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Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis
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 induc...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899124/ https://www.ncbi.nlm.nih.gov/pubmed/32062041 http://dx.doi.org/10.1016/j.cgh.2020.02.010 |
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author | Dulai, Parambir S. Singh, Siddharth Casteele, Niels Vande Meserve, Joseph Winters, Adam Chablaney, Shreya Aniwan, Satimai Shashi, Preeti Kochhar, Gursimran Weiss, Aaron Koliani-Pace, Jenna L. Gao, Youran Boland, Brigid S. Chang, John T. Faleck, David Hirten, Robert Ungaro, Ryan Lukin, Dana Sultan, Keith Hudesman, David Chang, Shannon Bohm, Matthew Varma, Sashidhar Fischer, Monika Shmidt, Eugenia Swaminath, Arun Gupta, Nitin Rosario, Maria Jairath, Vipul Guizzetti, Leonardo Feagan, Brian G. Siegel, Corey A. Shen, Bo Kane, Sunanda Loftus, Edward V. Sandborn, William J. Sands, Bruce E. Colombel, Jean-Frederic Lasch, Karen Cao, Charlie |
author_facet | Dulai, Parambir S. Singh, Siddharth Casteele, Niels Vande Meserve, Joseph Winters, Adam Chablaney, Shreya Aniwan, Satimai Shashi, Preeti Kochhar, Gursimran Weiss, Aaron Koliani-Pace, Jenna L. Gao, Youran Boland, Brigid S. Chang, John T. Faleck, David Hirten, Robert Ungaro, Ryan Lukin, Dana Sultan, Keith Hudesman, David Chang, Shannon Bohm, Matthew Varma, Sashidhar Fischer, Monika Shmidt, Eugenia Swaminath, Arun Gupta, Nitin Rosario, Maria Jairath, Vipul Guizzetti, Leonardo Feagan, Brian G. Siegel, Corey A. Shen, Bo Kane, Sunanda Loftus, Edward V. Sandborn, William J. Sands, Bruce E. Colombel, Jean-Frederic Lasch, Karen Cao, Charlie |
author_sort | Dulai, Parambir S. |
collection | PubMed |
description | 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 |
format | Online Article Text |
id | pubmed-7899124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-78991242021-02-22 Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis Dulai, Parambir S. Singh, Siddharth Casteele, Niels Vande Meserve, Joseph Winters, Adam Chablaney, Shreya Aniwan, Satimai Shashi, Preeti Kochhar, Gursimran Weiss, Aaron Koliani-Pace, Jenna L. Gao, Youran Boland, Brigid S. Chang, John T. Faleck, David Hirten, Robert Ungaro, Ryan Lukin, Dana Sultan, Keith Hudesman, David Chang, Shannon Bohm, Matthew Varma, Sashidhar Fischer, Monika Shmidt, Eugenia Swaminath, Arun Gupta, Nitin Rosario, Maria Jairath, Vipul Guizzetti, Leonardo Feagan, Brian G. Siegel, Corey A. Shen, Bo Kane, Sunanda Loftus, Edward V. Sandborn, William J. Sands, Bruce E. Colombel, Jean-Frederic Lasch, Karen Cao, Charlie Clin Gastroenterol Hepatol Article 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 2020-02-13 2020-12 /pmc/articles/PMC7899124/ /pubmed/32062041 http://dx.doi.org/10.1016/j.cgh.2020.02.010 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Dulai, Parambir S. Singh, Siddharth Casteele, Niels Vande Meserve, Joseph Winters, Adam Chablaney, Shreya Aniwan, Satimai Shashi, Preeti Kochhar, Gursimran Weiss, Aaron Koliani-Pace, Jenna L. Gao, Youran Boland, Brigid S. Chang, John T. Faleck, David Hirten, Robert Ungaro, Ryan Lukin, Dana Sultan, Keith Hudesman, David Chang, Shannon Bohm, Matthew Varma, Sashidhar Fischer, Monika Shmidt, Eugenia Swaminath, Arun Gupta, Nitin Rosario, Maria Jairath, Vipul Guizzetti, Leonardo Feagan, Brian G. Siegel, Corey A. Shen, Bo Kane, Sunanda Loftus, Edward V. Sandborn, William J. Sands, Bruce E. Colombel, Jean-Frederic Lasch, Karen Cao, Charlie Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis |
title | Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis |
title_full | Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis |
title_fullStr | Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis |
title_full_unstemmed | Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis |
title_short | Development and Validation of Clinical Scoring Tool to Predict Outcomes of Treatment With Vedolizumab in Patients With Ulcerative Colitis |
title_sort | development and validation of clinical scoring tool to predict outcomes of treatment with vedolizumab in patients with ulcerative colitis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7899124/ https://www.ncbi.nlm.nih.gov/pubmed/32062041 http://dx.doi.org/10.1016/j.cgh.2020.02.010 |
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