A Decision Rule for Predicting Bacterial Meningitis in Children with Cerebrospinal Fluid Pleocytosis When Gram Stain Is Negative or Unavailable


  • Bema K. Bonsu MBChB,

    1. From the Department of Pediatrics, Division of Emergency Medicine (BKB, HWO), and the Department of Laboratory Medicine (MJM), Columbus Children’s Hospital, Columbus, OH; the Division of Emergency Medicine, Children’s Hospitals and Clinics of Minnesota (HWO), Minneapolis, MN; and the Divisions of Emergency Medicine and Infectious Diseases, Children’s Hospital Boston (MBH), Boston, MA.
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  • Henry W. Ortega MD,

    1. From the Department of Pediatrics, Division of Emergency Medicine (BKB, HWO), and the Department of Laboratory Medicine (MJM), Columbus Children’s Hospital, Columbus, OH; the Division of Emergency Medicine, Children’s Hospitals and Clinics of Minnesota (HWO), Minneapolis, MN; and the Divisions of Emergency Medicine and Infectious Diseases, Children’s Hospital Boston (MBH), Boston, MA.
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  • Mario J. Marcon PhD,

    1. From the Department of Pediatrics, Division of Emergency Medicine (BKB, HWO), and the Department of Laboratory Medicine (MJM), Columbus Children’s Hospital, Columbus, OH; the Division of Emergency Medicine, Children’s Hospitals and Clinics of Minnesota (HWO), Minneapolis, MN; and the Divisions of Emergency Medicine and Infectious Diseases, Children’s Hospital Boston (MBH), Boston, MA.
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  • Marvin B. Harper MD

    1. From the Department of Pediatrics, Division of Emergency Medicine (BKB, HWO), and the Department of Laboratory Medicine (MJM), Columbus Children’s Hospital, Columbus, OH; the Division of Emergency Medicine, Children’s Hospitals and Clinics of Minnesota (HWO), Minneapolis, MN; and the Divisions of Emergency Medicine and Infectious Diseases, Children’s Hospital Boston (MBH), Boston, MA.
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  • Presented at the annual meeting of the Pediatric Academic Societies, San Francisco, CA, May 2004.

Bema K. Bonsu, MBChB, e-mail: bonsub@pediatrics.ohio-state.edu.


Objectives:  Among children with cerebrospinal fluid (CSF) pleocytosis, the task of separating aseptic from bacterial meningitis is hampered when the CSF Gram stain result is unavailable, delayed, or negative. In this study, the authors derive and validate a clinical decision rule for use in this setting.

Methods:  This was a review of peripheral blood and CSF test results from 78 children (<19 years) presenting to Children’s Hospital Columbus from 1998 to 2002. For those with a CSF leukocyte count of >7/μL, a rule was created for separating bacterial from viral meningitis that was based on routine laboratory tests, but excluded Gram stain. The rule was validated in 158 subjects seen at the same site (Columbus, 2002–2004) and in 871 subjects selected from a separate site (Boston, 1993–1999).

Results:  One point each (maximum, 6 points) was assigned for leukocytes >597/μL, neutrophils >74%, glucose <38 mg/dL, and protein >97 mg/dL in CSF and for leukocytes >17,000/mL and bands to neutrophils >11% in peripheral blood. Areas under receiver-operator-characteristic curves (AROCs) for the resultant score were 0.98 for the derivation set and 0.90 and 0.97, respectively, for validation sets from Columbus and Boston. Sensitivity and specificity pairs for the Boston data set were 100 and 44%, respectively, at a score of 0 and 97 and 81% at a score of 1. Likelihood ratios (LRs) increased from 0 at a score of 0 to 40 at a score of ≥4.

Conclusions:  Among children with CSF pleocytosis, a prediction score based on common tests of CSF and peripheral blood and intended for children with unavailable, negative, or delayed CSF Gram stain results has value for diagnosing bacterial meningitis.

