALE and experienced readers. Furthermore, UC-SCALE correlated with disease activity markers such calprotectin, C-reactive protein and patient-reported outcomes, Physician Global Assessment and Geboes Histologic scores (rs 0.40-0.55, ps < 0.0001). Finally, the value of using UC-SCALE was demonstrated by assessing individual endoscopic severity between baseline and induction.

CONCLUSIONS

Our fully automated scoring system enables accurate, objective, and localized assessment of endoscopic severity in UC patients. In addition, we provide a topological representation of the score as a marker of disease severity that correlates highly with clinical metrics. Ulcerative Colitis Severity Classification and Localized Extent reproduces central reading and holds promise to enhance disease severity evaluation in both clinical trials and everyday practice.

Assessment of Endoscopy-Based Scoring Systems for Prognostication in Ulcerative Colitis: A Comparative Analysis

BACKGROUND AND OBJECTIVE

Endoscopy-based scoring systems, including Mayo Endoscopic Score (MES), Modified Mayo Endoscopic Score (MMES), and Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) Score, have been introduced to evaluate UC prognosis. This study aims to compare their predictive capacity for clinical outcomes in UC patients.

METHODS

Consecutive UC patients from a tertiary hospital were included. The primary outcome was acute severe ulcerative colitis (ASUC), and secondary outcomes were UC-related admission, medication treatment escalation, disease extension and surgery. Predictive performance was assessed using receiver operating characteristic (ROC) curves.

RESULTS

Among 300 patients, 15.3% developed ASUC. Robust correlations were observed among the three scoring systems and were with elevated serum inflammatory markers. The DUBLIN score exhibited superior predictive ability for UC-related admission (AUC 0.751; 95%CI 0.698-0.799) and medication treatment escalation (AUC 0.735; 95% CI 0.681-0.784). No statistical differences were found among three scoring systems for predicting ASUC, disease extension, and surgery. Employing respective cut-offs of 2, 11.25, and 3, higher MES (HR = 3.859, 95% CI 1.636-9.107, p = 0.002), MMES (HR = 3.352, 95% CI 1.879-5.980, p < 0.001), and DUBLIN score (HR = 5.619, 95% CI 2.378-13.277, p < 0.001) were associated with an increased risk of developing ASUC.

CONCLUSION

The DUBLIN score, assessing the overall inflammatory burden of the intestinal tract, outperforms the MMES in predicting admission and medication treatment escalation related to UC. Its integration into clinical practice has the potential to enhance risk stratification for patients with UC.

Accuracy of the Pancolonic Modified Mayo Score in predicting the long-term outcomes of ulcerative colitis: a promising scoring system

Background

Different endoscopic scoring systems for assessing ulcerative colitis (UC) severity are available. However, most of them are not correlated with disease extent.

Objectives

Our study aimed to compare the predictive value of the PanMay score versus the endoscopic Mayo (MES), Ulcerative Colitis Endoscopic Index of Severity (UCEIS), and Dublin score in predicting long-term outcomes of UC.

Design

This retrospective study enrolled consecutive UC patients who underwent colonoscopy before at least a 3-year follow-up.

Methods

The PanMayo, MES, UCEIS, and Dublin scores and the baseline clinical and demographic characteristics of the participants were assessed. Endpoints were disease flare that required novel biological therapy, colectomy, and hospitalization. Patients were stratified using baseline clinical activity.

Results

Approximately 62.8% of the 250 enrolled patients were in clinical remission. In these patients, the PanMayo, MES, and Dublin scores were positively associated with the risk of clinical flare. The MES score increased with clinical flare. The PanMayo score (>12 points), but not the MES score, was associated with the need for novel biological initiation and biological escalation. Furthermore, the Dublin and UCEIS scores of patients in remission who need novel biological treatment had a similar trend. Colectomy risk was associated with PanMayo and Dublin scores.

Conclusion

The combined endoscopic assessment of disease extent and severity can be more accurate in predicting outcomes among patients with UC. PanMayo score can be utilized in addition to the existing scoring systems, thereby leading to a more accurate examination.

