DOP55 Histopathological features at diagnosis to predict long-term disease course of Crohn’s Disease
Bibliographic record
Abstract
Abstract Background Crohn’s disease (CD) is characterized by a heterogeneous disease course and treatment response. There is a clinical need to identify CD patients at diagnosis who are at risk for developing a severe disease course. Patient stratification using state-of-the-art clinical, serological or genetic markers does not predict disease course sufficiently to facilitate clinical decision making. The current study aimed to investigate the additive predictive value of histopathological features at diagnosis to discriminate between patients with a long-term mild and severe disease course. Methods Diagnostic biopsies from treatment-naïve CD patients with mild or severe disease courses in the first 10 years after diagnosis (i.e. based on the number of quarterly flares) were reviewed by two senior gastrointestinal pathologists after developing a standardized form comprising 15 histopathological features related to acute and chronic inflammation. Multivariable logistic regression models were built to identify predictive features and compute receiver operating characteristics (ROC) curves. Model 1 included clinically relevant baseline characteristics (Montreal classification, smoking status and gender). Next, histopathological were added by applying two different model-building strategies (forward selection and purposeful selection algorithm)(Model 2). Prediction models were internally validated using bootstrapping to obtain optimism-corrected performance estimates. Results In total, 817 biopsies from 137 CD patients (64 mild disease course, 73 severe disease course) were included. Based on clinical baseline characteristics alone, disease course could only be moderately predicted (Model 1 Area under ROC (AUROC): 0.738 (optimism 0.018), 95%CI 0.65–0.83, sensitivity 83.6%, specificity 53.1%). When adding histopathological features, in colonic, but not ileal, biopsies a combination of (1) basal plasmacytosis, (2) severe lymphocyte and plasma cell infiltration in the lamina propria, (3) Paneth cell metaplasia and (4) absence of ulcers were identified and resulted in significantly better prediction of a severe disease course (Model 2 AUROC: 0.883 (optimism 0.033), 95%CI 0.82–0.94, sensitivity 80.4%, specificity 84.2%, model 2 vs. model 1 AUROC p = 0.001)[Figure 1]. Conclusion In this first study investigating the additive predictive value of multiple histopathological features in biopsies at CD diagnosis, we found that certain features of chronic inflammation in colonic biopsies contributed to prediction of a severe disease course, thereby presenting a novel approach to improve stratification and facilitate clinical decision making.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".