Adam R Kinney, Alexandra L Schneider, Samuel E King, Xiang-Dong Yan, Jeri E Forster, Nazanin H Bahraini, Lisa A Brenner
{"title":"根据脑震荡后症状认可的不同构型识别和预测轻度脑损伤退伍军人亚群:潜类分析","authors":"Adam R Kinney, Alexandra L Schneider, Samuel E King, Xiang-Dong Yan, Jeri E Forster, Nazanin H Bahraini, Lisa A Brenner","doi":"10.1097/HTR.0000000000000890","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To identify distinct subgroups of veterans with mild traumatic brain injury (mTBI) based on configurations of postconcussive symptom (PCS) endorsement, and to examine predictors of subgroup membership.</p><p><strong>Setting: </strong>Outpatient Veterans Health Administration (VHA).</p><p><strong>Participants: </strong>Veterans with clinician-confirmed mTBI who completed the Neurobehavioral Symptom Inventory (NSI), determined using the Comprehensive Traumatic Brain Injury Evaluation database. Individuals who tended to overreport symptoms were excluded via an embedded symptom validity scale.</p><p><strong>Design: </strong>Retrospective cohort study leveraging national VHA clinical data from 2012 to 2020. Latent class analysis (LCA) with a split-sample cross-validation procedure was used to identify subgroups of veterans. Multinomial logistic regression was used to examine predictors of subgroup membership.</p><p><strong>Main measures: </strong>Latent classes identified using NSI items.</p><p><strong>Results: </strong>The study included 72 252 eligible veterans, who were primarily White (73%) and male (94%). The LCA supported 7 distinct subgroups of veterans with mTBI, characterized by diverging patterns of risk for specific PCS across vestibular (eg, dizziness), somatosensory (eg, headache), cognitive (eg, forgetfulness), and mood domains (eg, anxiety). The most prevalent subgroup was Global (20.7%), followed by Cognitive-Mood (16.3%), Headache-Cognitive-Mood (H-C-M; 16.3%), Headache-Mood (14.2%), Anxiety (13.8%), Headache-Sleep (10.3%), and Minimal (8.5%). The Global class was used as the reference class for multinomial logistic regression because it was distinguished from others based on elevated risk for PCS across all domains. Female (vs male), Black (vs White), and Hispanic veterans (vs non-Hispanic) were less likely to be members of most subgroups characterized by lesser PCS endorsement relative to the Global class (excluding Headache-Mood).</p><p><strong>Conclusion: </strong>The 7 distinct groups identified in this study distill heterogenous patterns of PCS endorsement into clinically actionable phenotypes that can be used to tailor clinical management of veterans with mTBI. Findings reveal empirical support for potential racial, ethnic, and sex-based disparities in PCS among veterans, informing efforts aimed at promoting equitable recovery from mTBI in this population.</p>","PeriodicalId":15901,"journal":{"name":"Journal of Head Trauma Rehabilitation","volume":" ","pages":"247-257"},"PeriodicalIF":2.4000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying and Predicting Subgroups of Veterans With Mild Traumatic Brain Injury Based on Distinct Configurations of Postconcussive Symptom Endorsement: A Latent Class Analysis.\",\"authors\":\"Adam R Kinney, Alexandra L Schneider, Samuel E King, Xiang-Dong Yan, Jeri E Forster, Nazanin H Bahraini, Lisa A Brenner\",\"doi\":\"10.1097/HTR.0000000000000890\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To identify distinct subgroups of veterans with mild traumatic brain injury (mTBI) based on configurations of postconcussive symptom (PCS) endorsement, and to examine predictors of subgroup membership.</p><p><strong>Setting: </strong>Outpatient Veterans Health Administration (VHA).</p><p><strong>Participants: </strong>Veterans with clinician-confirmed mTBI who completed the Neurobehavioral Symptom Inventory (NSI), determined using the Comprehensive Traumatic Brain Injury Evaluation database. Individuals who tended to overreport symptoms were excluded via an embedded symptom validity scale.</p><p><strong>Design: </strong>Retrospective cohort study leveraging national VHA clinical data from 2012 to 2020. Latent class analysis (LCA) with a split-sample cross-validation procedure was used to identify subgroups of veterans. Multinomial logistic regression was used to examine predictors of subgroup membership.</p><p><strong>Main measures: </strong>Latent classes identified using NSI items.