P171 A multi-marker serum test predicts mucosal healing status in Crohn’s disease regardless of disease location
Bibliographic record
Abstract
Non-invasive serological tests can be important adjuncts to endoscopy particularly in patients with Crohn’s disease (CD) given its transmural nature and lack of optimal endoscopic accessibility to the small bowel. A newly developed serological test has been shown to be an effective tool for assessing the intestinal mucosal state in CD patients.1 The aim of the present study was to assess the diagnostic performance and clinical utility of this novel test in specific subtypes of CD patients classified by the location of their disease. A 13-biomarker mucosal healing monitoring immunoassay (Prometheus Laboratories Inc.) termed as the Mucosal Healing Index (MHI) was developed and validated on a combined series of 748 serum specimens with matching colonoscopy scores (1). MHI is a scale of 0–100 where 0–40 identifies patients in remission (CDEIS <3) or mild (CDEIS 3–8) endoscopic disease and 50–100 identifies patients with endoscopically active (CDEIS ≥3) disease. Multiple logistic regression models were used in developing the MHI. In the present study, validation of the MHI, according to disease location, was evaluated in 412 longitudinal specimens from 118 CD patients collected during the TAILORIX2 clinical trial. Specimens were collected from patients at the time of or close to 3 serial endoscopies per patient. Endoscopies were centrally read and MH was defined as the absence of ulcers. MHI assay performance was assessed for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) in the combined group and by each disease location according to Montreal classification. Patient characteristics are shown in Table 1. MHI accuracy was 95%, 90%, and 87% for ileal, ileocolonic, and colonic disease, respectively. The detailed performance across disease locations is shown in Table 2. A novel serum test for the non-invasive evaluation of mucosal health shows comparable performance across ileal, ileocolonic and colonic anatomic disease locations in patients with CD. These results further validate the clinical utility of the test as an aid in assessing the state of the intestinal mucosa in CD patients regardless of disease location. References 1. Kelly et al., 2017, P2184, WCOG at ACG2017. 2. D’Haens et al., 2017, OP029 ECCO 2017.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".