S1196 Comparing Virtual Chromoendoscopy to Dye-Spraying Chromoendoscopy and White Light Endoscopy in Screening Patients With Inflammatory Bowel Disease: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Introduction: Patients with inflammatory bowel disease have high risk for colon cancer and surveillance colonoscopy is crucial for early detection of dysplasia and neoplasia for this patient population. Virtual chromoendoscopy (VCE) techniques have shown promise in enhancing lesion detection rates. However, the comparative effectiveness of these methods to the traditional dye-spraying chromoendoscopy (CE) and white light endoscopy (WLE) remains unclear. Methods: A comprehensive search was performed in electronic databases, including PubMed, EMBASE, and Web of Science, from inception until November 2022. Studies reporting number of detected lesions or number of patients with colonic lesions were included. Studies that reported tandem colonoscopies were excluded. For studies with crossover design, we included data from the first procedure. Random-effect models were used to estimate pooled risk ratios (RR) and 95% confidence intervals (95% CI). Subgroup analyses were conducted based on the study type (randomized (RCT) vs observational) and based on the VCE technique. R version 4.0.5 (R foundation for statistical computing, Vienna, Austria) was used to conduct the statistical analysis. Results: A total of 4,888 studies were assessed and 16 of them were eligible for our analysis. Of the included studies, two were observational, three had a crossover design, and five were published as abstracts (Table 1). Per patient analysis, VCE improved detecting patients with colonic lesions compared to CE (RR 0.73; 95% CI, 0.59–0.9) and WLE (RR 0.69; 95% CI, 0.54–0.87). However, these findings became statistically insignificant if only RCT were used in the subgroup analysis based on the study type (Figure 1A,C). Per the number of lesion analyses, VCE was not statistically different compared to CE (RR 0.73; 95% CI, 0.50–1.06) or WLE (RR 1.04; 95% CI, 0.65–1.67) (Figure 1B,D). In the subgroup analysis based on the VCE technique, there were no statistical differences between autofluorescence imaging, Fuji intelligent color enhancement, I-scan, or narrow band imaging compared to CE or WLE. Conclusion: The efficacy of virtual chromoendoscopy in detecting colonic lesions for patients with inflammatory bowel disease seems to be higher when compared to dye-spraying chromoendoscopy and white light colonoscopy. However, it is important to note that these findings are primarily based on observational data and do not hold up when considering only randomized trials.Figure 1.: Forest plots comparing virtual chromoendoscopy to dye-spraying chromoendoscopy and white light endoscopy. Table 1. - Characteristics of the included studies Study Year Study type Study design Publication Study setting Sample size Virtual CE Comparator Van Den 2010 RCT Crossover Full Netherlands 48 NBI WLE Feitosa 2011 RCT Parallel Abstract Brazil 29 NBI CE Pellise 2011 RCT Crossover Full Spain 60 NBI CE Ignjatovic 2012 RCT Parallel Full UK 112 NBI WLE Cassinotti 2015 RCT Parallel Abstract Italy 91 FICE WLE Bisschops 2016 RCT Parallel Full Canada 131 NBI CE Gasia 2016 Observational Parallel Full Canada 454 I-scan CE & WLE Watanabe 2016 RCT Parallel Abstract Japan 263 NBI CE Iacucci 2017 RCT Parallel Full Canada 270 I-scan CE & WLE Lopez-Serrano 2017 RCT Parallel Abstract Spain 66 I-scan CE Gulati 2018 RCT Crossover Full UK 48 FICE CE Vleugels 2018 RCT Parallel Full Netherlands & UK 210 AFI CE Kandiah 2021 RCT Parallel Full UK 188 I-scan WLE González-Bernardo 2021 RCT Parallel Full Spain 129 I-scan CE Lopez-Serranoa 2021 Observational Parallel Full Spain 191 I-scan CE Sinonquel 2022 RCT Parallel Abstract Europe (4 countries) 136 I-scan CE
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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 | 0,011 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,017 | 0,027 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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 ».