Week 6 Calprotectin Best Predicts Likelihood of Long-term Endoscopic Healing in Crohn’s Disease: A Post-hoc Analysis of the UNITI/IM-UNITI Trials
Bibliographic record
Abstract
OBJECTIVES: There is need for biomarkers as predictors of outcome of medical treatment in Crohn's disease. The purpose of this study was to evaluate the predictive performance of faecal calprotectin for short- and long-term clinical and endoscopic outcomes. METHODS: This post-hoc analysis of the UNITI/IM-UNITI studies [NCT01369329, NCT01369342, and NCT01369355; YODA #2019-4026] included 677 patients to evaluate the relationship of Week 6 calprotectin cut-offs and changes from baseline assessments in calprotectin for prediction of outcomes at Weeks 8, 32, and 52, using receiver operating characteristic curves with comparisons of areas under the curve [AUC]. The relationship between clinical and biomarker assessments at Week 6 and endoscopic remission [ER] at Week 52 was evaluated using multivariate logistic regression models adjusted for confounders. RESULTS: A Week 6 calprotectin <250 mg/kg demonstrated a significant ability to predict Week 52 ER (AUC 0.709, 95% confidence interval [CI] 0.566-0.852, p = 0.014) with fair accuracy, and performed better than other calprotectin cut-offs and deltas from baseline for prediction of Week 52 ER. When adjusted for covariates, patients with a Week 6 faecal calprotectin <250 mg/kg had 3.48 times [95% CI 1.31-9.28, p = 0.013] increased odds of Week 52 ER. No other Week 6 clinical assessment [clinical remission or clinical response] or biomarker [CRP <5 or drug level] had an association with Week 52 ER. CONCLUSIONS: In summary, the results of this post-hoc analysis suggest that Week 6 calprotectin levels < 250 mg/kg can be predictive of future endoscopic healing and may be more informative than clinical symptom improvement. PODCAST: This article has an associated podcast which can be accessed at https://academic.oup.com/ecco-jcc/pages/podcast.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".