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Systematic review and validation of diagnostic prediction models in patients suspected of meningitis
Ingeborg E van Zeggeren1, Merijn W Bijlsma1, Michael W Tanck2
1Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, the Netherlands.
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.
Insights
Existing diagnostic models for bacterial meningitis (BM) show variable accuracy. External validation is crucial, but current models are not recommended for routine use in suspected central nervous system (CNS) infections.
Area of Science:
- Neurology
- Infectious Diseases
- Clinical Diagnostics
Background:
- Diagnostic prediction models aid in assessing bacterial meningitis (BM) risk for central nervous system (CNS) infections.
- External validation is critical for determining the diagnostic accuracy of these models.
Purpose of the Study:
- To prospectively evaluate the diagnostic performance of existing bacterial meningitis prediction models in a clinical cohort.
- To determine the sensitivity, specificity, and predictive values of these models.
Main Methods:
- Prospective inclusion of patients undergoing lumbar puncture for suspected CNS infection.
- Systematic literature review to identify BM prediction models.
- Application and evaluation of identified models using sensitivity, specificity, AUC, and calibration analysis.
Main Results:
- Seventeen BM prediction models were evaluated in 363 patient episodes (27 with BM).
- Model sensitivity ranged from 37% to 100%, and specificity from 44% to 99%.
- The Oostenbrink cerebrospinal fluid model achieved the highest AUC (0.95), though calibration issues were noted across all models.
Conclusions:
- No current prediction model demonstrated sufficient performance for routine clinical use in managing individual patients.
- Future research should investigate the discrepancy between model accuracy and physician decision-making.
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