title: Performance of a predictive rule to distinguish bacterial and viral meningitis.
abstract: OBJECTIVE
  Although diagnostic performance has recently improved by using new diagnostic methodologies, acute patient management is usually initiated after considering only fairly elementary findings of CSF examination. Using these early findings it is often difficult to distinguish between bacterial and aseptic (viral) meningitis. In order to help distinguish these two categories, scoring tools have been proposed that are more or less complex and validated.
  
  
  METHODS
  The aim of this study was to establish a simple scoring tool and compare it to other available decision making systems. We retrospectively analysed all the meningitis cases from our patients at our institution and established a scoring tool for pediatric meningitis and for meningitis in adults by using categorized analysis tree methodology.
  
  
  RESULTS
  Main categories for bacterial etiology were, leucocytosis >15 giga, CSF leucocytes count >1700 per ml, CSF neutrophil percentage >80, CSF protein >2.3g/l and glucose CSF/blood ratio <0.33 for adults and CSF leucocytes count >1800, CSF neutrophil percentage >80, CSF protein >1.2g/l and glucose CSF/blood ratio <0.3 for children. Additionally, our new scoring tool and five published ones were compared using our data and two external data sets; from these scores, three, including ours, exhibited good sensitivity and specificity. We then performed several thousand Monte Carlo simulations of both bacterial and viral meningitis for children and adults. We found that our scoring tool (Meningitest) had very high performances with positive and negative predictive values of 97% and 94%, respectively.
  
  
  CONCLUSIONS
  Thus, from this analysis of five meningitis scoring systems, we believe that our new tool is simple, does not need any complex calculation and is effective in identifying bacterial vs viral meningitis in fully immunocompetent children and adults.
authors.affiliation: Service des Maladies Infectieuses, EA 652, University Hospital, Hopital du Bocage, 21000 Dijon, France. pascal.chavanet@chu-dijon.fr <pascal.chavanet@chu-dijon.fr>
authors.forename: Pascal
authors.initials: P
authors.lastname: Chavanet
authors.forename: Céline
authors.initials: C
authors.lastname: Schaller
authors.forename: Corine
authors.initials: C
authors.lastname: Levy
authors.forename: Juan
authors.initials: J
authors.lastname: Flores-Cordero
authors.forename: Max
authors.initials: M
authors.lastname: Arens
authors.forename: Lionel
authors.initials: L
authors.lastname: Piroth
authors.forename: Edouard
authors.initials: E
authors.lastname: Bingen
authors.forename: Henri
authors.initials: H
authors.lastname: Portier
mesh_terms: D000328:Adult; D002648:Child; D002675:Child, Preschool; D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D006801:Humans; D007964:Leukocytosis; D008582:Meningitis, Aseptic / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D016920:Meningitis, Bacterial / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000503:physiopathology; D008587:Meningitis, Viral / Q000134:cerebrospinal fluid / Q000175:diagnosis* / Q000503:physiopathology; D008875:Middle Aged; D009010:Monte Carlo Method; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity