A Non-Invasive Serological Test to Assess the Efficacy of Biologic and Non-Biologic Therapies on the Mucosal Health of Patients With Crohnʼs Disease
Bibliographic record
Abstract
Introduction: Mucosal healing (MH) is a desired treatment endpoint in Crohn's Disease (CD). A non-invasive and accurate serological test for the assessment of MH, regardless of treatment, would help to improve this target in routine practice. Recently, we developed a serological, multi-marker, algorithm-based, diagnostic test that is able to assess endoscopic appearance of MH with good accuracy. In this study, we aim to validate the test performance in a therapy agnostic cohort of CD patients treated with either biologic or non-biologic therapeutic options. Methods: This is a multi-center cross-sectional cohort study of CD patients (n=278) for the validation of a serum test that includes 13 protein biomarkers representing various biologic pathways involved in maintaining intestinal health (Ang1, Ang2, CEACAM1, CRP, EMMPRIN, IL7, mmp-1, -2, -3, -9, SAA1, TGFα, and VCAM1). Logistic regression model was used to produce a 0-100 scale termed as the mucosal healing Index (MHI). Endoscopic severity was categorized using the CDEIS, with active endoscopic disease being defined as a CDEIS ≥ 3. One way ANOVA was used to determine mean differences in MHI across endoscopic disease severity categories. p < 0.05 was considered as significant. Results: The median age of patients was 34 years (range: 18-88; males: 43.9%). Approximately 50% of the cohort consisted of patients treated with biologic therapies (adalimumab: 18.3%, infliximab: 15%, anti-integrins: 10.9%, ustekinumab: 6.5%) with the remaining on thiopurines or mesalamine. Therapy information was unavailable for 22/278 patients (8%), which were excluded from the analysis. The overall test accuracy for determining the mucosal severity in this CD patient population was 90% (Table 1). The negative predictive value (NPV) was 89% for identifying patients in remission or with mild endoscopic disease. The positive predictive value (PPV) was 90% for identifying patients with endoscopically active disease (CDEIS>3). Mean MHI values demonstrated positive correlation with increasing endoscopic disease severity (p < 0.0001; Figure 1), and there was no significant change in accuracy when looking at biologic exposed versus non-exposed individuals.Figure: Mean MHI scores and endoscopic disease severity.Table: Table. Overall Serologic Test Performance for Detecting Endoscopically Visualized Mucosal Disease Severity in CD PatientsConclusion: A novel serology test has been developed to assess the mucosal status in patients with CD. The test is able to accurately assess MH in CD patients across several different types of therapeutic classes. This test has the potential to be utilized as a non-invasive tool to monitor and help manage the care of CD patients regardless of therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".