The decline in acute bacterial meningitis brought about by the introduction in the 1990s of a conjugate vaccine for Haemophilus influenzae Type b and more recently by the introduction of a heptavalent vaccine for Streptococcus pneumoniae means that most children who present with clinical signs of meningitis and cerebrospinal fluid (CSF) pleocytosis will be diagnosed with aseptic meningitis.1,2 Because aseptic meningitis is often self-limited, the challenge to clinicians is how to strengthen decisions to discharge to home while identifying the small number of children with acute bacterial meningitis.

A positive CSF Gram stain result eases this challenge greatly because it strongly supports (apart from other screening laboratory tests) a diagnosis of bacterial meningitis and so mandates empiric antibiotic therapy and hospitalization. Unfortunately, the ability to make such decisions is severely hampered when this test is negative or unavailable, as occurs at sites (e.g., community hospitals) that may only offer Gram staining at specific intervals of the day. A negative Gram stain result, at a false-negative rate of ∼40% and a true-negative rate of ∼100%, translates to a clinically trivial negative likelihood ratio (LR) of 0.4, barely altering the odds of bacterial meningitis.3–6 In this scenario, but equally when a report of the CSF Gram stain is unavailable or delayed, alternative tools are needed not only to break the diagnostic impasse but also to inform decision-making regarding initial antibiotic treatment, which, even when lagging a mere few hours, is linked to adverse neurologic outcomes.7–11

A number of extant tools are reported for aiding the task of diagnosing meningitis.3,12–19 Of these, only two are validated by independent investigators to be accurate for diagnosing bacterial meningitis in children other than those from which they were derived.20 These include the bacterial meningitis score (BMS) by Nigrovic et al.3,13 and a fractional polynomial model by Bonsu and Harper.12 The BMS, a weighted tally of five predictors, is well established and simple to calculate at the bedside; however, it relies heavily on the CSF Gram stain result and so is limited when this test is delayed or unavailable. The model by Bonsu and Harper, too, while operational with or without the spinal fluid Gram stain result, is impractical at sites that lack computerized decision support capability or ready access to preprogrammable probability calculators because it requires substantial mathematical computation.12,20

A decision rule that combines the strengths of these tools, namely, independence from the CSF Gram stain and ease of use at the bedside, is likely to support decision-making in a wider array of clinical settings. In this study, we provide a report of one such rule. This new rule, which complements rather than supplants existing rules, is intended to support decision-making in children evaluated for signs of meningitis in the emergency department (ED) when the CSF Gram stain result is negative, delayed, or unavailable.


Study Design

Our study is a retrospective cohort analysis of laboratory test results obtained from previously healthy children with ages ranging from 4 weeks to 18 years who presented to the ED with signs of acute meningitis. Data were obtained from patient information archives at Columbus Children’s Hospital and Children’s Hospital Boston after approval by institutional review boards at both hospitals. Eligible subjects at Columbus Children’s Hospital were children diagnosed with acute viral meningitis or acute bacterial meningitis from June 1998 to June 2004. Eligible subjects at Children’s Hospital Boston were children who underwent lumbar puncture from June 1992 to July 1999 for signs of acute community-acquired meningitis.

Study Setting and Population

Both institutions are academic children’s hospitals with EDs that serve diverse socioeconomic populations. At Columbus Children’s Hospital, there are approximately 90,000 ED patient visits per year, of which 53% are by whites, 36% are by African Americans, and 2% are by Hispanics. Other groups, including those declining to comment on their race, constitute 9% of children seen at the hospital. At Children’s Hospital Boston there are approximately 50,000 ED patient visits per year, of which 35% are by whites, 25% are by African Americans, and 20% are by Hispanics. Other groups constitute 20% of children treated at the hospital.

Study Protocol

We reviewed, at both sites, laboratory test results of children evaluated for acute infectious meningitis. Data were entered into a database and included the white blood cell (WBC) count in peripheral blood and CSF; glucose, total protein, bacterial, and viral culture results of CSF specimens; polymerase chain reaction (PCR) results of CSF and throat specimens (performed routinely from late spring to early fall at Columbus Children’s Hospital); and viral culture results of throat specimens. At each site, data were linked by the subject’s medical record number, a unique ED encounter number, and the date and time of encounter. We excluded children who did not have CSF pleocytosis (CSF leukocyte count <8 cells/μL) as well as those with blood-contaminated CSF (>10,000 erythrocytes/μL) 12,18,20 and, for some analyses, missing results of selected tests.

Predictor Selection.  To create the clinical score, we relied on data obtained from Columbus Children’s Hospital for the interval June 1998 to February 2002 (derivation set). Operationally, healthy subjects were defined by excluding from all data sets children with International Classification of Diseases (ICD)-9–coded diagnoses of neurosurgical, noninfectious neurologic, and medical disease suggesting chronic poor health clinically recognizable at the initial encounter.

Bacterial meningitis was coded to be present if standard culture of the CSF specimen isolated a pathogen known to be a typical cause of this infection in an otherwise healthy child. A diagnosis of bacterial meningitis superseded a diagnosis of viral meningitis, irrespective of viral study results (no such case occurred). The following bacteria grown from culture of CSF were classified as contaminants and were removed from the dataset: Viridans streptococci, Streptococcus bovis, Staphylococcus epidermidis, Staphylococcus aureus, Enterobacter spp., Corynebacterium spp., Bacillus spp., Flavobaterium spp., and Enterococcus spp. For score creation, children for whom CSF did not grow a bacterial pathogen or for whom an enterovirus could not be identified were excluded. Viral meningitis was coded to be present if culture or PCR of CSF or throat specimens identified an enterovirus in a child with greater than seven WBCs/μL in CSF. We reasoned that the score had a good chance of generalizing to other settings if it was able to distinguish between bacterial and viral meningitis, diseases that are close in clinicopathologic spectrum and, therefore most difficult to separate clinically.

Predictors chosen for the score were tests commonly used for diagnosis. These included the WBC concentration and ratio of bands to neutrophils (a surrogate for a “left-shifted” WBC differential) in peripheral blood; and the WBC concentration, total protein, total glucose, and the percentage neutrophils in CSF. The CSF Gram stain result was not considered because: 1) a positive test result denotes the presence of bacteria, which by itself indicates bacterial meningitis, and 2) a negative Gram stain result barely alters the prior odds of bacterial meningitis, so from a practical standpoint presents a dilemma similar to that observed when the CSF Gram stain report is delayed or absent, i.e., creates the need to rely on other tests for diagnosis and treatment.

Data Analysis

We identified optimal cut-points for each selected predictor based on recursive partitioning, a technique that uses nonparametric bivariate analysis to partition samples into subsets with the lowest probability of false-negative and false-positive assignments with respect to a given outcome.21,22 Sample partitioning continues recursively with the option of penalizing misdiagnosis depending on the seriousness of misclassification or to enhance the performance of tests in the set used to derive the rule.

There is modest risk with recursive partitioning of overfitting the observed data, leading to poor performance in new data sets. It is also established that the usefulness of particular tests for predicting bacterial meningitis varies between patients. A hierarchical procedure like recursive partitioning, therefore, that orders tests sequentially based on their relative performance in a particular group of patients may translate poorly to new patients among whom other tests predominate. Specifically, tests positioned early in the hierarchy of a tree may be demoted when evaluated in new data sets. Even slightly altering observed data sets can sometimes lead to major changes in tree structure.21,22

For these reasons, and because CSF cell counts and chemistries are generally assigned equal or nearly equal weight for diagnosis, we adopted a rule that utilizes these tests in parallel rather than in sequence. To do this, we evaluated each test in a separate recursive partitioning tree, and, for robustness, chose test thresholds at the first branch. Overfitting was lowered by avoiding penalization for misclassification. Test thresholds so selected were aggregated into a prediction rule that gave equal weight to each test for determining whether a particular child had bacterial meningitis. One or zero points were assigned to each test (base classifier) depending on whether values for that test fell within or outside the selected intervals. A simple aggregation of base classifiers for a maximum of 6 points was expected to enhance the accuracy of the score in future data sets without increasing its complexity inordinately. Recursive partitioning was conducted with the RPART program of the R statistical analysis environment (http://www.r-project.org).

