Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach
Por:
Lannon E, Sanchez-Saez F, Bailey B, Hellman N, Kinney K, Williams A, Nag S, Kutcher ME, Goodin BR, Rao U and Morris MC
Publicada:
29 jul 2021
Ahead of Print:
29 jul 2021
Categoría:
Multidisciplinary
Resumen:
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.
Filiaciones:
Lannon E:
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Department of Psychology, University of Tulsa, Tulsa, Oklahoma, United States of America
Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Stanford, CA, United States of America
:
School of Engineering and Technology, Universidad Internacional de La Rioja, Logroño, Spain
Bailey B:
Department of Psychology, The Ohio State University, Columbus, Ohio, United States of America
Hellman N:
Department of Psychology, University of Tulsa, Tulsa, Oklahoma, United States of America
Kinney K:
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Department of Psychiatry, College of Medicine, University of Illinois at Chicago, Chicago, IL, United States of America
Williams A:
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Nag S:
Department of Neuroscience and Pharmacology, Meharry Medical Center, Tennessee, United States of America
Kutcher ME:
Department of Surgery, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Goodin BR:
Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
Rao U:
Department of Psychiatry & Human Behavior, Department of Pediatrics, and Center for the Neurobiology of Learning and Memory, University of California-Irvine, Irvine, California, United States of America
Children's Hospital of Orange County, Orange, CA, United States of America
Morris MC:
Department of Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
Green Published, gold
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