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The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease

BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgi...

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Autores principales: Stidham, Ryan W, Liu, Yumu, Enchakalody, Binu, Van, Tony, Krishnamurthy, Venkataramu, Su, Grace L, Zhu, Ji, Waljee, Akbar K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314116/
https://www.ncbi.nlm.nih.gov/pubmed/33769477
http://dx.doi.org/10.1093/ibd/izab035
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author Stidham, Ryan W
Liu, Yumu
Enchakalody, Binu
Van, Tony
Krishnamurthy, Venkataramu
Su, Grace L
Zhu, Ji
Waljee, Akbar K
author_facet Stidham, Ryan W
Liu, Yumu
Enchakalody, Binu
Van, Tony
Krishnamurthy, Venkataramu
Su, Grace L
Zhu, Ji
Waljee, Akbar K
author_sort Stidham, Ryan W
collection PubMed
description BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD. METHODS: Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation. RESULTS: We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery. CONCLUSIONS: Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery.
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spelling pubmed-83141162021-07-27 The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease Stidham, Ryan W Liu, Yumu Enchakalody, Binu Van, Tony Krishnamurthy, Venkataramu Su, Grace L Zhu, Ji Waljee, Akbar K Inflamm Bowel Dis Original Research Articles - Basic Science BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD. METHODS: Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation. RESULTS: We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery. CONCLUSIONS: Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery. Oxford University Press 2021-03-26 /pmc/articles/PMC8314116/ /pubmed/33769477 http://dx.doi.org/10.1093/ibd/izab035 Text en © 2021 Crohn’s & Colitis Foundation. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Research Articles - Basic Science
Stidham, Ryan W
Liu, Yumu
Enchakalody, Binu
Van, Tony
Krishnamurthy, Venkataramu
Su, Grace L
Zhu, Ji
Waljee, Akbar K
The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease
title The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease
title_full The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease
title_fullStr The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease
title_full_unstemmed The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease
title_short The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease
title_sort use of readily available longitudinal data to predict the likelihood of surgery in crohn disease
topic Original Research Articles - Basic Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314116/
https://www.ncbi.nlm.nih.gov/pubmed/33769477
http://dx.doi.org/10.1093/ibd/izab035
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