Cargando…
Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease
Skin biomechanical parameters (dynamic stiffness, frequency, relaxation time, creep, and decrement) measured using a myotonometer (MyotonPRO) could inform the management of sclerotic disease. To determine which biomechanical parameter(s) can accurately differentiate patients with sclerotic chronic g...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594905/ https://www.ncbi.nlm.nih.gov/pubmed/34790906 http://dx.doi.org/10.1016/j.xjidi.2021.100037 |
_version_ | 1784600082391236608 |
---|---|
author | Baker, Laura X. Chen, Fuyao Cronin, Austin Chen, Heidi Vain, Arved Jagasia, Madan Tkaczyk, Eric R. |
author_facet | Baker, Laura X. Chen, Fuyao Cronin, Austin Chen, Heidi Vain, Arved Jagasia, Madan Tkaczyk, Eric R. |
author_sort | Baker, Laura X. |
collection | PubMed |
description | Skin biomechanical parameters (dynamic stiffness, frequency, relaxation time, creep, and decrement) measured using a myotonometer (MyotonPRO) could inform the management of sclerotic disease. To determine which biomechanical parameter(s) can accurately differentiate patients with sclerotic chronic graft-versus-host disease from post–hematopoietic cell transplant controls, 15 patients with sclerotic chronic graft-versus-host disease and 11 post–hematopoietic cell transplant controls were measured with the myotonometer on 18 anatomic sites. Logistic regression and two machine learning algorithms (least absolute shrinkage and selection operator regression and random forest) were developed to classify subjects. In univariable analysis, frequency had the highest overfit-corrected area under the curve (0.91). Backward stepwise selection and random forest machine learning identified frequency and relaxation time as the optimal parameters for differentiating patients with sclerotic chronic graft-versus-host disease from post–hematopoietic cell transplant controls. Least absolute shrinkage and selection operator regression selected the combination of frequency and relaxation time (overfit-corrected area under the curve = 0.87). Discriminatory ability was maintained when only the sites accessible while the patient is supine (12 sites) were used. We report the distribution of values for these highly discriminative biomechanical parameters, which could inform the assessment of disease severity in future quantitative biomechanical studies of sclerotic chronic graft-versus-host disease. |
format | Online Article Text |
id | pubmed-8594905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-85949052021-11-16 Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease Baker, Laura X. Chen, Fuyao Cronin, Austin Chen, Heidi Vain, Arved Jagasia, Madan Tkaczyk, Eric R. JID Innov Original Article Skin biomechanical parameters (dynamic stiffness, frequency, relaxation time, creep, and decrement) measured using a myotonometer (MyotonPRO) could inform the management of sclerotic disease. To determine which biomechanical parameter(s) can accurately differentiate patients with sclerotic chronic graft-versus-host disease from post–hematopoietic cell transplant controls, 15 patients with sclerotic chronic graft-versus-host disease and 11 post–hematopoietic cell transplant controls were measured with the myotonometer on 18 anatomic sites. Logistic regression and two machine learning algorithms (least absolute shrinkage and selection operator regression and random forest) were developed to classify subjects. In univariable analysis, frequency had the highest overfit-corrected area under the curve (0.91). Backward stepwise selection and random forest machine learning identified frequency and relaxation time as the optimal parameters for differentiating patients with sclerotic chronic graft-versus-host disease from post–hematopoietic cell transplant controls. Least absolute shrinkage and selection operator regression selected the combination of frequency and relaxation time (overfit-corrected area under the curve = 0.87). Discriminatory ability was maintained when only the sites accessible while the patient is supine (12 sites) were used. We report the distribution of values for these highly discriminative biomechanical parameters, which could inform the assessment of disease severity in future quantitative biomechanical studies of sclerotic chronic graft-versus-host disease. Elsevier 2021-06-24 /pmc/articles/PMC8594905/ /pubmed/34790906 http://dx.doi.org/10.1016/j.xjidi.2021.100037 Text en © 2021 The Authors. Published by Elsevier, Inc. on behalf of the Society for Investigative Dermatology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Article Baker, Laura X. Chen, Fuyao Cronin, Austin Chen, Heidi Vain, Arved Jagasia, Madan Tkaczyk, Eric R. Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease |
title | Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease |
title_full | Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease |
title_fullStr | Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease |
title_full_unstemmed | Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease |
title_short | Optimal Biomechanical Parameters for Measuring Sclerotic Chronic Graft-Versus-Host Disease |
title_sort | optimal biomechanical parameters for measuring sclerotic chronic graft-versus-host disease |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594905/ https://www.ncbi.nlm.nih.gov/pubmed/34790906 http://dx.doi.org/10.1016/j.xjidi.2021.100037 |
work_keys_str_mv | AT bakerlaurax optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease AT chenfuyao optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease AT croninaustin optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease AT chenheidi optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease AT vainarved optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease AT jagasiamadan optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease AT tkaczykericr optimalbiomechanicalparametersformeasuringscleroticchronicgraftversushostdisease |