Development and Validation of a Multi-marker Serum Test for the Assessment of Mucosal Healing in Crohnʼs Disease Patients
Bibliographic record
Abstract
Introduction: Mucosal healing (MH) has become the primary therapeutic target in Crohn's Disease (CD). Ileocolonoscopy, currently the gold standard for assessing MH, is invasive and time-consuming with poor patient acceptance. This limits the practical feasibility for serial monitoring of mucosal disease activity and response to treatment. We developed a serum-based, multi-analyte MH algorithm incorporating a panel of biomarkers associated with biological pathways important for mucosal homeostasis in CD patients. Methods: Retrospective serum samples were taken from adult CD patients at or within 30 days of ileocolonoscopy. A panel of serum proteomics biomarkers, selected from a total of 48 markers based on their correlation with endoscopic activity, were used to train a logistic regression model against visualized endoscopic disease severity determined by either CDEIS or SES-CD scores. The model was independently validated in a prospectively collected, centrally read, longitudinal cohort of 118 patients from the TAILORIX clinical trial. The final model utilized 13 biomarkers to produce a 0-100 scale termed as the Mucosal Healing Index (MHI). The markers represent multiple biological pathways thought to be involved in the MH process including angiogenesis (Ang1, Ang2), cell adhesion (CEACAM1, VCAM1), growth factor signaling (TGF||), inflammation (CRP, SAA1), matrix remodeling (mmp-1, -2, -3, -9 and EMMPRIN), and immune modulation (IL7). Results: A total of 748 samples from 396 patients (mean age: 34 years, 49% males, 26% ileal, 52% ileocolonic and 22% colonic disease) were used for the training and validation of the MH index. The overall accuracy of the test was 90% with a negative predictive value (NPV) of 92% for identifying patients in remission (CDEIS<3) or with mild (CDEIS 3-8) endoscopic disease (MHI range 0-40) and a positive predictive value (PPV) of 87% for identifying patients with endoscopic evidence of active disease (CDEIS ≥3; MHI range 50-100). 14% of the specimens fell within an intermediate zone (MHI 41-49) with an observed 78% probability of active disease. Test performance is shown in Table 1.Table: Table. Mucosal Healing Index Performance for Detecting Mucosal Disease Severity in CD PatientsConclusion: A peripheral blood-based test has been developed that can be used as a non-invasive surrogate for mucosal endoscopic activity assessed with ileocolonoscopy in CD patients. The incorporation of this test into current practice could aid in the management and monitoring of CD patients to help determine therapeutic efficacy in a treat to target paradigm.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".