CAJ | 학술논문

目的探讨脑外伤昏迷患者入院时和治疗后半年清醒概率的预测模型并对模型软件开发和应用. 方法回顾性分析南方医科大学附属花都医院神经外科自2010年9月至2012年10月间收治的190例脑外伤昏迷患者的临床资料,二分类Logistic回归分析影响患者入院时、治疗后清醒的多种因素,建立预测清醒概率的模型并应用C++语言编制“脑外伤昏迷患者清醒概率预测”软件,应用此软件预测103例患者的清醒概率. 结果二分类Logistic回归显示影响清醒预后的因素包括年龄、瞳孔对光反射、运动格拉斯哥昏迷评分(mGCS)、中脑周围池分级、睁眼时间、脑缺血体积百分比.预测模型A和B性能良好(C统计值分别为0.955、0.975、模型判对率分别为90.5％、94.0％).应用软件预测103例患者入院时、治疗后清醒的判对率分别为87.3％、93.2％,结果可靠. 结论建立的预测模型可以简单且准确的预测脑外伤昏迷患者的清醒概率,“脑外伤昏迷患者清醒概率预测”软件可辅助临床决策的制定.
목적 탐토뇌외상혼미환자입원시화치료후반년청성개솔적예측모형병대모형연건개발화응용. 방법 회고성분석남방의과대학부속화도의원신경외과자2010년9월지2012년10월간수치적190례뇌외상혼미환자적림상자료,이분류Logistic회귀분석영향환자입원시、치료후청성적다충인소,건립예측청성개솔적모형병응용C++어언편제“뇌외상혼미환자청성개솔예측”연건,응용차연건예측103례환자적청성개솔. 결과 이분류Logistic회귀현시영향청성예후적인소포괄년령、동공대광반사、운동격랍사가혼미평분(mGCS)、중뇌주위지분급、정안시간、뇌결혈체적백분비.예측모형A화B성능량호(C통계치분별위0.955、0.975、모형판대솔분별위90.5％、94.0％).응용연건예측103례환자입원시、치료후청성적판대솔분별위87.3％、93.2％,결과가고. 결론 건립적예측모형가이간단차준학적예측뇌외상혼미환자적청성개솔,“뇌외상혼미환자청성개솔예측”연건가보조림상결책적제정.
Objective To explore the awakening probabilistic prediction models of coma patients with traumatic brain injury on admission and six months after treatment,and develop and apply the software of the models.Methods Clinical data of 190 coma patients with traumatic brain injury,admitted to our hospital from September 2010 to October 2012,were analyzed retrospectively.Potential predictive factors at admission and after awakening were analyzed by binary Logistic regression analysis; based on these factors,the awakening probabilistic prediction models of coma patients with traumatic brain injury were established; C++ language was used to write the computer software that could predict the awakening probability of 103 patients with traumatic brain injury.Results Multinomial Logistic regression analysis showed that 6 factors,including age,pupillary light reflex,movement Glasgow coma scale (mGCS) scores,morphology changes of mesencephalon surrounding cisterna,eye opening time after treatment,and percentages of ischemic brain volume in CT images,were independent factors to predict the awakening probability of coma patients with traumatic brain injury.Model A and B owned high performance (C statistics of models:0.955 and 0.975; accept rate of models:90.5％ and 94.0％).The established software based on models was easy to use with reliable results (the accept rate of 103 patients were 87.3％ and 93.2％).Conclusion The established models can timely and accurately predict the awakening probability of coma patients with traumatic brain injury; the software named sober probabilistic prediction for coma patients with traumatic brain injury can help in decision-making in clinics.