IDDF2020-ABS-0147 Development of a validated nomogram to predict aggressive Crohn’s disease: a retrospective cohort study
Bibliographic record
Abstract
Background Predicting aggressive Crohn’s disease (CD) is crucial for determining therapeutic strategies. We aimed to develop a prognostic model to predict disease-related complications leading to early-onset surgery within 1 year after diagnosis of CD and to create a nomogram to facilitate clinical decision-making. Methods This retrospective study was conducted from January 1, 2012, to December 31, 2016, in a single tertiary referral center, using data from patients newly diagnosed with CD and showing B1 behavior according to Montreal classification. The model was established using multivariable logistic regression analysis with evaluation of the receiver operating characteristic (ROC) curves and areas under the curve (AUC). The model was calibrated and assessed for discrimination. Further, a user-friendly nomogram was created. Results The mean follow-up period was 53.45±12.81 months. Of 614 eligible patients, 13.5% developed surgery-related complications, including stenosis, perforation, and severe gastrointestinal bleeding. We identified age (Odds ratio (OR) 0.914, P=0.004), disease duration (OR 2.675, P<0.001), perianal disease (OR 16.013, P<0.001), previous surgery (OR 3.652, P=0.003), and extraintestinal manifestations (OR 7.625, P=0.001) as significant independent factors associated with early-onset complications and developed a prognostic model ((figure 1A), A Prognostic model predicting complications leading to surgery within 1 year after diagnosis), whose predictive ability was appraised with AUC of 0.965, specificity of 96.71%, and sensitivity of 67.24%. This model was validated with good discrimination (AUC of 0.933), and excellent calibration was demonstrated using the Hosmer-Lemeshow goodness-of-fit test ((figure 1B), Hosmer-Lemeshow goodness-of-fit test demonstrating a good fit of this model). A nomogram was created to facilitate clinical bedside practice ((figure 1C) A nomogram predicting complications leading to surgery within 1 year after diagnosis in Crohn’s disease patients). Conclusions This validated prognostic model can effectively predict early-onset complications leading to surgery and screen aggressive CD, enabling physicians to customize therapeutic strategies and monitor the intensive disease.
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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 | 0.005 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".