Browsing by Subject "Patient reported outcome"

Now showing 1 - 3 of 3
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    Characterizing Extreme Phenotypes for Pain Interference in Persons with Chronic Pain following Traumatic Brain Injury: A NIDILRR and VA TBI Model Systems Collaborative Project
    (Wolters Kluwer, 2024) Hoffman, Jeanne M.; Ketchum, Jessica M.; Agtarap, Stephanie; Dams-O’Connor, Kristen; Hammond, Flora M.; Martin, Aaron M.; Sevigny, Mitch; Walker, William C.; Harrison-Felix, Cynthia; Zafonte, Ross; Nakase-Richardson, Risa; Physical Medicine and Rehabilitation, School of Medicine
    Objective: To define and characterize extreme phenotypes based on pain interference for persons with chronic pain following traumatic brain injury (TBI). Setting: Eighteen Traumatic Brain Injury Model System (TBIMS) Centers. Participants: A total of 1762 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview. Primary measures: The Brief Pain Inventory (BPI) interference scale, sociodemographic, injury, functional outcome, pain, and treatment characteristics. Results: Participants were predominantly male (73%), White (75%), middle-aged (mean 46 years), and who were injured in motor vehicle accidents (53%) or falls (20%). Extreme phenotypes were identified based on upper and lower 25th percentiles to create low-interference ( n = 441) and high-interference ( n = 431) extreme phenotypes. Bivariate comparisons found several sociodemographic, injury, function, pain, and treatment differences between extreme phenotype groups, including significant differences ( P < .001) on all measures of concurrent function with those in the low-interference extreme phenotype experiencing better function than those in the high-interference extreme phenotype. Lasso regression combined with logistic regression identified multivariable predictors of low- versus high-interference extreme phenotypes. Reductions in the odds of low- versus high-interference phenotypes were significantly associated with higher pain intensity (odds ratio [OR] = 0.33), having neuropathic pain (OR = 0.40), migraine headache (OR = 0.41), leg/feet pain (OR = 0.34), or hip pain (OR = 0.46), and more pain catastrophizing (OR = 0.81). Conclusion: Results suggest that for those who experience current chronic pain, there is high variability in the experience and impact of pain. Future research is needed to better understand how pain experience impacts individuals with chronic pain and TBI given that pain characteristics were the primary distinguishing factors between phenotypes. The use of extreme phenotypes for pain interference may be useful to better stratify samples to determine efficacy of pain treatment for individuals with TBI.
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    Characterizing Extreme Phenotypes for Perceived Improvement from Treatment in Persons with Chronic Pain following Traumatic Brain Injury: A NIDILRR and VA TBI Model Systems Collaborative Project
    (Wolters Kluwer, 2024) Hoffman, Jeanne M.; Ketchum, Jessica M.; Agtarap, Stephanie; Dams-O’Connor, Kristen; Hammond, Flora M.; Martin, Aaron M.; Sevigny, Mitch; Walker, William C.; Harrison-Felix, Cynthia; Zafonte, Ross; Nakase-Richardson, Risa; Physical Medicine and Rehabilitation, School of Medicine
    Objective: To define and characterize extreme phenotypes based on perceived improvement in pain for persons with chronic pain following traumatic brain injury (TBI). Setting: Eighteen Traumatic Brain Injury Model System (TBIMS) Centers. Participants: A total of 1762 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview. Primary measures: The Patient's Global Impression of Change (PGIC) related to pain treatment. Sociodemographic, injury, functional outcome, pain, and pain treatment characteristics. Results: Participants were mostly male (73%), White (75%), middle-aged (mean 46 years), injured in motor vehicle accidents (53%), or falls (20%). Extreme phenotypes were created for an extreme improvement phenotype ( n = 512, 29.8%) defined as "moderately better" or above on the PGIC and an extreme no-change group ( n = 290, 16.9%) defined as no change or worse. Least absolute shrinkage and selection operator (LASSO) regression combined with logistic regression identified multivariable predictors of improvement versus no-change extreme phenotypes. Higher odds of extreme improvement phenotype were significantly associated with being female (odds ratio [OR] = 1.85), married versus single (OR = 2.02), better motor function (OR = 1.03), lower pain intensity (OR = 0.78), and less frequent pain, especially chest pain (OR = 0.36). Several pain treatments were associated with higher odds of being in the extreme improvement versus no-change phenotypes including pain medication (OR = 1.85), physical therapy (OR = 1.51), yoga (OR = 1.61), home exercise program (OR = 1.07), and massage (OR = 1.69). Conclusion: Investigation of extreme phenotypes based on perceived improvement with pain treatment highlights the ability to identify characteristics of individuals based on pain treatment responsiveness. A better understanding of the biopsychosocial characteristics of those who respond and do not respond to pain treatments received may help inform better surveillance, monitoring, and treatment. With further research, the identification of risk factors (such as pain intensity and frequency) for treatment response/nonresponse may provide indicators to prompt changes in care for individuals with chronic pain after TBI.
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    Digital detection of dementia (D3): a study protocol for a pragmatic cluster-randomized trial examining the application of patient-reported outcomes and passive clinical decision support systems
    (MDPI, 2022-10-11) Kleiman, Michael J.; Plewes, Abbi D.; Owora, Arthur; Grout, Randall W.; Dexter, Paul Richard; Fowler, Nicole R.; Galvin, James E.; Ben Miled, Zina; Boustani, Malaz; Medicine, School of Medicine
    Background: Early detection of Alzheimer's disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. Methods: We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient's physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. Discussion: This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices.