Multivariate approach to differential diagnosis of acute meningitis

Abstract

A previously reported statistical model based on a combination of four parameters (total polymorphonuclear cell count in cerebrospinal fluid (CSF), CSF/blood glucose ratio, age and month of onset) appeared effective in differentiating acute viral meningitis (AVM) from acute bacterial meningitis (ABM). The objectives of this study were to validate this model on a large independent sample of patients with acute meningitis and to build and validate a new model based on this sample. Of 500 consecutive cases of community-acquired meningitis reviewed retrospectively, 115 were ABM, 283 were AVM and 102 were of uncertain etiology. For each of the ABM and AVM cases, the probability of ABM versus AVM (pABM) was calculated for both models. Sensitivity, specificity and predictive values as well as areas under the receiver operating characteristic (ROC) curves were calculated for both models. The original model proved an accurate and reliable diagnostic test. Its area under the ROC curve was 0.981. For pABM=0.1, its negative and positive predictive values were 0.99 and 0.68, respectively. The new model retained four slightly different independent variables: CSF protein level, total CSF polymorphonuclear cell count, blood glucose level and leukocyte count. Its area under the ROC curve was 0.991 and, for pABM=0.1, its negative and positive predictive values were 0.99 and 0.85, respectively. In conclusion, both models provide a valuable aid in differentiating AVM from ABM. They should be further evaluated in a prospective appraisal of their contribution to therapeutic decision making.