Validation.  To validate our score, we followed recommendations proposed in recent reviews.23–25 We first estimated the accuracy of the score in the data set from which it was derived (internal validation). This step was followed by external validation of the score in a data set assembled at the same site, but from a different time interval (temporal transportability), and in a data set assembled from a different site and time interval (geographic transportability). By using slightly different inclusion criteria for parts of our analysis, we also tested the methodologic transportability of our score. This scheme tested the robustness (statistical and clinical invariance) of our score to these factors.

Internal Validation of Score.  Internal validation of the model was achieved by bootstrapping.21,22 The bootstrap procedure generates new data sets of the same size as the observed data set by resampling from it with replacement. Diagnostic performance is adjusted for overoptimism (bias) by averaging from generated samples. The procedure was repeated 5,000 times. The area under the receiver-operating-characteristic curve (AROC), a common index of test performance, was estimated for the prediction score.26,27 The ROC curve plots sensitivity versus 1-specificity (LR) for ordered values of a test providing an estimate of global diagnostic capability. The higher the AROC, the more favorable the tradeoff is between sensitivity and specificity.28 An AROC of 0.5 indicates that a diagnosis model has no discriminating ability, whereas an AROC value of 1.0 indicates perfect discriminating ability. We adopted a nominal value of 0.7 to be the threshold for stating a model discriminated between bacterial and viral meningitis.29

We also calculated sensitivity, specificity, and interval LRs at a number of thresholds of the score.30 Because of their discrete nature, we considered each threshold of our prediction score to be an interval when calculating LRs. Based on published recommendations and cognizant of serious complications of missed bacterial meningitis, we adopted the following convention for interpreting LRs: LRs of <0.5 lower the odds of infection (markedly, if <0.1); LRs of >2 increase the odds of infection (markedly, if >10); and LRs from 0.5 to 2 do not alter the odds of infection.31

External Validation.  Our score was validated in two sets of children. The first was a previously assembled group of patients with a leukocyte count in CSF of >9/μL seen at Columbus Children’s Hospital from March 2002 to June 2004 (the small difference in thresholds for CSF leukocytes was considered insignificant). Because this sample was small, we did not exclude subjects who had incomplete reporting of the laboratory tests. Instead, the maximum score attained based on available test results was substituted for the full score.

The second validation set was children with a leukocyte count in CSF of >7/μL seen at Children’s Hospital Boston from 1993 to 1999. Criteria for diagnosing acute bacterial meningitis at this site were unchanged from those specified for children at Columbus Children’s Hospital. Because this sample was larger (see Results), we were able to restrict our analysis to subjects who had full reporting of laboratory test results.

It should be noted that controls chosen for external validation of the rule had wider clinical distribution than those chosen for creating the rule. Specifically, for external validation, controls included every eligible subject with CSF pleocytosis not assigned a discharge diagnosis of bacterial meningitis and irrespective of viral culture and PCR findings of CSF. By choosing this expanded definition, we were able to validate the accuracy and transportability of the prediction score in a more general sample of children.

Our primary goal was to develop an alternative tool with at least equal, but preferably greater sensitivity over the CSF Gram stain result for corroborating a negative Gram stain report or for excluding bacterial meningitis when the CSF Gram stain report is unavailable or delayed. With this goal in mind, we compared the sensitivity of our score to that quoted in the medical literature for CSF Gram stain: 60%–80% for untreated and 40%–60% for antibiotic-pretreated patients in a recent review.32 We considered our score sufficiently useful if for all cases studied, 95% confidence intervals (CIs) for sensitivity lay beyond the range reported for the CSF Gram stain result. This definition is likely to have intuitive interpretation, being more pragmatic than an arbitrarily selected threshold.


