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Enregistrement W3005971833 · doi:10.1016/j.cgh.2020.02.010

Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis

2020· article· en· W3005971833 sur OpenAlexafffund
Parambir S. Dulai, Siddharth Singh, Niels Vande Casteele, Joseph Meserve, Adam C. Winters, Shreya Chablaney, Satimai Aniwan, Preeti Shashi, Gursimran Kochhar, Aaron Weiss, Jenna L. Koliani‐Pace, Youran Gao, Brigid S. Boland, John T. Chang, David M. Faleck, Robert Hirten, Ryan C. Ungaro, Dana J. Lukin, Keith Sultan, David Hudesman, Matthew Bohm, Sashidhar Varma, Monika Fischer, Eugenia Shmidt, Arun Swaminath, Nitin Gupta, Maria Rosario, Vipul Jairath, Leonardo Guizzetti, Brian G. Feagan, Corey A. Siegel, Bo Shen, Sunanda V. Kane, Edward V. Loftus, William J. Sandborn, Bruce E. Sands, Jean‐Frédéric Colombel, Karen Lasch, Charlie Cao

Notice bibliographique

RevueClinical Gastroenterology and Hepatology · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueInflammatory Bowel Disease
Établissements canadiensRobarts Clinical TrialsWestern University
Organismes subventionnairesJanssen PharmaceuticalsFerring Research InstituteUniversity of California, San DiegoGenentechAgency for Healthcare Research and QualityShireNational Institute of Diabetes and Digestive and Kidney DiseasesMedImmuneKyowa Hakko KirinArrien PharmaceuticalsWestern UniversityCovis PharmaEli and Edythe Broad FoundationCrohn's and Colitis FoundationSchool of Public Health, University of California BerkeleyNovo NordiskVertex PharmaceuticalsAmerican College of GastroenterologyF. Hoffmann-La RocheIronwood Pharmaceuticals, IncorporatedPrometheusTakeda Pharmaceuticals U.S.A.AbbVieJohnson and JohnsonTeva Pharmaceutical IndustriesRocheGlaxoSmithKlineMerckSeres TherapeuticsAstraZenecaEli Lilly and CompanySamsungBristol-Myers SquibbBiogenPfizerActoGeniXBoehringer IngelheimAmgenGilead SciencesCelgene
Mots-clésVedolizumabMedicineUlcerative colitisLogistic regressionCohortInternal medicineReceiver operating characteristicCohort studyGastroenterologyDisease

Résumé

récupéré en direct d'OpenAlex

Background & AimsWe created and validated a clinical decision support tool (CDST) to predict outcomes of vedolizumab therapy for ulcerative colitis (UC).MethodsWe performed logistic regression analyses of data from the GEMINI 1 trial, from 620 patients with UC who received vedolizumab induction and maintenance therapy (derivation cohort), to identify factors associated with corticosteroid-free remission (full Mayo score of 2 or less, no subscore above 1). We used these factors to develop a model to predict outcomes of treatment, which we called the vedolizumab CDST. We evaluated the correlation between exposure and efficacy. We validated the CDST in using data from 199 patients treated with vedolizumab in routine practice in the United States from May 2014 through December 2017.ResultsAbsence of exposure to a tumor necrosis factor (TNF) antagonist (+3 points), disease duration of 2 y or more (+3 points), baseline endoscopic activity (moderate vs severe) (+2 points), and baseline albumin concentration (+0.65 points per 1 g/L) were independently associated with corticosteroid-free remission during vedolizumab therapy. Patients in the derivation and validation cohorts were assigned to groups of low (CDST score, 26 points or less), intermediate (CDST score, 27–32 points), or high (CDST score, 33 points or more) probability of vedolizumab response. We observed a statistically significant linear relationship between probability group and efficacy (area under the receiver operating characteristic curve, 0.65), as well as drug exposure (P < .001) in the derivation cohort. In the validation cohort, a cutoff value of 26 points identified patients who did not respond to vedolizumab with high sensitivity (93%); only the low and intermediate probability groups benefited from reducing intervals of vedolizumab administration due to lack of response (P = .02). The vedolizumab CDST did not identify patients with corticosteroid-free remission during TNF antagonist therapy.ConclusionsWe used data from a trial of patients with UC to develop a scoring system, called the CDST, which identified patients most likely to enter corticosteroid-free remission during vedolizumab therapy, but not anti-TNF therapy. We validated the vedolizumab CDST in a separate cohort of patients in clinical practice. The CDST identified patients most likely to benefited from reducing intervals of vedolizumab administration due to lack of initial response. ClinicalTrials.gov no: NCT00783718 We created and validated a clinical decision support tool (CDST) to predict outcomes of vedolizumab therapy for ulcerative colitis (UC). We performed logistic regression analyses of data from the GEMINI 1 trial, from 620 patients with UC who received vedolizumab induction and maintenance therapy (derivation cohort), to identify factors associated with corticosteroid-free remission (full Mayo score of 2 or less, no subscore above 1). We used these factors to develop a model to predict outcomes of treatment, which we called the vedolizumab CDST. We evaluated the correlation between exposure and efficacy. We validated the CDST in using data from 199 patients treated with vedolizumab in routine practice in the United States from May 2014 through December 2017. Absence of exposure to a tumor necrosis factor (TNF) antagonist (+3 points), disease duration of 2 y or more (+3 points), baseline endoscopic activity (moderate vs severe) (+2 points), and baseline albumin concentration (+0.65 points per 1 g/L) were independently associated with corticosteroid-free remission during vedolizumab therapy. Patients in the derivation and validation cohorts were assigned to groups of low (CDST score, 26 points or less), intermediate (CDST score, 27–32 points), or high (CDST score, 33 points or more) probability of vedolizumab response. We observed a statistically significant linear relationship between probability group and efficacy (area under the receiver operating characteristic curve, 0.65), as well as drug exposure (P < .001) in the derivation cohort. In the validation cohort, a cutoff value of 26 points identified patients who did not respond to vedolizumab with high sensitivity (93%); only the low and intermediate probability groups benefited from reducing intervals of vedolizumab administration due to lack of response (P = .02). The vedolizumab CDST did not identify patients with corticosteroid-free remission during TNF antagonist therapy. We used data from a trial of patients with UC to develop a scoring system, called the CDST, which identified patients most likely to enter corticosteroid-free remission during vedolizumab therapy, but not anti-TNF therapy. We validated the vedolizumab CDST in a separate cohort of patients in clinical practice. The CDST identified patients most likely to benefited from reducing intervals of vedolizumab administration due to lack of initial response. ClinicalTrials.gov no: NCT00783718

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,430

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,303
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations79
Publié2020
Routes d'admission2
Résumé présentoui

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