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Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis

IMPORTANCE: Tumor necrosis factor inhibitors (TNFis) have revolutionized the management of ankylosing spondylitis (AS); however, the lack of notable clinical responses in approximately one-half of patients suggests important heterogeneity in treatment response. Identifying patients likely to respond...

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Autores principales: Wang, Runsheng, Dasgupta, Abhijit, Ward, Michael M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924712/
https://www.ncbi.nlm.nih.gov/pubmed/35289857
http://dx.doi.org/10.1001/jamanetworkopen.2022.2312
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author Wang, Runsheng
Dasgupta, Abhijit
Ward, Michael M.
author_facet Wang, Runsheng
Dasgupta, Abhijit
Ward, Michael M.
author_sort Wang, Runsheng
collection PubMed
description IMPORTANCE: Tumor necrosis factor inhibitors (TNFis) have revolutionized the management of ankylosing spondylitis (AS); however, the lack of notable clinical responses in approximately one-half of patients suggests important heterogeneity in treatment response. Identifying patients likely to respond or not respond to TNFis could provide opportunities to personalize treatment strategies. OBJECTIVE: To develop models of the probability of short-term response to TNFi treatment in individual patients with active AS. DESIGN, SETTING, AND PARTICIPANTS: This is a retrospective cohort study using data of the TNFi group (ie, treatment group) from 10 randomized clinical trials (RCTs) of TNFi treatment among patients with active AS, conducted from 2002 to 2016. Participants were adult patients with active AS who failed nonsteroidal anti-inflammatory drugs. Included RCTs were phase 3 and 4 studies that assessed the efficacy of an originator TNFi at week 12 and/or week 24, either compared with placebo or an antirheumatic drug. The cohort was divided into a training and a testing set. Data analysis was conducted from July 1, 2019, to November 30, 2020. EXPOSURES: All included patients received an originator TNFi for at least 12 weeks. MAIN OUTCOMES AND MEASURES: Outcomes included major response and no response based on the change of AS Disease Activity Score at 12 weeks. Machine learning algorithms were applied to estimate the probability of having major response and no response for individual patients. RESULTS: The study included 1899 participants from 10 trials. The training set included 1207 individuals (mean [SD] age, 39 [12] years; 908 [75.2%] men), of whom 407 (33.7%) had major response and 414 (34.3%) had no response. In the reduced logistic regression models, accuracy was 0.74 for major response and 0.75 for no response. The probability of major response increased with higher C-reactive protein (CRP) level, patient global assessment (PGA), and Bath AS Disease Activity Index (BASDAI) question 2 score and decreased with higher body mass index (BMI) and Bath AS Functional Index (BASFI) score. The probability of no response increased with age and BASFI score, and decreased with higher CRP level, BASDAI question 2 score, and PGA. In the testing set (692 participants; mean [SD] age, 38 [11] years; 533 [77.0%] men), models demonstrated moderate to high accuracy. CONCLUSIONS AND RELEVANCE: In this cohort study, the probability of initial response to TNFi was predicted from baseline variables, which may facilitate personalized treatment decision-making.
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spelling pubmed-89247122022-03-30 Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis Wang, Runsheng Dasgupta, Abhijit Ward, Michael M. JAMA Netw Open Original Investigation IMPORTANCE: Tumor necrosis factor inhibitors (TNFis) have revolutionized the management of ankylosing spondylitis (AS); however, the lack of notable clinical responses in approximately one-half of patients suggests important heterogeneity in treatment response. Identifying patients likely to respond or not respond to TNFis could provide opportunities to personalize treatment strategies. OBJECTIVE: To develop models of the probability of short-term response to TNFi treatment in individual patients with active AS. DESIGN, SETTING, AND PARTICIPANTS: This is a retrospective cohort study using data of the TNFi group (ie, treatment group) from 10 randomized clinical trials (RCTs) of TNFi treatment among patients with active AS, conducted from 2002 to 2016. Participants were adult patients with active AS who failed nonsteroidal anti-inflammatory drugs. Included RCTs were phase 3 and 4 studies that assessed the efficacy of an originator TNFi at week 12 and/or week 24, either compared with placebo or an antirheumatic drug. The cohort was divided into a training and a testing set. Data analysis was conducted from July 1, 2019, to November 30, 2020. EXPOSURES: All included patients received an originator TNFi for at least 12 weeks. MAIN OUTCOMES AND MEASURES: Outcomes included major response and no response based on the change of AS Disease Activity Score at 12 weeks. Machine learning algorithms were applied to estimate the probability of having major response and no response for individual patients. RESULTS: The study included 1899 participants from 10 trials. The training set included 1207 individuals (mean [SD] age, 39 [12] years; 908 [75.2%] men), of whom 407 (33.7%) had major response and 414 (34.3%) had no response. In the reduced logistic regression models, accuracy was 0.74 for major response and 0.75 for no response. The probability of major response increased with higher C-reactive protein (CRP) level, patient global assessment (PGA), and Bath AS Disease Activity Index (BASDAI) question 2 score and decreased with higher body mass index (BMI) and Bath AS Functional Index (BASFI) score. The probability of no response increased with age and BASFI score, and decreased with higher CRP level, BASDAI question 2 score, and PGA. In the testing set (692 participants; mean [SD] age, 38 [11] years; 533 [77.0%] men), models demonstrated moderate to high accuracy. CONCLUSIONS AND RELEVANCE: In this cohort study, the probability of initial response to TNFi was predicted from baseline variables, which may facilitate personalized treatment decision-making. American Medical Association 2022-03-15 /pmc/articles/PMC8924712/ /pubmed/35289857 http://dx.doi.org/10.1001/jamanetworkopen.2022.2312 Text en Copyright 2022 Wang R et al. JAMA Network Open. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the CC-BY License.
spellingShingle Original Investigation
Wang, Runsheng
Dasgupta, Abhijit
Ward, Michael M.
Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis
title Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis
title_full Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis
title_fullStr Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis
title_full_unstemmed Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis
title_short Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis
title_sort predicting probability of response to tumor necrosis factor inhibitors for individual patients with ankylosing spondylitis
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924712/
https://www.ncbi.nlm.nih.gov/pubmed/35289857
http://dx.doi.org/10.1001/jamanetworkopen.2022.2312
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