Study Subjects

From June 1998 to February 2002, we identified 19 children with laboratory-established bacterial meningitis and 59 with laboratory-established enteroviral meningitis at Columbus Children’s Hospital who met all study criteria and had all ancillary test results recorded, for a total of 78 subjects. The median age of children with bacterial meningitis was 1.0 year (interquartile range [IQR] = 0.4–2.2 years) and for children with enteroviral meningitis, it was 6.6 years (IQR = 0.2–9.9 years; p = 0.0001). Bacteria isolated from the CSF of children evaluated at the Columbus Children’s Hospital included S. pneumoniae (n = 12), Neisseria meningitidis (n = 6), and Streptococcus agalactiae (n = 1). Information permitting identification of the specific types of enteroviruses isolated (culture/PCR) from individual patients was unavailable.


The following cut-points were identified by binary recursive partitioning for discriminating between bacterial and enteroviral meningitis: leukocyte concentration in peripheral blood >17,000 cells/dL, ratio of bands to neutrophils in peripheral blood >0.11 (11%), glucose in CSF <38 mg/dL, protein in CSF >97 mg/dL (5.38 mmol/L), WBCs in CSF >597 cells/μL, and neutrophils in CSF >74%. Sensitivity and specificity values for the derivation data set of cut-points selected for each laboratory test by the partitioning procedure are reported in Table 1. No individual laboratory test had both good sensitivity and specificity (CSF glucose <38 mg/dl did have a specificity/positive predictive value of 100%). When combined, however, the accuracy of the resultant 6-point prediction score was enhanced substantially (Tables 2 and 3).

Table 1.   Sensitivity and Specificity of Laboratory Test Thresholds Selected by Recursive Partitioning in Derivation Data Set*
Laboratory Test Incorporated into ScoreThreshold Designated to be PositiveSensitivity, n (%), 19 Bacterial CasesSpecificity, n (%), 59 Enteroviral Controls
  1. *Children seen at Columbus Children’s Hospital from 1998 to 2002.

  2. †Ratio of bands to neutrophils (B:N) in peripheral blood.

  3. ‡To convert mg/dL to mmol/L divide by 18 (or multiply by 0.055).

  4. CSF = cerebral spinal fluid.

Peripheral blood leukocytes>17,000 cells/mL12 (63)55 (93)
Peripheral blood B:N ratio†>11%16 (84)29 (49)
CSF leukocyte concentration>597 cells/μL12 (63)55 (93)
CSF neutrophil concentration>74%14 (74)49 (83)
CSF total protein concentration>97 mg/dL16 (84)55 (93)
CSF glucose concentration‡<38 mg/dL14 (74)59 (100)
Table 2.   Sensitivity and Specificity of the Decision Rule in Derivation and Validation Data Sets
Prediction ScoreColumbus Children’s Hospital Derivation Set (1998–2002)Columbus Children’s Hospital Validation Set (2002–2004)Boston Children’s Hospital Validation Set (1993–1999)Cases and Controls from All Three Data Sets (Columbus and Boston)
Sensitivity,*n (%), 19 CasesSpecificity,*n (%), 59 Controls Sensitivity,†n (%), 14 CasesSpecificity,†n (%), 144 ControlsSensitivity,‡n (%), 37 Cases Specificity,‡n (%), 834 CasesSensitivity,§n (%) [CI], 70 CasesSpecificity,§n (%) [CI], 1,037 Controls
  1. CI = confidence interval.

  2. *Sensitivity above and specificity below or equal to prediction score for cases with acute bacterial meningitis and controls with acute enteroviral meningitis.

