P350 Conventional biomarkers in newly diagnosed Crohn’s disease patients may predict early disease progression
Bibliographic record
Abstract
Crohn’s disease (CD) is a heterogeneous progressive disorder. Predicting factors for progression are scarce. We aimed to identify early risk factors for disease progression. A longitudinal, prospective, observational inception cohort, in a tertiary referral centre. Adults suspected of CD, or diagnosed with CD during the preceding six months prior to enrolment were recruited. Clinical and biological markers were obtained at recruitment. Disease progression was defined by indirect measures: either CD-related hospitalisation/surgery or commencing any medical therapy other than 5ASAs. For prediction analysis we focused only on treatment-naïve patients at enrolment, using any data obtained before interventions. Data analysis was performed using data mining methods. Biomarkers were analysed for threshold values by classification and regression trees (CART), and by Chi-square automatic interaction detector (CHAID) methods. We implemented the data into a stepwise forward multivariate Cox regression model. Overall 59 of 157 patients attained a diagnosis of treatment naïve CD: median age at diagnosis was 31.6 (IQR 21.5–46) years, males: 35 (59.3%), Ashkenazi: 22 (37.3%), never smokers: 33 (55.9%). Average BMI was 22.7 ± 4.5 kg/m2. Median follow-up: 15.1 (IQR 5.7–20.2) months. At enrolment median C-reactive protein (CRP) was 12.1 (IQR 3.7–20.6) mg/l and median fecal calprotectin (FC) was 436 (IQR 145–799) µg/gr stool. Montreal classification: L1 36 (63.1%); L2 15 (26.4%); L3 6 (10.5%); L4 5 (8.8%); B1, 48 (85.7%); B2 4 (7.1%); B3 4 (7.1%), and perianal involvement in 6 patients (10.7%). Disease progression was identified in 28 patients (47.5%): hospitalisation 9 (15.3%), surgery 1 (1.7%), medical intervention 26 patients (44.1%). The Cox regression model revealed several biomarkers that were associated with disease progression: ferritin > 47 ng/ml (HR 9.6, p = 0.002), GGT > 17 IU (HR 8.06; p = 0.001), ASCA IgA >2.4 IU (HR 3.4; p = 0.039), BMI < 22 kg/m2 (HR 5.7, p = 0.001), while colonic disease was found to be associated with decreased probability for progression (HR 0.4, p = 0.049). CRP and FC were not predictive of early disease progression. In newly diagnosed CD patients conventional biomarkers, even within normal ranges, maybe be used as strong independent predictors for disease progression. Integration of these measures into a model (creation of a nomogram) will be used to stratify newly diagnosed patients for near risk of progression. Such tools may enable better patient stratification and direct clinicians towards strategic personalised interventions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".