Summary

UC endoscopic scores do not assess extension. Our study aimed to analyze the predictive value of the PanMayo score. Based on 250 patients, results showed that the long-term disease outcomes of UC could be predicted with the PanMayo score more accurately.

Clinical value of the Toronto inflammatory bowel disease global endoscopic reporting score in ulcerative colitis

BACKGROUND

Endoscopic evaluation in diagnosing and managing ulcerative colitis (UC) is becoming increasingly important. Several endoscopic scoring systems have been established, including the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score and Mayo Endoscopic Subscore (MES). Furthermore, the Toronto Inflammatory Bowel Disease Global Endoscopic Reporting (TIGER) score for UC has recently been proposed; however, its clinical value remains unclear.

AIM

To investigate the clinical value of the TIGER score in UC by comparing it with the UCEIS score and MES.

METHODS

This retrospective study included 166 patients with UC who underwent total colonoscopy between January 2017 and March 2023 at the Affiliated Hospital of Qingdao University (Qingdao, China). We retrospectively analysed endoscopic scores, laboratory and clinical data, treatment, and readmissions within 1 year. Spearman's rank correlation coefficient, receiver operating characteristic curve, and univariate and multivariable logistic regression analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, United States) and GraphPad Prism version 9.0.0 for Windows (GraphPad Software, Boston, Massachusetts, United States).

RESULTS

The TIGER score significantly correlated with the UCEIS score and MES (r = 0.721, 0.626, both P < 0.001), showed good differentiating values for clinical severity among mild, moderate, and severe UC [8 (4-112.75) vs 210 (109-219) vs 328 (219-426), all P < 0.001], and exhibited predictive value in diagnosing patients with severe UC [area under the curve (AUC) = 0.897, P < 0.001]. Additionally, the TIGER (r = 0.639, 0,551, 0.488, 0.376, all P < 0.001) and UCEIS scores (r = 0.622, 0,540, 0.494, and 0.375, all P < 0.001) showed stronger correlations with laboratory and clinical parameters, including C-reactive protein, erythrocyte sedimentation rate, length of hospitalisation, and hospitalisation costs, than MES (r = 0.509, 0,351, 0.339, and 0.270, all P < 0.001). The TIGER score showed the best predictability for patients' recent advanced treatment, including systemic corticosteroids, biologics, or immunomodulators (AUC = 0.848, P < 0.001) and 1-year readmission (AUC = 0.700, P < 0.001) compared with the UCEIS score (AUC = 0.762, P < 0.001; 0.627, P < 0.05) and MES (AUC = 0.684, P < 0.001; 0.578, P = 0.132). Furthermore, a TIGER score of ≥ 317 was identified as an independent risk factor for advanced UC treatment (P = 0.011).

CONCLUSION

The TIGER score may be superior to the UCIES score and MES in improving the accuracy of clinical disease severity assessment, guiding therapeutic decision-making, and predicting short-term prognosis.

[Improved Mayo Endoscopic Score has a higher value for evaluating clinical severity of ulcerative colitis]

OBJECTIVE

To assess the value of Improved Mayo Endoscopic Score (IMES) for evaluation of the clinical severity of ulcerative colitis (UC).

METHODS

We retrospectively analyzed the clinical and endoscopic data of 167 patients diagnosed with UC in Beijing Tsinghua Changgung Hospital from January, 2015 to November, 2021. The severity of endoscopic lesions was determined by Mayo Endoscopic Score (MES, 0-3 points) and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score (0-8 points), and the scope of endoscopic lesions was evaluated based on the Montreal classification system. The IMES was established by combining the MES with the Montreal classification.