</p><p><strong>Results: </strong>The study included 72 252 eligible veterans, who were primarily White (73%) and male (94%). The LCA supported 7 distinct subgroups of veterans with mTBI, characterized by diverging patterns of risk for specific PCS across vestibular (eg, dizziness), somatosensory (eg, headache), cognitive (eg, forgetfulness), and mood domains (eg, anxiety). The most prevalent subgroup was Global (20.7%), followed by Cognitive-Mood (16.3%), Headache-Cognitive-Mood (H-C-M; 16.3%), Headache-Mood (14.2%), Anxiety (13.8%), Headache-Sleep (10.3%), and Minimal (8.5%). The Global class was used as the reference class for multinomial logistic regression because it was distinguished from others based on elevated risk for PCS across all domains. Female (vs male), Black (vs White), and Hispanic veterans (vs non-Hispanic) were less likely to be members of most subgroups characterized by lesser PCS endorsement relative to the Global class (excluding Headache-Mood).</p><p><strong>Conclusion: </strong>The 7 distinct groups identified in this study distill heterogenous patterns of PCS endorsement into clinically actionable phenotypes that can be used to tailor clinical management of veterans with mTBI. Findings reveal empirical support for potential racial, ethnic, and sex-based disparities in PCS among veterans, informing efforts aimed at promoting equitable recovery from mTBI in this population.</p>\",\"PeriodicalId\":15901,\"journal\":{\"name\":\"Journal of Head Trauma Rehabilitation\",\"volume\":\" \",\"pages\":\"247-257\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Head Trauma Rehabilitation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/HTR.0000000000000890\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Head Trauma Rehabilitation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/HTR.0000000000000890","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/23 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Identifying and Predicting Subgroups of Veterans With Mild Traumatic Brain Injury Based on Distinct Configurations of Postconcussive Symptom Endorsement: A Latent Class Analysis.
Objective: To identify distinct subgroups of veterans with mild traumatic brain injury (mTBI) based on configurations of postconcussive symptom (PCS) endorsement, and to examine predictors of subgroup membership.
Setting: Outpatient Veterans Health Administration (VHA).
Participants: Veterans with clinician-confirmed mTBI who completed the Neurobehavioral Symptom Inventory (NSI), determined using the Comprehensive Traumatic Brain Injury Evaluation database. Individuals who tended to overreport symptoms were excluded via an embedded symptom validity scale.
Design: Retrospective cohort study leveraging national VHA clinical data from 2012 to 2020. Latent class analysis (LCA) with a split-sample cross-validation procedure was used to identify subgroups of veterans. Multinomial logistic regression was used to examine predictors of subgroup membership.
Main measures: Latent classes identified using NSI items.
Results: The study included 72 252 eligible veterans, who were primarily White (73%) and male (94%). The LCA supported 7 distinct subgroups of veterans with mTBI, characterized by diverging patterns of risk for specific PCS across vestibular (eg, dizziness), somatosensory (eg, headache), cognitive (eg, forgetfulness), and mood domains (eg, anxiety). The most prevalent subgroup was Global (20.7%), followed by Cognitive-Mood (16.3%), Headache-Cognitive-Mood (H-C-M; 16.3%), Headache-Mood (14.2%), Anxiety (13.8%), Headache-Sleep (10.3%), and Minimal (8.5%). The Global class was used as the reference class for multinomial logistic regression because it was distinguished from others based on elevated risk for PCS across all domains. Female (vs male), Black (vs White), and Hispanic veterans (vs non-Hispanic) were less likely to be members of most subgroups characterized by lesser PCS endorsement relative to the Global class (excluding Headache-Mood).
Conclusion: The 7 distinct groups identified in this study distill heterogenous patterns of PCS endorsement into clinically actionable phenotypes that can be used to tailor clinical management of veterans with mTBI. Findings reveal empirical support for potential racial, ethnic, and sex-based disparities in PCS among veterans, informing efforts aimed at promoting equitable recovery from mTBI in this population.
期刊介绍:
The Journal of Head Trauma Rehabilitation is a leading, peer-reviewed resource that provides up-to-date information on the clinical management and rehabilitation of persons with traumatic brain injuries. Six issues each year aspire to the vision of “knowledge informing care” and include a wide range of articles, topical issues, commentaries and special features. It is the official journal of the Brain Injury Association of America (BIAA).