Nigrovic Lise E, Kuppermann Nathan, Malley Richard
Department of Medicine, Children's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
Pediatrics. 2002 Oct;110(4):712-9. doi: 10.1542/peds.110.4.712.
Children with meningitis are routinely admitted to the hospital and administered broad-spectrum antibiotics pending culture results because distinguishing bacterial meningitis from aseptic meningitis is often difficult.
To develop and validate a simple multivariable model to distinguish bacterial meningitis from aseptic meningitis in children using objective parameters available at the time of patient presentation.
Retrospective cohort study of all children with meningitis admitted to 1 urban children's hospital from July 1992 through June 2000, randomly divided into derivation (66%) and validation sets (34%).
Six hundred ninety-six previously healthy children aged 29 days to 19 years, of whom 125 (18%) had bacterial meningitis and 571 (82%) had aseptic meningitis.
Multivariable logistic regression and recursive partitioning analyses identified the following predictors of bacterial meningitis from the derivation set: Gram stain of cerebrospinal fluid (CSF) showing bacteria, CSF protein > or =80 mg/dL, peripheral absolute neutrophil count > or =10 000 cells/mm3, seizure before or at time of presentation, and CSF absolute neutrophil count > or =1000 cells/mm3. A Bacterial Meningitis Score (BMS) was developed on the derivation set by attributing 2 points for a positive Gram stain and 1 point for each of the other variables.
The accuracy of the BMS when applied to the validation set.
A BMS of 0 accurately identified patients with aseptic meningitis without misclassifying any child with bacterial meningitis in the validation set. The negative predictive value of a score of 0 for bacterial meningitis was 100% (95% confidence interval: 97%-100%). A BMS > or =2 predicted bacterial meningitis with a sensitivity of 87% (95% confidence interval: 72%-96%).
The BMS accurately identifies children at low (BMS = 0) or high (BMS > or =2) risk of bacterial meningitis. Outpatient management may be considered for children in the low-risk group.
由于区分细菌性脑膜炎和无菌性脑膜炎通常很困难，患有脑膜炎的儿童通常会被收治入院，并在培养结果出来之前使用广谱抗生素。
利用患者就诊时可用的客观参数，开发并验证一个简单的多变量模型，以区分儿童细菌性脑膜炎和无菌性脑膜炎。
对1992年7月至2000年6月期间入住一家城市儿童医院的所有脑膜炎患儿进行回顾性队列研究，随机分为推导组（66%）和验证组（34%）。
696名年龄在29天至19岁之间的既往健康儿童，其中125名（18%）患有细菌性脑膜炎，571名（82%）患有无菌性脑膜炎。
多变量逻辑回归和递归划分分析从推导组中确定了以下细菌性脑膜炎的预测因素：脑脊液（CSF）革兰氏染色显示细菌、CSF蛋白≥80mg/dL、外周绝对中性粒细胞计数≥10000个/mm³、就诊前或就诊时癫痫发作以及CSF绝对中性粒细胞计数≥1000个/mm³。通过对革兰氏染色阳性赋予2分，对其他每个变量赋予1分，在推导组上制定了细菌性脑膜炎评分（BMS）。
BMS应用于验证组时的准确性。
BMS为0准确识别了无菌性脑膜炎患者，在验证组中没有将任何细菌性脑膜炎患儿误分类。BMS评分为0对细菌性脑膜炎的阴性预测值为100%（95%置信区间：97%-100%）。BMS≥2预测细菌性脑膜炎的敏感性为87%（9置信区间：72%-96%）。
BMS准确识别出细菌性脑膜炎低风险（BMS = 0）或高风险（BMS≥2）的儿童。对于低风险组的儿童，可以考虑门诊管理。
