CONTEXT: Children with meningitis are routinely admitted to the hospital and administered broad-spectrum antibiotics pending culture results because distinguishing bacterial meningitis from aseptic meningitis is often difficult. OBJECTIVE: To develop and validate a simple multivariable model to distinguish bacterial meningitis from aseptic meningitis in children using objective parameters available at the time of patient presentation. DESIGN: Retrospective cohort study of all children with meningitis admitted to 1 urban children's hospital from July 1992 through June 2000, randomly divided into derivation (66%) and validation sets (34%). PATIENTS: Six hundred ninety-six previously healthy children aged 29 days to 19 years, of whom 125 (18%) had bacterial meningitis and 571 (82%) had aseptic meningitis. INTERVENTION: Multivariable logistic regression and recursive partitioning analyses identified the following predictors of bacterial meningitis from the derivation set: Gram stain of cerebrospinal fluid (CSF) showing bacteria, CSF protein > or =80 mg/dL, peripheral absolute neutrophil count > or =10 000 cells/mm3, seizure before or at time of presentation, and CSF absolute neutrophil count > or =1000 cells/mm3. A Bacterial Meningitis Score (BMS) was developed on the derivation set by attributing 2 points for a positive Gram stain and 1 point for each of the other variables. MAIN OUTCOME MEASURE: The accuracy of the BMS when applied to the validation set. RESULTS: A BMS of 0 accurately identified patients with aseptic meningitis without misclassifying any child with bacterial meningitis in the validation set. The negative predictive value of a score of 0 for bacterial meningitis was 100% (95% confidence interval: 97%-100%). A BMS > or =2 predicted bacterial meningitis with a sensitivity of 87% (95% confidence interval: 72%-96%). CONCLUSIONS: The BMS accurately identifies children at low (BMS = 0) or high (BMS > or =2) risk of bacterial meningitis. Outpatient management may be considered for children in the low-risk group.
CONTEXT: Children with meningitis are routinely admitted to the hospital and administered broad-spectrum antibiotics pending culture results because distinguishing bacterial meningitis from aseptic meningitis is often difficult. OBJECTIVE: To develop and validate a simple multivariable model to distinguish bacterial meningitis from aseptic meningitis in children using objective parameters available at the time of patient presentation. DESIGN: Retrospective cohort study of all children with meningitis admitted to 1 urban children's hospital from July 1992 through June 2000, randomly divided into derivation (66%) and validation sets (34%). PATIENTS: Six hundred ninety-six previously healthy children aged 29 days to 19 years, of whom 125 (18%) had bacterial meningitis and 571 (82%) had aseptic meningitis. INTERVENTION: Multivariable logistic regression and recursive partitioning analyses identified the following predictors of bacterial meningitis from the derivation set: Gram stain of cerebrospinal fluid (CSF) showing bacteria, CSF protein > or =80 mg/dL, peripheral absolute neutrophil count > or =10 000 cells/mm3, seizure before or at time of presentation, and CSF absolute neutrophil count > or =1000 cells/mm3. A Bacterial Meningitis Score (BMS) was developed on the derivation set by attributing 2 points for a positive Gram stain and 1 point for each of the other variables. MAIN OUTCOME MEASURE: The accuracy of the BMS when applied to the validation set. RESULTS: A BMS of 0 accurately identified patients with aseptic meningitis without misclassifying any child with bacterial meningitis in the validation set. The negative predictive value of a score of 0 for bacterial meningitis was 100% (95% confidence interval: 97%-100%). A BMS > or =2 predicted bacterial meningitis with a sensitivity of 87% (95% confidence interval: 72%-96%). CONCLUSIONS: The BMS accurately identifies children at low (BMS = 0) or high (BMS > or =2) risk of bacterial meningitis. Outpatient management may be considered for children in the low-risk group.
Authors: François G Brivet; Sophie Ducuing; Frédéric Jacobs; Isabelle Chary; Roger Pompier; Dominique Prat; Bogdan D Grigoriu; Patrice Nordmann Journal: Intensive Care Med Date: 2005-10-22 Impact factor: 17.440
Authors: Andrew M Fine; Lise E Nigrovic; Ben Y Reis; E Francis Cook; Kenneth D Mandl Journal: J Am Med Inform Assoc Date: 2007-01-09 Impact factor: 4.497
Authors: J Bishara; N Hadari; M Shalita-Chesner; Z Samra; O Ofir; M Paul; N Peled; S Pitlik; Y Molad Journal: Eur J Clin Microbiol Infect Dis Date: 2007-09 Impact factor: 3.267
Authors: Rianne Oostenbrink; Karel G M Moons; Carl G M Moons; Arda G Derksen-Lubsen; Diederick E Grobbee; Henriette A Moll Journal: Eur J Epidemiol Date: 2004 Impact factor: 8.082
Authors: Rodrigo Hasbun; Merijn Bijlsma; Matthijs C Brouwer; Nabil Khoury; Christiane M Hadi; Arie van der Ende; Susan H Wootton; Lucrecia Salazar; Md Monir Hossain; Mark Beilke; Diederik van de Beek Journal: J Infect Date: 2013-04-22 Impact factor: 6.072