RESULTS

The IMSE showed stronger correlations with modified Truelove and Witts Disease Severity, Mayo score and partial Mayo score (r=0.712, 0.784, and 0.703, respectively) than MES (r=0.642, 0.754, and 0.604, respectively), Montreal classification (r=0.598, 0.628, and 0.603, respectively) and UCEIS (r= 0.670, 0.767, and 0.677, respectively). ROC curve analysis showed that IMES was superior to MES, Montreal and UCEIS in diagnosis of severe and moderate- to-severe UC. IMES also showed stronger correlations with the laboratory indicators including CRP (r=0.583), WBC (r=0.235), HB (r=-0.280), PLT (r=0.352), ALB (r=-0.396) and ESR (r=0.471) than MES and Montreal classification. An IMES score of 5 was of greater value than a MES score of 3, E3, and UCEIS≥6 for predicting the administration of systemic hormones, immunosuppressants, or surgery in the near future.

CONCLUSION

IMES can better reflect the clinical severity of UC and has good correlations with the laboratory indicators of the patients.

Inflammatory Bowel Disease Treatments and Predictive Biomarkers of Therapeutic Response

Inflammatory bowel disease (IBD) is a chronic immune-mediated inflammation of the gastrointestinal tract with a highly heterogeneous presentation. It has a relapsing and remitting clinical course that necessitates lifelong monitoring and treatment. Although the availability of a variety of effective therapeutic options including immunomodulators and biologics (such as TNF, CAM inhibitors) has led to a paradigm shift in the treatment outcomes and clinical management of IBD patients, some patients still either fail to respond or lose their responsiveness to therapy over time. Therefore, according to the recent Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE-II) recommendations, continuous disease monitoring from symptomatic relief to endoscopic healing along with short- and long-term therapeutic responses are critical for providing IBD patients with a tailored therapy algorithm. Moreover, considering the high unmet need for novel therapeutic approaches for IBD patients, various new modulators of cytokine signaling events (for example, JAK/TYK inhibitors), inhibitors of cytokines (for example IL-12/IL-23, IL-22, IL-36, and IL-6 inhibitors), anti-adhesion and migration strategies (for example, β7 integrin, sphingosine 1-phosphate receptors, and stem cells), as well as microbial-based therapeutics to decolonize the bed buds (for example, fecal microbiota transplantation and bacterial inhibitors) are currently being evaluated in different phases of controlled clinical trials. This review aims to offer a comprehensive overview of available treatment options and emerging therapeutic approaches for IBD patients. Furthermore, predictive biomarkers for monitoring the therapeutic response to different IBD therapies are also discussed.

Comparing the clinical application values of the Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) score and Ulcerative Colitis Endoscopic Index of Severity (UCEIS) in patients with ulcerative colitis

Background

The significance of endoscopic evaluation in the diagnosis and management of ulcerative colitis (UC) has been widely recognized. Over the years, scholars have established several endoscopic scores. Herein, we assessed the clinical application value of the Mayo Endoscopic Subscore (Mayo ES), the Degree of Ulcerative Colitis Burden of Luminal Inflammation (DUBLIN) score, and the Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score in UC patients, by comparing their correlation with disease activity and their predictive potential for treatment response and clinical outcomes.

Methods

UC patients hospitalized from September 2015 to September 2019 were retrospectively analysed. We employed Spearman's rank correlation coefficient to assess the linear association of the assessed endoscopic scores with the clinical parameters. The receiver-operating characteristic curve was applied to evaluate the predictive capabilities of the endoscopic scores for treatment escalation and 1-year readmission.

Results

A total of 178 patients were enrolled; most of them (82%) suffered moderate or severe colitis. Among them, 48 (27%) patients received treatment escalation and 59 (33%) were readmitted within 1 year. The DUBLIN and UCEIS scores demonstrated higher correlations with clinical parameters than the Mayo ES. The DUBLIN scores significantly differed between patients with mild, moderate, and severe colitis (all P < 0.001). The UCEIS scores demonstrated the best predictabilities for treatment escalation and 1-year readmission with an area under the curve of 0.88 and 0.75, respectively. Compared to the UCEIS and DUBLIN scores, the predictive capabilities of the Mayo ES for treatment escalation (both P < 0.001) and 1-year readmission (P < 0.001 and P = 0.002, respectively) were lower. The UCEIS scores exhibited a significant difference between the steroid-responsive group and the steroid-dependent or steroid-refractory group (both P < 0.001), while no significant differences in the Mayo ES and DUBLIN scores were found among the three groups (both P > 0.05).