  3. †Sensitivity above and specificity below or equal to stated prediction score for cases of acute bacterial meningitis and controls based on the maximum score derived from available laboratory tests (see text for an explanation of how to calculate the score).

  4. ‡Sensitivity above and specificity below or equal to stated prediction score for children with complete reporting of laboratory test results (see text).

  5. §Sensitivity above and specificity below or equal to stated prediction score with 95% CIs for three data sets combined.

019 (100)20 (34)14 (100)61 (42)37 (100)369 (44)70 (100)
[95, 100]
450 (43)
[40, 46]
119 (100)50 (85)14 (100)114 (79)36 (97)675 (81)69 (99)
[92, 100]
839 (81)
[78, 83]
217 (90)56 (95)10 (71)127 (88)32 (87)794 (95)59 (84)
[74, 92]
977 (94)
[93, 96]
316 (84)58 (98)5 (36)133 (92)25 (68)820 (98)46 (66)
[53, 77]
1,011 (97)
[96, 98]
47 (37)59 (100)1 (7)139 (97)12 (32)827 (99)20 (29)
[18, 41]
1,025 (99)
[98, 99]
Table 3.   Interval Likelihood Ratios (LR) of the Decision Rule and a Pragmatic Alternative* in External Data Set†
Prediction ScoreOriginal prediction rulePragmatic prediction rule*
Cases, n/37 (%)Controls, n/834 (%)ILR‡ (95% CI)Cases, n/37 (%)Controls, n/834 (%)ILR‡ (95% CI)
  1. To convert mg/dL to mmol/L divide by 18 (or multiply by 0.055).

  2. CI = confidence interval.

  3. *The pragmatic (easier to memorize) rule substitutes the following cut-points for thresholds in the original rule (compare Table 1): leukocytes >600 cells/μL, neutrophils >75%, total protein >100 mg/dL, glucose <40 mg/dL in CSF, and bands-to- neutrophils ratio >10% in peripheral blood. The threshold for the peripheral blood leukocyte count stays unchanged at >17,000 cells/mL. Each positive test result is assigned a single point and aggregated into a final score.

  4. †Boston Children’s Hospital: largest data set; provides the most precise estimates of interval LRs.

  5. ‡Interval LR at stated prediction score with 95% CIs.

  6. §Interval LR at or above a prediction score of 4; interval LR at or above a prediction score of 3 was 17 (95% CI = 12 to 24) for original rule and 16 (95% CI = 12 to 22) for pragmatic rule.

 00 (0)369 (44.2)0 (0, 0.2)0 (0)333 (39.9)0 (0, 0.2)
 11 (2.7)306 (36.7)0.07 (0.01, 0.5)1 (2.7)331 (39.6)0.07 (0.01, 0.5)
 24 (10.8)119 (14.3)0.8 (0.3, 1.9)3 (8.1)124 (14.9)0.5 (0.2, 1.6)
 37 (18.9)26 (3.1)6 (3, 14)9 (24.3)30 (3.6)7 (4, 13)
≥425 (67.6)14 (1.6)40§ (23, 71)24 (64.8)16 (1.9)34§ (20, 58)

Internal validation (Columbus Children’s Hospital)

The resultant score discriminated accurately between bacterial and enteroviral meningitis when validated by the bootstrap procedure, AROC value 0.98 (95% CI = 0.96 to 1.00), with no need to correct for bias. The AROC for the derivation sample remained the same (0.98, 95% CI = 0.96 to 1.00) when missing scores were replaced with values based on a multiple imputation procedure. The rule had a sensitivity of 100% and a specificity of 34% at a score of 0 and a sensitivity of 100% and a specificity of 85% at a score of 1 (Table 2). Two separate scores (0 and 1), therefore, were observed to have 100% sensitivity for diagnosing bacterial meningitis. LRs for separating bacterial from viral meningitis were 0 (95% CI = 0 to 0.5) at a score of zero, 0 (95% CI = 0 to 0.3) at a score of one, 1 (95% CI = 0.3 to 3) at a score of two, 2 (95% CI = 0.1 to 16) at a score of three, and 50 (95% CI = 7 to 350) at a score greater or equal to four.

