Department of Neurosurgery, Chengdu Fifth People's Hospital/Affiliated Chengdu No.5 People's Hospital of Chengdu University of TCM, Chengdu, 611130, China.
Department of Infection Control, Chengdu Fifth People's Hospital/Affiliated Chengdu No.5 People's Hospital of Chengdu University of TCM, Chengdu, 611130, China.
Neurocrit Care. 2021 Apr;34(2):557-565. doi: 10.1007/s12028-020-01076-1. Epub 2020 Aug 10.
Lumbar drainage (LD) is one of the common treatment techniques in neurosurgery. There is a risk of secondary meningitis when using this modality. We aim to predict the probability of the complication by designing a nomogram.
A retrospective study was conducted in a teaching hospital. Data were collected and LD-related meningitis (LDRM) was identified, mainly based on clinical manifestations and cerebrospinal fluid analysis. Univariate analysis was used to screen the risk factors, and binary logistic analysis was performed to build the prediction model, which was furtherly transferred into a nomogram. The prediction performance was evaluated by receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, and nomogram calibration plot. Internal validation was processed by using ordinary bootstrapping.
A total of 273 patients who match the research criteria were enrolled, in which 37 cases (13.6%) were confirmed to have LDRM. Univariate analysis showed the risk factors included diabetes (p = 0.003), admission on surgical intensive care unit (p = 0.012), duration time (p < 0.001), site leakage (p < 0.001), and craniotomy (p < 0.001). In multivariate analysis, four of the variables were identified as independent risk factors to establish a prediction model, and a graphical nomogram was designed. The area under the ROC curve was 0.837, and the p value in the Hosmer-Lemeshow test was 0.610, with a mean absolute error in the calibration plot calculated as 0.022. The indices in the testing set were in good accordance with the original set when internal validation was performed.
This is the first study to transform the prediction model of LDRM into a nomogram, which can be considered as a tool for clinicians to assess infection risk.
腰椎引流（LD）是神经外科中常见的治疗技术之一。使用这种方式存在继发脑膜炎的风险。我们旨在通过设计列线图来预测这种并发症的概率。
在一家教学医院进行回顾性研究。主要基于临床表现和脑脊液分析来收集数据并识别 LD 相关脑膜炎（LDRM）。使用单因素分析筛选危险因素，进行二项逻辑回归分析建立预测模型，进一步转化为列线图。通过接受者操作特征（ROC）曲线、Hosmer-Lemeshow 检验和列线图校准图评估预测性能。使用普通bootstrap 进行内部验证。
共纳入符合研究标准的 273 例患者，其中 37 例（13.6%）被确诊为 LDRM。单因素分析显示，危险因素包括糖尿病（p=0.003）、入住外科重症监护病房（p=0.012）、时间持续时间（p<0.001）、部位漏液（p<0.001）和开颅术（p<0.001）。多因素分析确定了四个变量为独立危险因素，建立了预测模型，并设计了图形列线图。ROC 曲线下面积为 0.837，Hosmer-Lemeshow 检验的 p 值为 0.610，校准图的平均绝对误差计算为 0.022。内部验证时，测试集的指标与原始集吻合良好。
这是首次将 LDRM 的预测模型转化为列线图的研究，可以作为评估感染风险的临床工具。