Conclusion

This study demonstrates that both the DUBLIN and UCEIS scores outperform the Mayo ES in assessing disease severity and predicting treatment response and clinical outcomes in UC patients.

Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

Introduction:

The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading.

Methods:

Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis.

Results and Conclusion:

Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation.

Plain language summary

Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety

Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

Treat-to-Target in Pediatric Inflammatory Bowel Disease: What Does the Evidence Say?

The traditional management of inflammatory bowel disease, based on treatment intensification guided by clinical activity alone, has been revised in the last 10 years and a treat-to-target approach has been proposed and is currently under evaluation as a disease-modifying strategy. Treat-to-target focuses on objective and scheduled measures to monitor intestinal damage, with consequent therapeutic adjustments in case of failure to achieve pre-defined targets. Identification of targets has been set out by the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) committee in 2015. Mucosal healing is universally accepted as the main target both in Crohn's disease and ulcerative colitis, given its proven association with better long-term outcomes than clinical remission alone. Equally important is to ensure patients' clinical remission and improve patient-reported outcomes. Transmural healing (for Crohn's disease) and histological remission (for ulcerative colitis), listed as adjunctive targets, are likely to become primary targets in the near future. The ultimate goal of this approach is to modify the natural history of inflammatory bowel diseases by trying to block bowel damage progression, with interventions in the pre-clinical stage. In this review, we will discuss the current recommended therapeutic targets, as well as those that are considered adjunctive targets, with a focus on the limited pediatric literature available. Prospective long-term trials are warranted in order to identify the most appropriate target for the pediatric population and its specific issues. Identification of reliable predictors of disease course, outcome, and response to treatment will help to individually adapt each step of this monitoring algorithm and consequent therapeutic decision.

Treat-to-Target in Inflammatory Bowel Diseases, What Is the Target and How Do We Treat?

Inflammatory bowel diseases, including Crohn disease (CD) and ulcerative colitis (UC), are chronic, progressive, immune-mediated inflammatory diseases of the gastrointestinal tract. Early therapy using a treat-to-target (T2T) approach, which implies identification of a pre-defined target, followed by optimization of therapy and regular monitoring until the goal is achieved is critical in preventing adverse long-term outcomes. In this review, the authors discuss the T2T guidance developed by the Selecting Therapeutic Targets in Inflammatory Bowel Disease committee, new evidence published on the role of various targets in CD and UC, as well as the real-world applicability of T2T."

A Treat-to-Target Update in Ulcerative Colitis: A Systematic Review

OBJECTIVES

In 2015, the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) program proposed shifting the therapeutic focus on ulcerative colitis (UC) toward altering the natural history of the disease course by regularly monitoring objective measurements of disease activity and tailoring treatment accordingly. The therapeutic paradigm shift was well received in the research community and is often cited. However, new evidence on optimal UC treatment targets continues to accumulate since the time of the STRIDE guidelines. This systematic review summarizes the evidence accrued since the STRIDE UC recommendations, discusses the barriers for adoption of treat-to-target approaches in clinical practice in UC, and suggests directions for future research.

METHODS

We systematically reviewed MEDLINE for studies from the time of the STRIDE systematic review up to March 31, 2018, that assessed the potential treatment targets identified by the STRIDE recommendations.

RESULTS

Each potential treatment target literature search returned > 200 articles, which were then reviewed by 2 independent investigators for relevant studies. Selected studies of clinical factors, patient-reported outcomes, endoscopy, histology, imaging, and biomarkers and implications on treatment targets are summarized.

CONCLUSIONS

It appears that the relative weight given to different therapeutic targets in the development and improvement of UC treatments could be optimized, with an increased emphasis on endoscopic and histological targets over clinical or symptomatic targets. For this evolution to occur, however, new research has to demonstrate that the treat-to-target approach will deliver on the promise of better long-term outcomes compared with current approaches.