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本次文献选自Kourilsky A, Palpacuer C, Rogers A, Chauvet D, Wiart C, Bourdillon P, Le Guérinel C. Multivariate models to predict pain recurrence and sensitive complications after percutaneous balloon compression in trigeminal neuralgia. J Neurosurg. 2022 Apr 22:1-10.本次学习由王珺楠副主任医师主讲。
Statistical Analyses
A descriptive analysis of the cohort was conducted. This analysis included point estimates, numbers and percentages for qualitative variables, and mean and standard deviation and median and range for quantitative variables.
对该队列进行了描述性分析。该分析包括点估计、定性变量的数字和百分比，以及定量变量的平均值和标准差、中位数和范围。
The search for predictive factors of pain recurrence and severe sensitive complications was carried out by analyzing data according to the following variables: preoperative characteristics, including age; sex; preoperative symptom duration; side of the face affected by the pain; trigeminal nerve territory affected by the pain (subsequently divided depending on the presence of involvement of the mandibular branch of the trigeminal nerve [V3]); presence of atypical features of the pain; presence of an area of hypoesthesia; multiple sclerosis (MS); presence of a neurovascular conflict on MRI; past medical history of thermocoagulation, MVD, glycerol rhizotomy, or stereotactic radiosurgery; intraoperative variables including time of compression divided into two groups (1 minute or > 1 minute); type of compression device used (i.e., specifically made Mullan compression set, universal surrogate Fogarty balloon); occurrence of bleeding during the procedure; blood pressure abnormality; heart rate abnormality; or CSF leak through the cannula.
根据以下变量分析数据，寻找疼痛复发和严重感觉并发症的预测因素：术前特征，包括年龄、性别；术前症状持续时间；受疼痛影响的面部侧别；受疼痛影响的三叉神经区域（随后根据三叉神经下颌分支（V3）的受累情况进行划分）；不典型疼痛；存在感觉减退区域；多发性硬化症（MS）；MRI上存在神经血管压迫；既往有热凝、MVD、甘油注射术或立体定向放射手术的病史；术中变量包括压迫时间分为两组（1分钟或>1分钟）；使用的压迫手术设备；手术过程中出血；血压异常；心率异常；或脑脊液漏。
Missing Data
Analysis of raw data with exclusion of missing data was chosen as the primary result because the small amount of missing data allowed simple exclusion of missing data after verification that the data were missing completely at random, meaning that the presence of missing data was not linked to the primary endpoint, namely the time to recurrence or the occurrence of a sensory complication. This analysis was carried out by comparing the behavior of patients with the missing variable and those with the data present for the primary endpoint.
选择排除缺失数据的原始数据分析作为主要结果，因为缺失数据数量少，允许在验证数据完全随机缺失后简单排除缺失数据，这意味着缺失数据的存在与主要终点数据没有关联，即复发时间或发生感觉并发症。该分析是通过比较有缺失变量的患者和那些有主要终点数据的患者的行为来进行的。
Time to Pain Recurrence
The survival function and the median survival time were estimated with the Kaplan-Meier product-limit estimator. The median follow-up time was calculated with the reverse Kaplan-Meier method. The identification of factors predicting pain recurrence was performed with univariate Cox proportional hazards regression models. Only variables with < 20% of missing values were considered. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were estimated. The log-linearitassumption was investigated graphically for quantitative variables, which were recoded into categorical variables according to the median or quartiles if necessary. Factors associated with a p value < 0.20 were entered in a multivariate Cox model. A step-by-step selection procedure based on the Akaike information criterion (AIC) was used to select the variables of the final model. A predictive nomogram was drawn based on the set of predictors that were included in the final model to predict the risk of a pain recurrence at the median survival time.
使用Kaplan-Meier乘积限估计器估计生存函数和中位数生存时间。采用反向Kaplan-Meier法计算中位随访时间。采用单因素Cox比例风险回归模型来确定预测疼痛复发的因素。只考虑缺失值<20%的变量。评估危险比（HRs）及其相应的95%可信间隔（CIs）。对定量变量的对数线性假设进行了图形化研究，如有必要，则根据中位数或四分位数重新记录为分类变量。p值<0.20的相关因素输入到一个多变量Cox模型中。采用基于Akaike信息准则（AIC）的逐步选择程序来选择最终模型的变量。根据最终模型中包含的一组预测因子，绘制了一个预测列线图，以预测中位随访时间内疼痛复发的风险。
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