A SIMPLE ULTRASOUND SCORE FOR THE ACCURATE DETECTION AND MONITORING OF PEDIATRIC INFLAMMATORY BOWEL DISEASE
Notice bibliographique
Résumé
BACKGROUND: Inflammatory bowel disease (IBD) can lead to long-term, irreversible complications and morbidity in adulthood. Cross-sectional imaging is essential to early diagnosis and optimal disease management. As such, there is a need for a safe and accessible imaging modality for monitoring pediatric IBD. The gold standard, endoscopy, requires general anesthesia in children. Magnetic resonance imaging provides excellent visualization, but is expensive and availability is limited. Alternatively, computed tomography (CT) is associated with radiation risk and is not recommended for repeated use. Ultrasound is accurate in the detection of disease activity, and our team has previously developed a simple score for inflammatory activity in adults based on a retrospective population with prospective score validation. OBJECTIVES: The aim of this study was to establish the most significant parameters in predicting severity of inflammatory disease activity in a retrospective population and develop a simple transabominal ultrasound score for further validation in the pediatric population. DESIGN/METHODS: 86 children were retrospectively included from an established database of children with IBD, and cross-referenced with Picture Archiving and Communication (PACs) imaging database. Only patients that had endoscopy and sonography within 60 days were included for comparison. Ultrasound parameters included: bowel wall thickness, mesenteric fat, hyperemia and lymphadenopathy. The weighted kappa statistic was calculated to assess agreement between sonographic and endoscopic findings. Using a proportional odds model and ordinal logistic regression, 4 statistically significant (p<0.05) parameters predicting disease activity were identified in the retrospective cohort and used to generate a grey-scale ultrasound (US) score that was then compared to gold standard endoscopy. Variables with significance were weighted to classify individuals into different severity classes (normal, mild, moderate and severe). Receiver operating characteristic curves (ROC) were plotted to demonstrate the discriminative and predictive capacity of the score. RESULTS: There was moderate agreement in disease severity between sonographic and endoscopic findings for all disease locations, including: ileocolonic, colonic and sigmoid disease (weight kappa=0.59) and substantial agreement in disease severity between imaging modalities for ileocolonic disease (weight kappa=0.72). Significant clinical predictors of pediatric IBD disease severity were bowel wall thickness and hyperemia (p<0.05). The AUC was 86.3% for normal vs mild and active disease and 76.8% for normal and mild vs active disease, indicating a good performance of the developed severity score. An ultrasound score of >=7 provided the best result in terms of combined sensitivity (74.32%) and specificity (100%) with regard to accurately predicting disease severity. CONCLUSION: Bowel wall thickness and hyperemia are the transabominal ultrasound parameters that best predict disease severity in children with IBD. These parameters can be combined into an accurate simple predictive score, effective in the detection of inflammatory activity in children with IBD.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».