基于特征解构策略识别与颅脑损伤患者日常生活能力受损相关的急性期特征  

Feature Deconstruction Strategy Based Identification of Acute Features Associated With Impairment of Activities of Daily Living in Patients With Traumatic Brain Injury

作　　者：刘迎 王婕妤 朱陈淮玉 林心意 孙艺航 时燕薇[1,2,3] 曾燕妮 LIU Ying;WANG Jie-yu;ZHUCHEN Huai-yu;LIN Xin-yi;SUN Yi-hang;SHI Yan-wei;ZENG Yan-ni(Faculty of Forensic Medicine,Zhongshan School of Medicine,Sun Yat-sen University,Guangzhou 510080,China;Guangdong Province Translational Forensic Medicine Engineering Technology Research Center,Guangzhou 510080,China;Guangdong Province Key Laboratory of Brain Function and Disease,Guangzhou 510080,China)

机构地区：[1]中山大学中山医学院法医学系,广东广州510080 [2]广东省法医学转化医学工程技术研究中心,广东广州510080 [3]广东省脑功能与脑疾病重点实验室,广东广州510080

出　　处：《中山大学学报（医学科学版）》2023年第6期949-957,共9页Journal of Sun Yat-Sen University:Medical Sciences

基　　金：国家自然科学基金(81772032);科技创新2030(2021ZD0202000)。

摘　　要：【目的】寻找与创伤性颅脑损伤(TBI)预后相关的急性期特征。【方法】回顾性分析354名TBI患者的人口学、急性期和慢性期特征,使用传统的基于关联分析和预测模型的策略以及一种基于特征解构的创新的研究策略,识别与预后指标-慢性期日常生活能力(ADL)受损相关的急性期特征。特征解构策略通过使用LASSO构建基于其他非ADL的慢性期指标预测ADL的模型,找到解释TBI人群ADL的关键慢性期特征维度,再分析与这些特征维度显著相关的人口学、急性期变量。【结果】特征解构策略将ADL在TBI人群中解构为“受伤后脑萎缩”“自知力受损程度”“四肢乏力”等慢性期特征维度,同时首次揭示了急性期特征与具体慢性期损伤特征的联系,如TBI患者昏迷时间长和GCS评分低时,慢性期“近记忆受损”的风险最大[scaled coma time OR95%CI=94.288(35.095,273.231);scaled GCS OR95%CI=0.068(0.030,0.147)];TBI患者有脑积水时,慢性期“自知力受损”和“定向力障碍”的风险最大[insight impairment OR95%CI=6.760(3.653,12.855);disorientation OR95%CI=6.538(3.530,12.490)]。所有策略均表明ADL受损最大的急性期风险因素为昏迷时间长、GCS评分低和有脑积水。【结论】本研究提出了一种新的建立TBI急性期特征和预后间关联的研究策略,识别了与预后指标ADL相关的人口学和急性期特征。【Objective】To identify acute phase features associated with the prognosis of traumatic brain injury(TBI).【Methods】Through two traditional strategies,correlation analysis and prediction model,and one innovative research strat‐egy based on feature deconstruction,a retrospective analysis was conducted using demographic,acute phase and chronic phase features of 354 TBI patients to identify acute phase features associated with activities of daily living(ADL)in chron‐ic phase of TBI.For feature deconstruction strategy,the LASSO(Least Absolute Shrinkage and Selection Operator)algo‐rithm was used to build a prediction model that could effectively predict ADL based on non-ADL chronic phase features.The model could indicate the key chronic phase dimensions determining the ADL in TBI patients.We then identified demo‐graphic and acute phase variables that were significantly associated with these key chronic phase features.【Results】The feature deconstruction strategy revealed that ADL could be deconstructed into chronic phase dimensions such as weak limbs in TBI population.Importantly,to the best of our knowledge,this strategy revealed for the first time the association of these important acute phase features with specific chronic phase impairment features.For example,TBI patients had a higher risk for chronic phase recent memory impairment if they had a prolonged coma time and low GCS scores at acute phase[scaled coma time OR95%CI=94.288(35.095,273.231);scaled GCS OR95%CI=0.068(0.030,0.147)];the patients had a higher risk for insight impairment and disorientation at chronic phase if they had hydrocephalus at acute phase[insight impairment OR95%CI=6.760(3.653,12.855);disorientation OR95%CI=6.538(3.530,12.490)].All strategies showed that the strongest risk factors for ADL damage in the chronic phase included prolonged coma time and low GCS scores as well as hydrocephalus.【Conclusion】This study provides an innovative research strategy to establish the association between acute injury features and chronic

关 键 词：创伤性颅脑损伤 日常生活能力 预测模型 LASSO 急性期 

分 类 号：D919[医药卫生—法医学] R749.12[政治法律—法学]