P381 Intestinal ultrasound in newly diagnosed Ulcerative Colitis predicts the need for colectomy
Bibliographic record
Abstract
Abstract Background Upon diagnosis of ulcerative colitis (UC), it remains challenging to predict who will have a severe disease course, ultimately necessitating colectomy. Predictive tools capable of risk-stratifying patients and individualize treatment are warranted. The aim of this study was to assess whether Intestinal ultrasound (IUS), performed at the onset of UC, could help predict the need for colectomy within the first year of diagnosis. Methods In a Danish prospective inception cohort, all new-onset adult patients with UC (including unclassified IBD) with left-sided or extensive colitis underwent IUS near the diagnostic endoscopy. The assessment included the International Bowel Ultrasound Segmental Activity Score (IBUS-SAS) and the bowel wall thickness (BWT) of the most inflamed segment. Besides IUS, we recorded age, gender, smoking, BMI, the Mayo endoscopic score (MES), the Simple Clinical Colitis Activity Index (SCCAI), and biomarkers hemoglobin (hgb), C-reactive protein (CRP), albumin and fecal calprotectin. Results During inclusion (May 2021 – April 2023), we included 193 UC patients with Montreal classification E2 or E3. In total, 12/193 (6%) underwent colectomy within the first year or during follow-up (min. 6 months). Baseline characteristics are presented in Table 1. Univariable analyses identified high MES, CRP, SCCAI, IBUS-SAS, and BWT, along with low Hgb and albumin, as significantly associated with an increased colectomy risk. Multivariable analysis with stepwise reduction identified only SCCAI (OR: 2.0, CI 1.2–3.3, p<0.005) and BWT (OR: 1.6, CI 1.1–2.3, p=0.01) as independent predictors. Through ROC analysis, BWT presented the highest accuracy (AUC: 0.89), and the optimal cut-off by Youden Index for colectomy prediction was > 6.1 mm. Combining all the significant non-IUS predictors, CRP, MES, SCCAI, hgb, and albumin yielded an AUC of 0.88. The highest accuracy was found by combining SCCAI and BWT, with an AUC of 0.94. Kaplan-Meier curve for colectomy-free survival is presented in Figure 1. Among patients with BWT>6.1mm, 18/21 (86%) started systemic steroids within the first week, compared to 43/125 (34%) of patients with BWT ≤ 6.1, p=0.007. Among patients with BWT > 6.1mm, 11/21 (52%) started biologics within the first 31 days, compared to 5/125 (4%) of patients with BWT≤6.1mm, p<0.001. Despite more aggressive treatment, 8/21 (38%) with BWT>6.1mm had a colectomy compared to 2/125 (2%) with BWT≤6.1mm.Conclusion IUS at UC onset, specifically BWT, is a robust predictor of colectomy risk within the first year of diagnosis. Combining clinical symptom scores with IUS enhances predictive accuracy. It can accurately identify patients at an increased risk of colectomy, guiding early treatment decisions for improved patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.003 | 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".