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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
SHI Yan-wei, E-mail: shiyanw@mail.sysu.edu.cn
ZENG Yan-ni, E-mail: zengyn5@mail.sysu.edu.cn
Published:20 November 2023,
Received:19 June 2023,
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刘迎,王婕妤,朱陈淮玉等.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[J].中山大学学报(医学科学版),2023,44(06):949-957.
LIU Ying,WANG Jie-yu,ZHUCHEN Huai-yu,et al.Feature Deconstruction Strategy Based Identification of Acute Features Associated With Impairment of Activities of Daily Living in Patients With Traumatic Brain Injury[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(06):949-957.
刘迎,王婕妤,朱陈淮玉等.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[J].中山大学学报(医学科学版),2023,44(06):949-957. DOI: 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0608.
LIU Ying,WANG Jie-yu,ZHUCHEN Huai-yu,et al.Feature Deconstruction Strategy Based Identification of Acute Features Associated With Impairment of Activities of Daily Living in Patients With Traumatic Brain Injury[J].Journal of Sun Yat-sen University(Medical Sciences),2023,44(06):949-957. DOI: 10.13471/j.cnki.j.sun.yat-sen.univ(med.sci).2023.0608.
目的
2
寻找与创伤性颅脑损伤(TBI)预后相关的急性期特征。
方法
2
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
2
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
2
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
2
To identify acute phase features associated with the prognosis of traumatic brain injury (TBI).
Methods
2
Through two traditional strategies, correlation analysis and prediction model, and one innovative research strategy 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 chronic phase of TBI. For feature deconstruction strategy, the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm 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 demographic and acute phase variables that were significantly associated with these key chronic phase features.
Results
2
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
2
This study provides an innovative research strategy to establish the association between acute injury features and chronic recovery features, and to identify demographic and acute phase features associated with the prognosis of TBI.
创伤性颅脑损伤日常生活能力预测模型LASSO急性期
traumatic brain injuryADLprediction modelLASSOacute phase
Mcmillan T, Wilson L, Ponsford J, et al. The Glasgow Outcome Scale - 40 years of application and refinement[J]. Nat Rev Neurol, 2016, 12(8): 477-485.
耿景殿. 颅脑损伤部位及伤后社会功能损害与精神伤残等级的相关性研究[D]. 汕头大学, 2021.
Geng JD. Study on the correlation between the site of craniocerebral injury and the impairment of social functional after injury and the grade of mental disability[D]. Shantou University, 2021.
De Oliveira DV, Vieira RDCA, Pipek LZ, et al. Long-term outcomes in severe traumatic brain injury and associated factors: a prospective cohort study[J]. J Clin Med, 2022, 11(21): 6466.
刘青青. 日常生活能力和社会功能量表在精神伤残评定应用中的初步研究[D]. 昆明医科大学, 2017.
Liu QQ. The preliminary research in the evaluation of mental disability assessment of activity of daily living and impaired social function schedule[D]. Kunming Medical University, 2017.
刘露, 李豪喆, 陈琛, 等. 日常生活活动量表在轻度精神伤残评定中的应用[J]. 法医学杂志, 2018, 34(1): 44-48.
Liu L, Li HZ, Chen C, et al. Application of activities of daily living scale in mild psychiatric impairment assessment[J]. J Forens Med, 2018, 34(1): 44-48.
崔俊杰, 孙建军, 李贺, 等. 创伤后脑梗死形成机制及危险因素研究进展[J]. 中国神经精神疾病杂志, 2022, 48(8): 498-502.
Cui JJ, Sun JJ, Li H, et al. Research progress on formation mechanism and risk factors of post-traumatic cerebralinfarction[J]. Chin J Nervous Mental Dis, 2022, 48(8): 498-502.
Kanchan A, Singh AR, Khan NA, et al. Impact of neuropsychological rehabilitation on activities of daily living and community reintegration of patients with traumatic brain injury[J]. Indian J Psychiatry, 2018, 60(1): 38-48.
Katz S, Downs TD, Cash HR, et al. Progress in development of the index of ADL1[J]. The Gerontologist, 1970, 10(1_Part_1): 20-30.
Wade D, Collin C. The Barthel ADL Index: a standard measure of physical disability?[J]. Int Disabil Stud, 1988, 10(2): 64-67.
Cerasa A, Tartarisco G, Bruschetta R, et al. Predicting outcome in patients with brain injury: differences between machine learning versus conventional statistics[J]. Biomedicines, 2022, 10(9): 2267.
Caceres E, Olivella JC, Yanez M, et al. Risk factors and outcomes of lower respiratory tract infections after traumatic brain injury: a retrospective observational study[J]. Front Med (Lausanne), 2023, 10: 1077371.
Nourelahi M, Dadboud F, Khalili H, et al. A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 months[J]. Acute Crit Care, 2022, 37(1): 45-52.