External Validation (Columbus Children’s Hospital)

Between March 2002 and June 2004, a total of 14 pathogens were isolated from children diagnosed with bacterial meningitis at Columbus Children’s Hospital. These included N. meningitidis (n = 6), S. pneumoniae (n = 3), S. agalactiae (n = 4), and Escherichia coli (n = 1). There were 144 controls (54 with confirmed enteroviral meningitis) after removing children with known neurologic or neurosurgical diseases. For 7 of 14 cases and 68 of 144 controls, there was insufficient information to calculate the full score, so the maximum score based on available test results was substituted. The median age of children diagnosed with bacterial meningitis was 1.54 years (IQR = 0.29–11.88 years). Controls had a median age of 4.84 years (IQR = 0.22–9.72 years).

Despite the fact that a full report of tests to permit complete scoring was unavailable in approximately 50% of cases and controls, the AROC in these children was 0.90 (95% CI = 0.88–0.92). No case of bacterial meningitis was missed at a score of 0 or 1 (Table 2). Sensitivity and specificity values were 100 and 42% at a score of >0 and 100% and 79% at a score of >1. The LR increased from 0 at scores of 0 and 1 to 9 at a score of >2.

External Validation (Children’s Hospital Boston)

Thirty-seven pathogens were isolated from children diagnosed with bacterial meningitis at the Children’s Hospital Boston. Pathogens included S. pneumoniae (21), N. meningitidis (7), S. agalactiae (5), E. coli (2), and H. influenzae Type b (2). There were 834 controls after removing children with known neurosurgical/neurologic diseases. The median age of children diagnosed with bacterial meningitis was 0.97 years (IQR = 0.39–2.73 years). Controls had a median age of 0.19 years (IQR = 0.12–1.02 years)

The AROC among children evaluated at Children’s Hospital Boston was 0.97 (95% CI = 0.94 to 0.99). No case of bacterial meningitis was missed at a score of 0 (Table 2). This score had a sensitivity of 100% and a specificity of 44%. LRs were more precise and increased from 0 and 0.07 at scores of 0 and 1, respectively, to 40 at a score of ≥4. Unlike the aforementioned sets, one case of bacterial meningitis was missed at a score equal to 1. This was an 8-year-old boy diagnosed with pneumococcal meningitis who had the following laboratory test results: CSF leukocytes 279 cells/μL, CSF percent neutrophils 87%, CSF protein concentration 31.8 mg/dL, CSF glucose concentration 63 mg/dL (3.5 mmol/L), peripheral WBC count 9360 cells/mL, and a bands-to-neutrophils ratio in peripheral blood 0.01 (1%).

Similar test performance characteristics (Table 3) were observed for a pragmatic rule that for ease of memorization replaced thresholds in the original score with the following values: bands-to-neutrophils ratio in peripheral blood >10%; leukocytes >17,000 cells/mL (left unchanged); and neutrophils >75%, leukocytes >600 cells/μL, total protein >100 mg/dL, and glucose <40 mg/dL (2.22 mmol/L) in CSF. The AROC for this rule (Boston data set) was 0.96 (95% CI = 0.94 to 0.98; Figure 1).

Figure 1.

 Receiver-operator-characteristic (ROC) curve analysis: proportion of cases and controls at or above sequential cut-points of the original prediction rule, Boston Children’s Hospital. Area under the curve = 0.97. Numbers (0 to 6) that are adjacent to the points on the curve are sequential thresholds of the prediction rule.

