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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.

SUBMITTER: Lannon E

PROVIDER: S-EPMC8320990 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionAgitation, experienced by patients with dementia, is difficult to manage and stressful for caregivers. Currently, agitation is primarily assessed by caregivers or clinicians based on self-report or very brief periods of observation. This limits availability of comprehensive or sensitive enough reporting to detect early signs of agitation or identify its precipitants. The purpose of this article is to provide proof of concept for characterizing and predicting agitation using a system that continuously monitors patients' activities and living environment within memory care facilities.MethodsContinuous and unobtrusive monitoring of a participant is achieved using behavioral sensors, which include passive infrared motion sensors, door contact sensors, a wearable actigraphy device, and a bed pressure mat sensor installed in the living quarters of the participant. Environmental sensors are also used to continuously assess temperature, light, sound, and humidity. Episodes of agitation are reported by nursing staff. Data collected for 138 days were divided by 8-hour nursing shifts. Features from agitated shifts were compared to those from non-agitated shifts using t-tests.ResultsA total of 37 episodes of agitation were reported for a male participant, aged 64 with Alzheimer's disease, living in a memory care unit. Participant activity metrics (eg, transitions within the living room, sleep scores from the bedmat, and total activity counts from the actigraph) significantly correlated with occurrences of agitation at night (P < 0.05). Environmental variables (eg, humidity) also correlated with the occurrences of agitation at night (P < 0.05). Higher activity levels were also observed in the evenings before agitated nights.DiscussionA platform of sensors used for unobtrusive and continuous monitoring of participants with dementia and their living space seems feasible and shows promise for characterization of episodes of agitation and identification of behavioral and environmental precipitants of agitation.

Project description:BackgroundAcute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.ObjectiveThe primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.MethodsWe developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.ResultsThe developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.ConclusionsNAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

Project description:BackgroundSince 2014, over 1.6 million people have been forcibly displaced by the conflict in eastern Ukraine. In 2014, 8% of reproductive-aged women in Ukraine had ever experienced sexual violence, compared to 5% in 2007. This increase was driven by non-domestic sexual violence. Our study examined characteristics of women in eastern Ukraine receiving psychosocial services following sexual violence compared to survivors of other forms of gender-based violence.MethodsIntake data collected between February 2016 and June 2017 by psychosocial service providers in five conflict-affected areas of Ukraine from women, aged 15-49, (N = 8525), was analyzed. Descriptive analysis and covariate adjusted logistic and negative binomial regressions were used to identify socioeconomic, incident and access to services factors associated with having experienced sexual violence compared to other forms of violence.ResultsAmong this sample of survivors receiving psychosocial services, 2.6% (n = 220) reported experiencing sexual violence. A majority of sexual violence acts reported were committed by non-domestic perpetrators (61.4%); followed by intimate partners (25.9%). Almost half of sexual violence cases occurred at home (49.1%). Experiencing sexual violence was positively associated with being younger, single and internally displaced, and negatively with engaging in unpaid labor, such as childcare. Women who experienced sexual violence delayed seeking care by 4 days compared to other gender-based violence survivors. Sexual violence survivors were less likely than physical violence survivors to have reported the incident prior to receiving care (adjusted odds ratio = 0.39; 95% confidence interval = 0.28-0.54).ConclusionsNon-domestic and intimate partner sexual violence were both prevalent in our sample. Compared to survivors disclosing other types of gender-based violence, sexual violence survivors appear to face unique barriers to reporting and accessing timely care. Prevention and outreach programs tailored to the specific vulnerabilities, such as displacement status, and needs of sexual violence survivors in conflict settings are urgently needed.

Project description:BackgroundPreventing transition to chronic back pain (CBP) is a long-sought strategy that could rescue patients from prolonged suffering. Recent rodent and human brain imaging studies suggest involvement of sexually dimorphic, dopaminergic-motivational, mesolimbic circuits in the transition to chronic pain (tCBP), and hint that the combination of carbidopa/levodopa and naproxen (LDP + NPX) may block tCBP. Here we evaluated, in people with recent-onset back pain, whether a 3-month treatment with LDP + NPX is safe, blocks tCBP, and whether its efficacy is sex-dependent.MethodsA total of 72 participants were enrolled and stratified by risk for tCBP using brain-imaging biomarkers. Low-risk participants entered a no-treatment arm. Others were randomized to placebo + naproxen or LDP + NPX for 3 months.ResultsBoth treatments resulted in more than 50% pain relief for approximately 75% of participants. A strong sex by treatment interaction was observed for daily pain intensity (phone NRS, P = 0.007), replicated on 4-week average pain (Pain/4w, P = 0.00001), and in intent-to-treat analysis (Pain/4w, P = 0.000004). Nucleus accumbens functional connectivity with medial prefrontal cortex, a predefined objective biomarker, showed sex dependence at baseline (P = 0.03) and sex-by-treatment interaction effect 3 months after treatment cessation (P = 0.031). Treatment modified the psychological profile of participants, and disrupted brain modeling-based predicted back pain intensity trajectories. Forty participants were queried 3.3 years from trial start; back pain ratings were similar between end of treatment and at 3.3 years (P = 0.62), indicating persistence of relief for this duration.ConclusionsThese results provide the first evidence for preventing transition to chronic back pain using sex-specific pharmacotherapy. These provocative observations require confirmation in a larger study. ClinicalTrials.gov identifier: NCT01951105.

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

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