OBJECTIVES Diagnostic prediction models have been developed to assess the likelihood of bacterial meningitis (BM) in patients presented with suspected central nervous system (CNS) infection. External validation in patients suspected of meningitis is essential to determine the diagnostic accuracy of these models. METHODS We prospectively included patients who underwent a lumbar puncture for suspected CNS infection. After a systematic review of the literature, we applied identified models for BM to our cohort. We calculated sensitivity, specificity, predictive values, area under the curve (AUC) and, if possible, we evaluated the calibration of the models. RESULTS From 2012-2015 we included 363 episodes. In 89 (24%) episodes, the patient received a final diagnosis of a CNS infection, of whom 27 had BM. Seventeen prediction models for BM were identified. Sensitivity of these models ranged from 37% to 100%. Specificity of these models ranged from 44% to 99%. The cerebrospinal fluid model of Oostenbrink reached the highest AUC of 0.95 (95% CI 0.91-0.997). Calibration showed over- or underestimation in all models. CONCLUSION None of the existing models performed well enough to recommend as routine use in individual patient management. Future research should focus on differences between diagnostic accuracy of the prediction models and physician's therapeutic decisions.

中文翻译：

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对怀疑为脑膜炎的患者的诊断预测模型进行系统的审查和验证。

目的已开发出诊断预测模型，以评估可疑中枢神经系统（CNS）感染患者中细菌性脑膜炎（BM）的可能性。对怀疑为脑膜炎的患者进行外部验证对于确定这些模型的诊断准确性至关重要。方法我们前瞻性地纳入了因怀疑中枢神经系统感染而接受腰椎穿刺的患者。在对文献进行系统回顾之后，我们将确定的BM模型应用到我们的队列中。我们计算了灵敏度，特异性，预测值，曲线下面积（AUC），并在可能的情况下评估了模型的校准。结果从2012年至2015年，我们收录了363集。在89次（24％）发作中，该患者接受了CNS感染的最终诊断，其中27例患有BM。确定了17种BM预测模型。这些模型的灵敏度范围从37％到100％。这些模型的特异性范围为44％至99％。Oostenbrink的脑脊髓液模型的最高AUC为0.95（95％CI 0.91-0.997）。在所有模型中，校准均显示过高或过低。结论现有的模型都没有表现良好，无法推荐作为个人患者管理中的常规使用。未来的研究应关注预测模型的诊断准确性与医师的治疗决策之间的差异。结论现有的模型都没有表现良好，无法推荐作为个人患者管理中的常规使用。未来的研究应关注预测模型的诊断准确性与医师的治疗决策之间的差异。结论现有的模型都没有表现良好，无法推荐作为个人患者管理中的常规使用。未来的研究应关注预测模型的诊断准确性与医师的治疗决策之间的差异。