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Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identif...

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Autores principales: Lannon, Edward, Sanchez-Saez, Francisco, Bailey, Brooklynn, Hellman, Natalie, Kinney, Kerry, Williams, Amber, Nag, Subodh, Kutcher, Matthew E., Goodin, Burel R., Rao, Uma, Morris, Matthew C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320990/
https://www.ncbi.nlm.nih.gov/pubmed/34324550
http://dx.doi.org/10.1371/journal.pone.0255277
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author Lannon, Edward
Sanchez-Saez, Francisco
Bailey, Brooklynn
Hellman, Natalie
Kinney, Kerry
Williams, Amber
Nag, Subodh
Kutcher, Matthew E.
Goodin, Burel R.
Rao, Uma
Morris, Matthew C.
author_facet Lannon, Edward
Sanchez-Saez, Francisco
Bailey, Brooklynn
Hellman, Natalie
Kinney, Kerry
Williams, Amber
Nag, Subodh
Kutcher, Matthew E.
Goodin, Burel R.
Rao, Uma
Morris, Matthew C.
author_sort Lannon, Edward
collection PubMed
description Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.
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spelling pubmed-83209902021-07-31 Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach Lannon, Edward Sanchez-Saez, Francisco Bailey, Brooklynn Hellman, Natalie Kinney, Kerry Williams, Amber Nag, Subodh Kutcher, Matthew E. Goodin, Burel R. Rao, Uma Morris, Matthew C. PLoS One Research Article Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data. Public Library of Science 2021-07-29 /pmc/articles/PMC8320990/ /pubmed/34324550 http://dx.doi.org/10.1371/journal.pone.0255277 Text en © 2021 Lannon et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Lannon, Edward
Sanchez-Saez, Francisco
Bailey, Brooklynn
Hellman, Natalie
Kinney, Kerry
Williams, Amber
Nag, Subodh
Kutcher, Matthew E.
Goodin, Burel R.
Rao, Uma
Morris, Matthew C.
Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
title Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
title_full Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
title_fullStr Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
title_full_unstemmed Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
title_short Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
title_sort predicting pain among female survivors of recent interpersonal violence: a proof-of-concept machine-learning approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320990/
https://www.ncbi.nlm.nih.gov/pubmed/34324550
http://dx.doi.org/10.1371/journal.pone.0255277
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