Tokuda Yasuharu, Koizumi Masahiro, Stein Gerald H, Birrer Richard B
Center for Clinical Epidemiology, St. Luke's Life Science Institute, Tokyo, Japan.
Intern Med. 2009;48(7):537-43. doi: 10.2169/internalmedicine.48.1832. Epub 2009 Apr 1.
To derive and validate a clinical prediction model with high sensitivity for differentiating aseptic meningitis (AM) patients from bacterial meningitis (BM) patients.
We developed the model using the derivation cohort in a community rural hospital in Okinawa and assessed its performance using the validation cohort in a metropolitan urban hospital in Tokyo. There were 66 (39.5%) and 5 (17.9%) adult patients with BM among the derivation (n=167) and the validation cohort (n=28), respectively. Recursive partitioning analysis was used to determine the important classification variables and to develop a sensitive model to safely exclude BM.
The model produced high- and low-risk groups based on the following: 1) Gram stain, 2) CSF neutrophil percent < or =15%, 3) CSF neutrophil count < or =150 cells/mm(3), and, 4) mental status change. Among the derivation cohort, there were 65 patients with BM in the high-risk group (n=76), while only one patient with BM was noted (sensitivity, 99%) in the low-risk group (n=91). Among the validation cohort, there were 5 patients with BM in the high-risk group (n=7), while no patient was classified with BM (sensitivity, 100%) in the low-risk group (n=21).
This simple and sensitive model might be useful to safely identify low-risk patients for BM who would not require antibiotic treatment.
推导并验证一种对区分无菌性脑膜炎（AM）患者和细菌性脑膜炎（BM）患者具有高敏感性的临床预测模型。
我们使用冲绳一家社区乡村医院的推导队列开发了该模型，并使用东京一家大都市城市医院的验证队列评估其性能。在推导队列（n = 167）和验证队列（n = 28）中，分别有66名（39.5%）和5名（17.9%）成年BM患者。采用递归划分分析来确定重要的分类变量，并开发一个敏感模型以安全地排除BM。
该模型基于以下因素产生高风险和低风险组：1）革兰氏染色，2）脑脊液中性粒细胞百分比≤15%，3）脑脊液中性粒细胞计数≤150个细胞/mm³，以及4）精神状态改变。在推导队列中，高风险组（n = 76）有65名BM患者，而低风险组（n = 91）仅发现1名BM患者（敏感性，99%）。在验证队列中，高风险组（n = 7）有5名BM患者，而低风险组（n = 21）没有患者被归类为BM（敏感性，100%）。
这个简单且敏感的模型可能有助于安全地识别不需要抗生素治疗的低风险BM患者。
