Department of Physical Medicine and Rehabilitation, Mayo Clinic, Rochester, MN, USA.
Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
NeuroRehabilitation. 2020;47(1):65-77. doi: 10.3233/NRE-203082.
The Neurobehavioral Symptom Inventory (NSI-22) is a validated self-report measure designed to assess neurobehavioral symptoms (NBS) after mild TBI (MTBI). Psychological and behavioral factors have been shown to be predictors of persistent NBS reporting in veterans; however, there is still a gap in knowledge about these associations in a civilian population presenting for treatment.
This study seeks to identify the predictors of increased NBS reporting on the NSI-22 in a treatment-seeking population with MTBI.
Analysis of 80 treatment seeking participants admitted to an interdisciplinary outpatient rehabilitation program with a diagnosis of MTBI. NSI-22 was used to measure NBS reporting. Predictor variables identified by univariate analysis were entered into a multivariable regression model, which was adjusted for demographic variables.
Higher NSI-22 scores correlated with increased level of depressive complaints (PHQ-9), higher disability (M2PI), lower satisfaction with life (SWLS), prior MTBI, fewer years of education, absence of motor vehicle collision (MVC), and unemployment at time of assessment. When those variables were used in a multivariable linear regression model, PHQ-9, M2PI, years of education, and absence of MVC remained statistically significant.
Psychological factors and level of societal participation predicted increased NBS as compared with injury severity and time since injury.
神经行为症状量表(NSI-22)是一种经过验证的自我报告量表,旨在评估轻度创伤性脑损伤(MTBI)后的神经行为症状(NBS)。心理和行为因素已被证明是退伍军人持续报告 NBS 的预测因素;然而,在出现治疗的平民人群中,这些关联的知识仍然存在差距。
本研究旨在确定治疗性 MTBI 人群中 NSI-22 报告 NBS 增加的预测因素。
对 80 名接受多学科门诊康复计划治疗的 MTBI 诊断患者进行分析。使用 NSI-22 来衡量 NBS 报告。通过单变量分析确定的预测变量被输入多变量回归模型,该模型根据人口统计学变量进行了调整。
NSI-22 评分越高,抑郁症状(PHQ-9)、残疾程度(M2PI)、生活满意度(SWLS)越低、先前有 MTBI、教育年限越少、无机动车碰撞(MVC)和评估时失业的可能性越高。当这些变量在多变量线性回归模型中使用时,PHQ-9、M2PI、教育年限和缺乏 MVC 仍然具有统计学意义。
与损伤严重程度和损伤后时间相比,心理因素和社会参与程度预测 NBS 增加。