P1152 Single center study of using clinical decision support tool to predict outcomes of vedolizumab therapy in Crohn’s disease patients
Bibliographic record
Abstract
Abstract Background There is a need to identify Crohn’s Disease (CD) patients who will be most suitable and obtain most clinical benefit from treatment with vedolizumab (VDZ), an anti-α4β7 integrin inhibitor for the treatment of ulcerative colitis and Crohn’s Disease (CD). Previously, a clinical decision support tool (CDST) has been developed and validated to guide treatment decisions in CD patients treated with VDZ.1 In this study, we analyzed the ability of the CDST to predict clinical outcomes in CD patients. Methods Patients with moderate to severe CD receiving maintenance treatment with VDZ in single center were retrospectively analyzed using the CDST. We divided patient by CDST into low (≤13 points), intermediate (>13 and ≤19 points) and high response probability groups (>19 points) based on the previously developed scoring system. Clinical outcomes assessed included persistence on therapy and CD-related hospitalizations and surgery rates. Results We included 174 patients who were treated with VDZ for at least 6 months in our center. Using the CDST, at baseline, 23 patients (13%) had a low, 81 (47%) had an intermediate, and 70 (40%) had a high probability of treatment response with VDZ. Patients with low probability showed a trend toward reduced persistence on therapy, though this difference did not reach statistical significance (p=0.13). However, when patients were analyzed according to disease location by Montreal classification, a clearer pattern emerged in those with isolated ileal (L1) or colonic (L2) CD. In this subgroup, patients with high probability of treatment response to VDZ demonstrated significantly longer treatment persistence (p=0.038); 91% of these patients remained on therapy after two years compared to 55% in the intermediate group. This trend was not observed in ileocolonic (L3) CD, where persistence rates were similar across groups (83% in high and intermediate groups and 75% in the low probability group; p=0.23). We tried to assess the ability of CDST in prediction of complications of CD and we observed that number of hospitalization days was significantly lower in the high probability group (median: 0; third quartile: 4 days) compared to the intermediate group (median: 17; third quartiles: 9 and 21 days; p<0.001), while surgery rates showed no significant differences across groups (p=0.5). Conclusion The CDST partially identifies CD patients likely to benefit from VDZ. Patients with low CDST scores demonstrate poorer persistence, particularly in the subgroup with L1/L2 localization, and a higher risk of hospitalization, while those with high CDST scores exhibit better treatment persistence. These findings further validate the CDST as a valuable tool for guiding treatment decisions in CD. References 1.Dulai PS, Boland BS, Singh S, et al. Development and Validation of a Scoring System to Predict Outcomes of Vedolizumab Treatment in Patients With Crohn's Disease. Gastroenterology. 2018;155(3):687-695.e10.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".