中文版EMSE量表对癫痫持续状态患者预后的预测价值研究

Prediction of mortality using Chinese version of epidemiology-based mortality score in status epilepticus （EMSE）

目的评估基于流行病学癫痫持续状态病死率评分（epidemiology-based mortality score in status epilepticus, EMSE）量表对预测成人癫痫持续状态（status epilepticus，SE）预后的价值。方法采用EMSE对2013年6月至2016年6月期间在浙江省中医院住院治疗的54例成年癫痫持续状态患者病历资料进行回顾性评估，同时采用癫痫持续状态严重程度评分（status epilepticus severity score, STESS）量表与之进行比较，描绘ROC曲线，计算曲线下面积，找到理想界值，比较不同量表的预测价值，并通过Fisher线性判别分析，得到判别方程式并计算正判率。结果54例sE患者中，13例（24．10％）住院期间死亡。使用STESS预测病死率的ROC曲线下面积为0．705，理想界值／〉3分（灵敏度为0．77，特异度为0．56）；使用EMSE预测病死率的ROC曲线下面积为0．800，理想界值≥79分（灵敏度为0．92，特异度为0．61）；其中，EMSE的子项目组合EMSE—EAC（病因．年龄．合并症）预测病死率的ROC曲线下面积为0．814，理想界值≥32分（灵敏度为1．00，特异度为0．56）；另一子项目组合EMSE-EACE（病因-年龄-合并症-脑电图）预测病死率的ROC曲线下面积为0．925，理想界值≥71分（灵敏度为0．77，特异度为0．98）。两两比较显示EMSE．EACE曲线下面积大于STESS及EMSE，差异有统计学意义（P〈0．01）。以预后为因变量（存活赋值为0，死亡赋值为1），分别以STESS、EMSE、EMSE—EAC及EMSE-EACE评分值为自变量，进行Fisher线性判别分析，采用自身验证回代法对判别函数进行检验，正判率分别为44．44％、62．96％、70．37％和81．48％，结论EMSE是临床用于预测癫痫持续状态病死率的有效评分系统，其中EMSE-EACE比STESS及EMSE在预测住院sE患者病死率方面更有优势。

Objective To study the value of epidemiology-based mortality score, a novel scoring system, in in-hospital adult patients with status epilepticus （SE） for predicting mortality, and to compare it with the status epilepticus severity score （STESS）. Methods The clinical and electroencephalography data of 54 adult patients with SE admitted from June 2013 to June 2016 were derived from a prospective SE database of Zhejiang Provincial Hospital of Traditional Chinese Medicine. The outcome was defined as inhospital death or survival at discharge. When the receiver-operating characteristic （ ROC ） curves were made, the area under ROC （AUC） and the optimal cutoff value were calculated. Fisher＇ s linear discriminant function analysis was conducted with the outcome as dependent variable and the scores as independent variables. Results Among 54 patients with SE recruited into the study, 13 （24. 10 % ） died in the hospital. The ROC curve for prediction of in-hospital death based on the STESS had a AUC of 0. 705 with an optimal cutoff value for discrimination （best match for both sensitivity （0. 77 ） and specificity （0. 56） to be /〉 3 points. The AUC based on the EMSE was 0. 800 with an optimal cutoff value for discrimination （best match for both sensitivity （0. 92） and specificity （0. 61 ） to be I〉 79 points. Three elements added in combination with EMSE system （etiology-age-comorbidity, EMSE-EAC ） predicted in- hospital mortality with the best match for both sensitivity （ 1.00 ） and specificity （ 0. 56 ） as the optimal cutoff point was I〉32 points, and the AUC was 0. 814. Four elements added in combination with EMSE system （etiology-age-comorbidity-EEG, EMSE-EACE） predicted in-hospital mortality with the best match for both sensitivity （0. 77） and specificity （0. 98 ） as the optimal cutoff point was ≥71 points with an AUC of 0. 925. The AUC of EMSE-EACE was larger than that of both STESS and EMSE （ Both P 〈 0. 01 ）. Discriminant equations were found by Fisher linear discriminant analysis. The rates of accuracy of the equation for predicting patients＇ prognosis were 44. 44% （STESS）, 62. 96% （ EMSE）, 70. 37% （EMSE- EAC） and 81.48% （EMSE-EACE） respectively, suggesting that the equations of EMSE, EMSE-EAC and EMSE-EACE have superior stability. Conclusions The EMSE is an effective clinical scoring system that focuses on individual mortality. EMSE-EACE is superior over both STESS and EMSE in the prediction of in- hospital death.