P497 Malnutrition in inflammatory bowel disease: a correlation between clinical and nutritional parameters
Bibliographic record
Abstract
Abstract Background In recent years there has been a growing focus on malnutrition affecting patients with inflammatory bowel disease (IBD) due to its consequences, including increased risk of complications, reduced quality of life and response to treatment. Several screening tools are available to assess malnutrition, but none are specific to IBD. We assessed the accuracy of MUST and Saskatchewan IBD-Nutrition Risk tool (SaskIBD-NR) compared to malnutrition according to ESPEN criteria and the association between the mediterranean diet, clinical, biochemical, and anthropometric parameters and malnutrition. Methods This cross-sectional study was conducted at the tertiary IBD centres of Rho Hospital and Policlinico of Palermo between October 2022 and July 2023. Consecutive outpatients with an established diagnosis of IBD participated at the study. All patients completed the MUST and SaskIBD-NR tool. IBD type, age, smoking habit, disease features (location, behaviour,), steroid therapy, previous surgery, clinical and endoscopic disease activity and anthropometric data (BMI, waist and calf circumference, handgrip strength) were analysed. Mediterranean diet score was also administered to assess adherence to the Mediterranean diet. Results The study sample consisted of 158 IBD patients, 96/168 (60.7%) with Crohn’s disease and 62/168 (42.28%) with ulcerative colitis (UC) were enrolled. Median age was 46.5 (32-58). The handgrip strength mean values were 31.96±10.73 Kg and the calf circumference median values were 34 cm (32-37). The prevalence of malnutrition was 13.3 %; 16/96 (16.7%) were affected by CD and 5/62 (8.1%) by UC. A moderate correlation was found between the MUST and the SaskIBD-NR tool (P<0.0001, r:0.581, Spearman). MUST have good performance in detecting malnutrition in terms of sensitivity (89%) and specificity (90%) and large area under the ROC curve (AUC 0.924, p<0.001). SaskIBD-NR had lower accuracy (AUC 0.731;p=0.0002) and sensibility (695) but high NPV (94%). Calf circumferences had a strong accuracy in predicting malnutrition with AUC of 0.887 (p<0.0001) and sensibility of 84%. At the multivariate logistic regression, waist circumference (OR 0,69 95% CI: 0,54-0,89; p=0,004) and albumin (OR 0,03, 95% CI 0,01-0,85; p=0.04) were independently associated with malnutrition. Conclusion Malnutrition is frequent in IBD patients. SaskIBD NR tool is less sensitive than MUST but could be useful to exclude non-malnourished patients. Anthropometric parameters such as calf and waist circumference and bioumoral parameters such as albumin should be assessed because they can predict malnutrition. Further studies are needed to validate this finding.
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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) | 0.000 | 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 | 0.001 |
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".