DOP60 Simplified rules to identify bio-naïve patients with Crohn’s Disease with higher likelihood of clinical remission when initiating vedolizumab versus anti-TNFα therapies: Analysis of EVOLVE study data
Notice bibliographique
Résumé
Abstract Background Previously, we identified subsets of biologic-naïve patients (pts) with Crohn’s disease (CD) from the EVOLVE study who had higher rates of clinical remission (CR) [Fig 1] when initiating vedolizumab (VDZ) vs anti-TNFα treatment, using prediction models based on multiple baseline characteristics.1,2 To aid use in practice, we investigated whether these subsets could be identified using simpler rules based on fewer baseline characteristics. Methods Using data from EVOLVE, we used recursive partitioning and regression tree (RPART) classification to predict membership in the previously identified higher CR subsets for VDZ. The RPART algorithm repeatedly splits data, based on baseline predictors (demographics, prior treatments, clinical characteristics at treatment initiation, Charlson comorbidity index, prior extraintestinal manifestations [EIMs], and prior healthcare resource use). At each split, the predictor and its value as chosen by the algorithm to split the data were those that maximized the number of pts classified correctly. Simplified rules were developed from the resulting RPART decision trees. Analyses of the treatment effect of VDZ vs anti-TNFα were conducted in the subsets of pts identified by each rule. Results Pts with data on CR and candidate predictors were included (VDZ [n=195]; anti-TNFα [n=245]). Three simplified rules (A, B, & C) were identified (Table 1). Pt characteristics included in the rules (exacerbation ongoing at treatment initiation, no emergency department/emergency room (ED/ER) visits prior to treatment initiation, no fistulae at most recent assessment prior to treatment initiation, pre-initiation disease behaviour) were among the main predictors of CR in VDZ pts identified previously. Pts identified by Rule A comprised 32% of the EVOLVE population, and were those who 1) had an exacerbation ongoing at index, 2) did not have ED/ER visits prior to initiation and 3) had pre-initiation disease behaviour classified as other than stricturing with/without perianal disease. Among these pts, median time to CR for VDZ and anti-TNFα pts were 6.7 and 18.1 months, respectively (unadjusted log-rank p<0.001), and the adjusted hazard ratio (HR) of CR for VDZ vs anti-TNFα was 2.9 (95% CI: 1.7, 5.0). Rules B & C identified larger subsets in which VDZ vs anti-TNFα treatment differences were smaller but still statistically significant. Conclusion Simple rules were developed to identify biologic-naïve, CD pts in whom VDZ initiation appeared to have a larger effect on CR relative to anti-TNFα initiation. Validation of these rules in other data sources is important to confirm these findings; if validated, these simplified rules can inform targeting of treatment and optimization of outcomes for pts with CD treated with VDZ.
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 | 0,011 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».