Agreement between local and central reading of endoscopic disease activity in ulcerative colitis: results from the tofacitinib OCTAVE trials

Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale

OBJECTIVES

Existing endoscopic scores for ulcerative colitis (UC) objectively categorize disease severity based on the presence or absence of endoscopic findings; therefore, it may not reflect the range of clinical severity within each category. However, inflammatory bowel disease (IBD) expert endoscopists categorize the severity and diagnose the overall impression of the degree of inflammation. This study aimed to develop an artificial intelligence (AI) system that can accurately represent the assessment of the endoscopic severity of UC by IBD expert endoscopists.

METHODS

A ranking-convolutional neural network (ranking-CNN) was trained using comparative information on the UC severity of 13,826 pairs of endoscopic images created by IBD expert endoscopists. Using the trained ranking-CNN, the UC Endoscopic Gradation Scale (UCEGS) was used to express severity. Correlation coefficients were calculated to ensure that there were no inconsistencies in assessments of severity made using UCEGS diagnosed by the AI and the Mayo Endoscopic Subscore, and the correlation coefficients of the mean for test images assessed using UCEGS by four IBD expert endoscopists and the AI.

RESULTS

Spearman's correlation coefficient between the UCEGS diagnosed by AI and Mayo Endoscopic Subscore was approximately 0.89. The correlation coefficients between IBD expert endoscopists and the AI of the evaluation results were all higher than 0.95 (P < 0.01).

CONCLUSIONS

The AI developed here can diagnose UC severity endoscopically similar to IBD expert endoscopists.

Modified Mayo score versus Mayo score for evaluation of treatment efficacy in patients with ulcerative colitis: data from the tofacitinib OCTAVE program

OBJECTIVES

The subjectivity of the Physician Global Assessment (PGA) is a limitation of the Mayo score in assessing severity of ulcerative colitis (UC). We compared treatment efficacy using endpoint definitions based on modified Mayo (mMayo) score, versus those based on Mayo score, using data from the tofacitinib OCTAVE program.

DESIGN

This post hoc analysis included data from two 8-week induction studies (OCTAVE Induction 1 and 2) and a 52-week maintenance study (OCTAVE Sustain).

METHODS

Remission and clinical response [with nonresponder imputation (NRI)] were assessed using mMayo (without PGA) and Mayo scores, and further stratified by prior tumor necrosis factor inhibitor (TNFi) failure status.

RESULTS

At week 8 of OCTAVE Induction 1 and 2, remission rates with placebo and tofacitinib 10 mg twice daily (BID), respectively, were 7.7% and 24.8% (mMayo) and 6.0% and 17.6% (Mayo). At week 52 of OCTAVE Sustain, remission rates with placebo, tofacitinib 5 and 10 mg BID, respectively, were 12.1%, 35.9%, and 42.1% (mMayo) and 11.1%, 34.3%, and 40.6% (Mayo). A statistically significant (p < 0.05) treatment effect of tofacitinib versus placebo was observed for remission and clinical response at all time points, regardless of scoring definition or prior TNFi failure status.

CONCLUSIONS

A significant effect of tofacitinib versus placebo was demonstrated across efficacy endpoints using mMayo score, consistent with previously reported data using Mayo score. Treatment effect sizes were generally similar regardless of scoring definition. This observation may help contextualize tofacitinib therapy outcomes with those of new UC therapies and support the use of Mayo score-based endpoints in UC clinical trials.

TRAIL REGISTRATION

ClinicalTrials.gov identifiers: NCT01465763; NCT01458951; NCT01458574.