A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study

Objective To derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full-term neonate's risk level of having bacterial meningitis (BM). Methods A multicentered dataset was categorized into derivation (689 full-term neonates aged <= 28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed. Results In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C-reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm's sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%-98.9%) and 98.9% (95% CI 97.6%-99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%-100%) and 98.8% (95% CI 97.7%-100%). Interpretation The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP-related decision-making.

Automated summary

The study developed a sequential algorithm integrating clinical and laboratory parameters to stratify the risk of BM in neonates, showing excellent predictive performance and proving to be helpful for clinicians in decision-making related to LP.