颅脑损伤昏迷患者苏醒的影响因素分析

发布时间：2017-12-27 23:03

本文关键词：颅脑损伤昏迷患者苏醒的影响因素分析　出处：《新乡医学院》2015年硕士论文　论文类型：学位论文

【摘要】：目的颅脑损伤(brain injury)是神经外科急危重症之一,预后差,致残率、致死率高,对人类健康和生命造成严重危害。我国颅脑损伤的发病率为100-200/10万人,18%-20%为重型颅脑损伤,30%-50%最终死亡,每年因此造成的经济损失达数百亿。重型颅脑损伤患者通常合并昏迷,对颅脑损伤昏迷患者能否苏醒的预测和评估,对重型颅脑损伤患者临床治疗方案的制定有着重要的意义。目前,颅脑损伤昏迷预后的研究涵盖了以下几个方面：神经系统检查如瞳孔改变和GCS评分,影像学检查如头颅CT、MRI,神经电生理学检查如脑电图(EEG)和诱发电位,以及生理生化如血糖、血钠、脑脊液SB100等改变。但是由于各地区医疗设备以及医疗水平差异很大,因此在医疗设备欠发达地区,特别是在基层医院评估手段受到很大的限制。本研究的目的是为了探讨颅脑损伤昏迷患者预后影响因素,建立颅脑损伤后预测昏迷患者苏醒率的模型,协助临床医师进行临床决策的制定。方法回顾性分析2009年2月-2014年2月河南省安阳地区医院神经外科共收治380例颅脑损伤昏迷患者,了解患者预后情况即是否苏醒。收集患者19项可能影响因素包括：性别、年龄、瞳孔大小、瞳孔对光反应变化、入院时GCS评分情况、呼吸异常、复合伤、血压、心率、中线移位情况、颅内血肿量、环池及脑干形态变化、脑干听觉诱发电位(BAEP)变化、是否有消化道出血、血糖值、是否合并高钠血症、是否合并颅内感染、是否合并肺部感染、是否合并脑积水。采用SPSS 19.0统计软件分析,对各项可能影响因素进行单因素分析,多因素分析采用Logistic回归分析,确定影响患者苏醒率的因素,并且建立回归方程。其中检验水准α=0.05。结果1本研究共入组380例患者,在随访期限内,苏醒192例(50.5%),未苏醒188例(49.5%)。年龄、性别、心率异常、颅内血肿量等4项与患者苏醒率无关(P0.05)；呼吸异常、低血压、是否合并其他部位损伤、瞳孔对光反射消失、是否出现瞳孔散大、中线移位的程度、GCS评分高低、中脑周围池变化的程度、脑干听觉诱发电位异常、高钠血症、消化道出血、肺部感染、颅内感染、脑积水、血糖值等15项与患者苏醒率存在相关性(P0.05)。对单因素分析中的15个有影响的因素进行Logistic回归分析,呼吸异常、环池及脑干形态变化分级、脑干听觉诱发电位异常、GCS分组、脑积水、颅内感染等6项与颅脑损伤昏迷患者苏醒率之间存在显著相关性(P0.05)。2建立回归方程：Logistic(P)=19.135+2.872Xi+2.341X2-25.516X3.1-23.021X3.2-20.844X3.3-19.554X3.4+ 0.996X3.5+3.612X4+2.487X5+2.521X6.本研究所建立的回归模型总体判对率为92.6%，，模型的Nagelkerke决定系数R2=0.850。结论1.GCS评分、呼吸异常、环池和脑干形态变化、脑干听觉诱发电位异常、伴发颅内感染、脑积水6项因素是影响颅脑损伤患者预后的显著性因素。2.本研究所建立的回归模型可为临床工作中对颅脑损伤昏迷患者能否苏醒的预测提供有效且简便的方法,为此类患者临床治疗方案的制定提供可靠的依据,有利于合理分配医疗卫生资源。
[Abstract]:Objective craniocerebral injury (brain injury) is one of the critical and critical diseases in the Department of neurosurgery. The prognosis is poor, the rate of disability and death is high, and it is serious harm to human health and life. The incidence of craniocerebral injury in China is 100-200/10 million, 18%-20% is a severe head injury, and 30%-50% eventually dies, and the economic loss is hundreds of billions of dollars a year. Severe craniocerebral injury is usually associated with coma. It is of great significance to predict and assess coma recovery in patients with traumatic brain injury, and to formulate a clinical treatment plan for patients with severe head injury. At present, the prognosis of head injury coma research covers the following aspects: the examination of the nervous system such as pupil change and GCS score, imaging examination such as brain CT, MRI, electrophysiological examinations such as EEG and evoked potentials (EEG), and the physiological and biochemical blood glucose, blood sodium, cerebrospinal fluid SB100 change. However, due to the large difference of medical devices and medical level in different regions, the evaluation methods in the less developed areas of medical equipment, especially in the grass-roots hospitals, are greatly limited. The purpose of this study is to explore the prognostic factors of coma patients after craniocerebral injury, and establish a model to predict the recovery rate of coma patients after craniocerebral injury, so as to assist clinicians in making clinical decisions. Methods a retrospective analysis of 380 cases of craniocerebral injury comatose patients in Department of Neurosurgery, Anyang District