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

Parambir S. Dulai, Siddharth Singh, Niels Vande Casteele, Joseph Meserve, Adam Winters, Shreya Chablaney, Satimai Aniwan, Preeti Shashi, Gursimran Kochhar, Aaron Weiss, Jenna L. Koliani-Pace, Youran Gao, Brigid S. Boland, John T. Chang, David Faleck, Robert Hirten, Ryan Ungaro, Dana Lukin, Keith Sultan, David HudesmanShannon Chang, 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 Kane, Edward V. Loftus, William J. Sandborn, Bruce E. Sands, Jean Frederic Colombel, Karen Lasch, Charlie Cao

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

BACKGROUND & AIMS: We created and validated a clinical decision support tool (CDST) to predict outcomes of vedolizumab therapy for ulcerative colitis (UC).

METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

Original languageEnglish (US)
Pages (from-to)2952-2961.e8
JournalClinical Gastroenterology and Hepatology
Volume18
Issue number13
DOIs
StatePublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 The Authors

Keywords

  • Biologic
  • Personalized Medicine
  • Prognostic Factor
  • Response to Treatment

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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