Diagnostic Utility of Cerebrospinal Fluid White Blood Cell Components for the Identification of Bacterial Meningitis in Infants

Background To evaluate the diagnostic and predictive utility of cerebrospinal fluid (CSF) white blood cell (WBC) components in the diagnosis of bacterial meningitis in infants discharged from the neonatal intensive care unit (NICU).Methods We identified a cohort of infants discharged from a Pediatrix NICU between 1997 and 2020 who did not have an immunodeficiency, had at least 1 CSF culture collected within the first 120 days of life, and at least 1 CSF laboratory specimen obtained on the day of culture collection. We only included an infant's first CSF culture and excluded cultures from CSF reservoirs and those growing contaminants or nonbacterial organisms. We examined the utility of CSF WBC components to diagnose or predict bacterial meningitis by calculating sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating curve (AUC) at different cutoff values for each parameter. We performed subgroup analysis excluding infants treated with antibiotics the day before CSF culture collection.Results Of the 20 756 infants that met the study inclusion criteria, 320 (2%) were diagnosed with bacterial meningitis. We found (AUC [95% CI]) CSF WBC count (0.76 [0.73-0.79]), CSF neutrophil count (0.74 [0.70-0.78]), and CSF neutrophil percent (0.71 [0.67-0.75]) had the highest predictive values for bacterial meningitis, even when excluding infants with early antibiotic administration.Conclusions No single clinical prediction rule had the optimal discriminatory power for predicting culture-proven bacterial meningitis, and clinicians should be cautious when interpreting CSF WBC parameters in infants with suspected meningitis. This study evaluated the diagnostic utility of CSF WBC components in infants with suspected bacterial meningitis. CSF WBC count, neutrophil count, and neutrophil percent showed the highest predictive value. No single clinical prediction rule achieved optimal discriminatory power.