Pugh Mary Jo, Munger Clary Heidi, Myers Madeleine, Kennedy Eamonn, Amuan Megan, Swan Alicia A, Hinds Sidney, LaFrance W Curt, Altalib Hamada, Towne Alan, Henion Amy, White Abigail, Baca Christine, Wang Chen-Pin
Informatics, Decision Enhancement, & Analytic Sciences (IDEAS) Center, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Division of Epidemiology, Department of Internal Medicine, University of Utah Health Science Center, University of Utah, Salt Lake City, Utah, USA.
Epilepsia. 2025 Jan;66(1):170-183. doi: 10.1111/epi.18170. Epub 2024 Nov 2.
To investigate phenotypes of comorbidity before and after an epilepsy diagnosis in a national cohort of post-9/11 Service Members and Veterans and explore phenotypic associations with mortality.
Among a longitudinal cohort of Service Members and Veterans receiving care in the Veterans Health Administration (VHA) from 2002 to 2018, annual diagnoses for 26 conditions associated with epilepsy were collected over 5 years, ranging from 2 years prior to 2 years after the year of first epilepsy diagnosis. Latent class analysis (LCA) was used to identify probabilistic comorbidity phenotypes with distinct health trajectories. Descriptive statistics were used to describe the characteristics of each phenotype. Fine and Gray cause-specific survival models were used to measure mortality outcomes for each phenotype up to 2021.
Six distinct phenotypes were identified: (1) relatively healthy, (2) post-traumatic stress disorder, (3) anxiety and depression, (4) chronic disease, (5) bipolar/substance use disorder, and (6) polytrauma. Accidents were the most common cause of death overall, followed by suicide/mental health and cancer, respectively. Each phenotype exhibited unique associations with mortality and cause of death, highlighting the differential impact of comorbidity patterns on patient outcomes.
By delineating clinically meaningful epilepsy comorbidity phenotypes, this study offers a framework for clinicians to tailor interventions. Moreover, these data support systems of care that facilitate treatment of epilepsy and comorbidities within an interdisciplinary health team that allows continuity of care. Targeting treatment toward patients with epilepsy who present with specific heightened risks could help mitigate adverse outcomes and enhance overall patient care.
在“9·11”事件后的军人和退伍军人全国队列中,调查癫痫诊断前后的共病表型,并探讨表型与死亡率的关联。
在2002年至2018年期间接受退伍军人健康管理局(VHA)护理的军人和退伍军人纵向队列中,收集了从首次癫痫诊断年份前2年到诊断后2年的5年期间26种与癫痫相关疾病的年度诊断信息。采用潜在类别分析(LCA)来识别具有不同健康轨迹的概率性共病表型。使用描述性统计来描述每种表型的特征。采用Fine和Gray特定病因生存模型来测量每种表型截至2021年的死亡率结果。
识别出六种不同的表型:(1)相对健康型,(2)创伤后应激障碍型,(3)焦虑和抑郁型,(4)慢性疾病型,(5)双相情感障碍/物质使用障碍型,以及(6)多发伤型。总体而言,事故是最常见的死亡原因,其次分别是自杀/心理健康问题和癌症。每种表型与死亡率和死亡原因均呈现独特的关联,凸显了共病模式对患者结局的不同影响。
通过描绘具有临床意义的癫痫共病表型,本研究为临床医生制定干预措施提供了一个框架。此外,这些数据支持在跨学科医疗团队中促进癫痫和共病治疗的护理系统,该系统可实现护理的连续性。针对具有特定高风险的癫痫患者进行治疗,有助于减轻不良结局并提高整体患者护理水平。