P388 Prediction model to safely CEASE anti-TNF therapy in Crohn’s disease: validation of a predictive diagnostic tool for the cessation of anti-TNF treatment in CD in a Dutch population
Bibliographic record
Abstract
Abstract Background Tools for patient identification to safely cease anti-TNF therapy in Crohn’s disease (CD) patients are urgently needed. After an individual participant data meta-analysis (IPD-MA) a predictive diagnostic tool has been developed for cessation of anti-TNF therapy in CD. This study aims to validate this tool. Methods A retrospective study was conducted, in 16 Dutch Hospitals, of CD patients in whom anti-TNF therapy was ceased. Inclusion criteria were anti-TNF therapy use >6 months, start of anti-TNF therapy due to luminal CD and remission as an indication for cessation. Collected baseline demographic, clinical, biochemical, treatment and imaging data were included; age, gender, smoking, Montreal classification, disease- and remission duration, history of surgery, type of anti-TNF medication, previous or concomitant immunosuppressant, thiopurines level, previous anti-TNF therapy, anti-TNF therapy duration, haemoglobin, leukocytes, thrombocytes, albumin, C-reactive protein, anti-TNF serum concentration, anti-infliximab/adalimumab antibodies, remission at MRI/endoscopy, additional stop reason other than remission. The primary outcome was documented relapse of CD that necessitated (re)introduction of biologicals, corticosteroids or immune-suppressants or surgery. Results A total of 523 CD patients (333 females (63%), median age 40 years (IQR 32 – 53)) were included. 293 (56%) patients experienced a relapse after anti-TNF cessation after a median follow-up of 30.2 months (IQR 15–51). The relapse rate was 33% (95% CI 31–34) and 53% (95% CI 52–53) after 1 and 2 years, respectively. The discriminative ability of the prediction model in this external validation cohort (Table 1) equalled that of a previous IPD-MA with a C-statistic of 0.59. An update of the model with faecal calprotectin resulted in a C-statistic of 0.60 [0.55–0.63] and a reported calibration slope of 0.69. Conclusion A previously developed predictive diagnostic tool to safely cease anti-TNF therapy in CD has been validated, however, showed moderate performance in this external cohort. A further update of the model with biochemical and histological data is necessary to improve our ability to adequately select patients for cessation of anti-TNF therapy and is currently being performed.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".