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Published on: October 29, 2014
Diagnostic prediction models for bacterial meningitis in children with a suspected central nervous system infection:
Nina S Groeneveld1, Merijn W Bijlsma2, Ingeborg E van Zeggeren1
1Department of Neurology, Amsterdam UMC Location AMC, Amsterdam, The Netherlands.
Objectives:
Diagnostic prediction models exist to assess the probability of bacterial meningitis (BM) in paediatric patients with suspected meningitis. To evaluate the diagnostic accuracy of these models in a broad population of children suspected of a central nervous system (CNS) infection, we performed external validation.
Methods:
We performed a systematic literature review in Medline to identify articles on the development, refinement or validation of a prediction model for BM, and validated these models in a prospective cohort of children aged 0-18 years old suspected of a CNS infection.
Primary And Secondary Outcome Measures:
We calculated sensitivity, specificity, predictive values, the area under the receiver operating characteristic curve (AUC) and evaluated calibration of the models for diagnosis of BM.
Results:
In total, 23 prediction models were validated in a cohort of 450 patients suspected of a CNS infection included between 2012 and 2015. In 75 patients (17%), the final diagnosis was a CNS infection including 30 with BM (7%). AUCs ranged from 0.69 to 0.94 (median 0.83, interquartile range [IQR] 0.79-0.87) overall, from 0.74 to 0.96 (median 0.89, IQR 0.82-0.92) in children aged ≥28 days and from 0.58 to 0.91 (median 0.79, IQR 0.75-0.82) in neonates.
Conclusions:
Prediction models show good to excellent test characteristics for excluding BM in children and can be of help in the diagnostic workup of paediatric patients with a suspected CNS infection, but cannot replace a thorough history, physical examination and ancillary testing.
Insights
Diagnostic prediction models effectively help rule out bacterial meningitis (BM) in children with suspected central nervous system (CNS) infections. These models show good to excellent accuracy but should complement, not replace, clinical evaluation.
Area of Science:
- Pediatric infectious diseases
- Clinical epidemiology
- Diagnostic test evaluation
Background:
- Bacterial meningitis (BM) poses a significant risk in children with suspected central nervous system (CNS) infections.
- Existing diagnostic prediction models aim to assess the probability of BM in pediatric patients.
Purpose of the Study:
- To externally validate existing diagnostic prediction models for bacterial meningitis (BM) in a broad pediatric population.
- To evaluate the diagnostic accuracy and calibration of these models in children with suspected CNS infections.
Main Methods:
- Systematic literature review in Medline to identify relevant prediction models for BM.
- External validation of identified models in a prospective cohort of 450 children (0-18 years) with suspected CNS infections (2012-2015).
- Calculation of sensitivity, specificity, predictive values, AUC, and calibration assessment.
Main Results:
- 23 prediction models were validated. 17% of patients had CNS infections, with 7% diagnosed with BM.
- Overall AUCs ranged from 0.69 to 0.94 (median 0.83).
- Models demonstrated good to excellent performance, particularly in children aged ≥28 days (median AUC 0.89).
Conclusions:
- Diagnostic prediction models exhibit good to excellent test characteristics for excluding BM in pediatric patients.
- These models can aid in the diagnostic workup of suspected CNS infections in children.
- Models should be used in conjunction with thorough clinical assessment and ancillary testing, not as a replacement.
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