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

Abstract

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.

The association between violence against women and chronic pain: a systematic review and meta-analysis

“…Additionally, some studies have investigated the relationship between post-traumatic stress disorder (PTSD) and physical health consequences [33,34]. There are also longitudinal studies that track the health of abused women over time [35][36][37]. Most of these studies show a positive association between any kind of violence and all chronic conditions, with a significant difference between the group of abused women and the group of not-abused women [38][39][40][41][42][43][44].…”

The potential role of social and familial networks in shaping the well‐being of children in shelters for women survivors of intimate partner violence

“…Intimate partner violence (IPV) is a widespread social and public issue, cutting across ethnonational and religious groups, socio‐economic status and age groups (Both et al, 2019; Lannon et al, 2021), and has negative consequences for both women's and children's well‐being (Graham‐Bermann et al, 2015; S. E. McDonald et al, 2019). Almost one third of women in intimate relationships have experienced emotional, physical and/or sexual abuse from their intimate partner worldwide (World Health Organization [WHO], 2020).…”

“…Intimate partner violence (IPV) is a widespread social and public issue, cutting across ethnonational and religious groups, socio-economic status and age groups (Both et al, 2019;Lannon et al, 2021), and has negative consequences for both women's and children's well-being…”

“…The limitations of this study are discussed, along with implications for future research, as well as highlights for future intervention. K E Y W O R D S children exposed to IPV, children in shelters, children's networks, intimate partner violence, social and familial networks, women survivors of IPV 1 | INTRODUCTIONIntimate partner violence (IPV) is a widespread social and public issue, cutting across ethnonational and religious groups, socio-economic status and age groups (Both et al, 2019;Lannon et al, 2021), and has negative consequences for both women's and children's well-being…”

Desenvolvimento de modelos preditores de resposta clínica em doentes com dor crônica com técnicas de inteligência artificial

“…The development and popularization of machine learning techniques and the improvement of diagnostic instruments based on these technologies have revolutionized decision-making by health professionals (13). However, although many studies have relied on ML to diagnose chronic pain, little has been attributed to its treatment (150)(151)(152).…”