P1016 Real-world data on sequential therapy in moderate-to-severe UC: Unveiling second-line strategies after anti-TNF failure
Bibliographic record
Abstract
Abstract Background The development of anti-TNFs has brought major advances in the treatment of ulcerative colitis (UC). Yet, a significant proportion of patients do not respond favorably to first-line anti-TNF therapy, which may require the use of second-, third-, or fourth-line biologics or small molecules. Treatment selection and sequencing, however, remains a major unmet need. Methods We conducted a multicenter, retrospective study including patients with moderate-to-severe UC who failed first-line anti-TNFs, and received sequential therapy with biologics or small-molecules. The effectiveness of sequential therapy, more specifically second-line agents was determined and compared by treatment persistence and colectomy-free survival up to 3 years post-initiation, and assessed using Kaplan-Meier analyses. Multivariate Cox regression analysis was performed to identify the predictive value of various factors for colectomy and persistence. Results 683 UC patients were included. The median follow-up time was 62 months (IQR: 36-98), during which 14.2% of patients required colectomy. The probability of colectomy-free survival was 97.5%, 93.9% and 92.3% at 1, 2, and 3 years. Persistence rates increased significantly with the number of therapy lines (P<.0001). The presence of deep ulcers at diagnosis (HR: 0.45; P=.009), prior cyclosporine use (CYA; HR: 0.41; P=.028), and low serum albumin at first-line therapy (HR: 0.92; P=.002) appeared to be predictive for colectomy. Following anti-TNF failure, significantly higher colectomy-free survival rates were observed over 3 years with ustekinumab (UST; P=.014; Figure 1a.), than with vedolizumab or tofacitinib. Second-line UST also showed superior persistence at 3, 6, 12, and 24 months (P=.049; Figure 1b.), but not at 36 months. Neither the risk of colectomy (P=.343), nor the rate of persistence with second-line therapy (P=.19) was influenced by the reason of first-line anti-TNF discontinuation. Prior CYA use (HR: 0.42; P=.047) negatively influenced persistence with second-line therapy. Conclusion Despite multiple lines of sequential biological and small molecule therapy we found a low incidence of colectomy and a high rate of persistence. Following anti-TNF failure, regardless of its cause, UST might be the preferred second-line agent in moderate-to-severe UC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".