BMJ open · 2024
Diagnostic prediction models for bacterial meningitis in children with a suspected central nervous system infection: a systematic review and prospective validation study.
СтатьяПоддержка исследования: вне правительства СШАСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Groeneveld Nina S, Bijlsma Merijn W, van Zeggeren Ingeborg E, Staal Steven L, Tanck Michael W T, van de Beek Diederik, Brouwer Matthijs C
Аннотация
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.
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.