Hospital of Henan province from February 2009 to February -2014, was carried out to know the prognosis of patients. Collect 19 patients may influence factors include: gender, age, pupil size, pupillary light responses, admission GCS score, abnormal breathing, blood pressure, heart rate, combined injuries, midline shift, intracranial hematoma volume, ring pool and morphological changes of brainstem, brainstem auditory evoked potential (BAEP) changes, whether with gastrointestinal bleeding, blood glucose, whether with hypernatremia, whether complicated with intracranial infection, pulmonary infection, whether patients with hydrocephalus. SPSS 19 statistical software was used to analyze the possible factors. Univariate analysis was performed. Multivariate regression analysis was used to identify the factors influencing the recovery rate of patients, and establish regression equation. Results the Logistic regression analysis was used to establish the regression equation. The test level was alpha =0.05. Results in the 1 study, 380 patients were enrolled in the study. During the period of follow-up, 192 cases (50.5%) were awakened and 188 (49.5%) were not recovered. Age, gender, abnormal heart rate, volume of intracranial hematoma and 4 patients with recovery rate independent (P0.05); abnormal breathing, low blood pressure, whether associated injuries, pupillary light reflex, or mydriasis, midline shift, GCS score, perimesencephalic change degree, brainstem auditory evoked potential anomalies, hypernatremia, digestive tract hemorrhage, pulmonary infection, intracranial infection, hydrocephalus, blood glucose of 15 patients with recovery rate correlation (P0.05). On the 15 influential factors in univariate analysis were analyzed by Logistic regression analysis, abnormal breathing, ring pool and brainstem classification, morphological changes of brainstem auditory evoked potential abnormalities, GCS group, hydrocephalus, intracranial infection and 6 with craniocerebral injury and coma significant correlation between patients' recovery rate (P0.05). 2, a regression equation is established: Logistic (P) =19.135+2.872Xi+2.341X2-25.516X3.1-23.021X3.2-20.844X3.3-19.554X3.4+ 0.996X3.5+3.612X4+2.487X5+2.521X6.. The overall judgement rate of the regression model established by this research is 92.6%, and the Nagelkerke coefficient of R2=0.850 of the model is R2=0.850. Conclusion 1.GCS score, respiratory abnormalities, morphological changes of the cisterna and brainstem, brainstem auditory evoked potential abnormalities, intracranial infection and hydrocephalus are 6 factors that influence the prognosis of patients with craniocerebral injury. 2., the regression model established by this study can provide an effective and simple method for predicting the recovery of coma patients with traumatic brain injury in clinical work, providing a reliable basis for the formulation of clinical treatment plan for such patients, and is conducive to the rational allocation of medical and health resources.
【学位授予单位】：新乡医学院
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：R651.15

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