Sandhaug M, Andelic N, Berntsen SA, et al. Self and near relative ratings of functional level one year after traumatic brain injury[J]. Disabil Rehabilit, 2012, 34(11): 904-909.
Breiman L. Manual for Setting Up, Using, and Understanding Random Forest V4. 0[Computer software manual][Z]. 2003.
Aulchenko YS, Ripke S, Isaacs A, et al. GenABEL: an R library for genome-wide association analysis[J]. Bioinformatics, 2007, 23(10): 1294-1296.
Romagnosi F, Bernini A, Bongiovanni F, et al. Neurological pupil index for the early prediction of outcome in severe acute brain injury patients[J]. Brain Sci, 2022, 12(5): 609.
Potvin M-J, Brayet P, Paradis V, et al. Predictive value of a new brief cognitive test for long-term functional outcome in acute traumatic brain injury[J]. Arch Phys Med Rehabil, 2022, 103(11): 2131-2137.
Jahns FP, Miroz JP, Messerer M, et al. Quantitative pupillometry for the monitoring of intracranial hypertension in patients with severe traumatic brain injury[J]. Crit Care, 2019, 23: 1-9.
Tang OY, Shao B, Kimata AR, et al. The impact of frailty on traumatic brain injury outcomes: an analysis of 691 821 nationwide cases[J]. Neurosurgery, 2022, 91(5): 808-820.
Schneider ALC, Huie JR, Boscardin WJ, et al. Cognitive outcome 1 year after mild traumatic brain injury: results from the TRACK-TBI study[J]. Neurology, 2022, 98(12): e1248-e1261.
Mamatkulovich MA, Abdukholikovich AM. The correlations of clinical-neurological signs with the different outcomes of traumatic brain injury and their prognostic important[J]. Med Res Arch, 2022, 10(9).
Mazzini L, Campini R, Angelino E, et al. Posttraumatic hydrocephalus: a clinical, neuroradiologic, and neuropsychologic assessment of long-term outcome [J]. Arch Phys Med Rehabil, 2003, 84(11): 1637-1641.
王耀宾, 王丽丽, 钟世亮. 外伤后弥漫性脑萎缩25例法医学分析[J].法医学杂志, 2019, 35(1): 48-51; +57.
Wang YB, Wang LL, Zhong SL. Forensic analysis of 25 cases of diffuse brain atrophy after trauma[J]. J Foren Med, 2019, 35(1): 48-51;+57.
Yamamoto H, Takeda K, Koyama S, et al. The relationship between upper limb function and activities of daily living without the effects of lower limb function: a cross-sectional study[J]. British J Occupat Ther, 2022, 85(5): 360-366.
Gao L, Wu X, Hu J, et al. Intensive management and prognosis of 127 cases with traumatic bilateral frontal contusions[J]. World Neurosurg, 2013, 80(6): 879-888.
Flanagan SR, Hibbard MR, Gordon WA. The impact of age on traumatic brain injury[J]. Phys Med Rehabilit Clin, 2005, 16(1): 163-177.
Brett BL, Gardner RC, Godbout J, et al. Traumatic brain injury and risk of neurodegenerative disorder[J]. Biol Psychiatry, 2022, 91(5): 498-507.
Zhang B, Hu M, Sun Y, et al. Associations between the prevalence, treatment, control of hypertension and cognitive trajectories among chinese middle-aged and older adults[J]. Am J Geriatr Psychiatry, 2022, 30(10): 1123-1134.
Mkubwa J, Bedada A, Esterhuizen T. Traumatic brain injury: association between the Glasgow Coma Scale score and intensive care unit mortality[J]. South Afr J Crit Care, 2022, 38(2): 60-63.
Matsuo K, Aihara H, Nakai T, et al. Machine learning to predict in-hospital morbidity and mortality after traumatic brain injury[J]. J Neurotrauma, 2020, 37(1): 202-210.
Trent T, Vashisht A, Novakovic S, et al. Pupillary light reflex measured with quantitative pupillometry has low sensitivity and high specificity for predicting neuroworsening after traumatic brain injury[J]. J Am Assoc Nurse Pract, 2023, 35(2): 130-134.
Cho MJ, Lee HD, Kim JW, et al. Relationship between short-term memory impairment and the dorsolateral prefrontal cortex injury in patients with mild traumatic brain injury[J]. JIN, 2022, 21(3).
Hochstetler A, Raskin J, Blazer-Yost BL. Hydrocephalus: historical analysis and considerations for treatment[J]. Eur J Med Res, 2022, 27(1): 168.
Maas AIR, Menon DK, Manley GT, et al. Traumatic brain injury: progress and challenges in prevention, clinical care, and research[J]. Lancet Neurol, 2022, 21(11): 1004-1060.
Jiang JY, Gao GY, Feng JF, et al. Traumatic brain injury in China[J]. Lancet Neurol, 2019, 18(3): 286-295.
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