Finally, at a prediction score of 0, the sensitivity of our score when combining 70 cases of acute bacterial meningitis from the three data sets was 100% (95% CI = 95% to 100%; Table 2) and beyond the range published for the CSF Gram stain result (40%–80%). Specificity at the same score when combining 1037 controls from the three data sets was 43% (95% CI = 40% to 46%). At a score of 1, sensitivity and specificity values, respectively, when combining all cases and controls in the three data sets were 99% (95% CI = 92% to 100%) and 81% (95% CI = 78% to 83%).


In this study, we present a new rule for diagnosing bacterial meningitis that does not require complex mathematical computation and is independent of the CSF Gram stain result, but appears sufficiently accurate to permit the timely diagnosis of this infection in children with CSF pleocytosis. We believe that this rule can be used to support clinical decisions relating to initial treatment and hospitalization of children suspected to have meningitis in clinical settings for which other validated rules published in the medical literature are limited or inapplicable.

It is worth reemphasizing that our prediction score extends rather than replaces existing rules. Utilized within a Bayesian framework of diagnosis, our score adjusts the prior odds of acute bacterial meningitis based on information gathered during the clinical examination to derive the final odds of this infection.30 It is likely to be most beneficial when clinical findings are inconclusive and Gram stain results of CSF are negative or unavailable. In this setting, a low score is likely to corroborate a truly negative CSF Gram stain result and to exclude bacterial infection, allowing clinicians to pursue less aggressive management strategies. Alternatively, low odds of bacterial meningitis may be adjusted higher if the score shows a high likelihood of bacterial meningitis, avoiding delays to diagnosis and antibiotic treatment.

Our prediction score is not a substitute for careful clinical examination and judgment. Indeed, proper use presupposes good clinical assessment skills and acumen. Additionally, within a Bayesian framework, diagnosis that remains inconclusive after application of our score may be further strengthened by combining LRs reported here with those estimated for other laboratory tests. Tests utilized at other sites that are reported to be helpful for diagnosing bacterial meningitis include serum procalcitonin and C-reactive protein.33 More research, however, is needed to reliably estimate the incremental value of newer (over traditional) laboratory tests for clinical diagnosis and for formalizing these and other diagnosis tools for decision-making.


Our study inherits all the limitations generally associated with a retrospective cohort design. It avoids, however, biases and ambiguities inherent to rules derived from a review of subjective findings by relying on quantitative test results alone. To create our rule, we relied exclusively on cases of bacterial and enteroviral meningitis confirmed by culture or nucleic acid amplification techniques. We chose this strict definition to avoid disease misclassification resulting from the unpredictable yield of bacteria from CSF culture in children receiving antibiotic treatment.15,34,35 Even so, and despite the imposition of reasonable safeguards, over-fitting due to the recursive partitioning procedure is possible. We also expect data sets to include a fraction of children pretreated with antibiotics because information about prior treatment is difficult to verify, retrospectively. This last limitation is compounded by the operational difficulty that even when the interval from prior antibiotic therapy to the ED encounter is recorded reliably, it still is difficult to select a realistic/sensible time frame beyond which to assert indisputably that such therapy has (vs. has not) ceased to confound spinal fluid test results.

These restrictions (as well as others that were potentially missed) had the potential to lower the performance of the rule in new subjects. Reassuringly, our resultant rule showed no substantial bias when evaluated by the bootstrap procedure, and when validated in children from other sites and time intervals, demonstrated good temporal, geographic, and methodologic transportability. We believe, therefore, that it captures robust predictors of acute bacterial meningitis (those that are invariant to these factors), generalizing sufficiently well for clinical diagnosis and management in diverse settings.23–25 Validation by other investigators is needed to solidify our findings and to further clarify the usefulness and practical application of this decision rule.23–25


The new rule reported in this study and intended, primarily, for children with unavailable, delayed, or negative CSF Gram stain and for sites that lack automated decision support capability appears to have adjunctive value for identifying acute bacterial meningitis at the initial clinical encounter. At a score of 0, it substantially lowers the prior odds of bacterial meningitis in children with CSF pleocytosis. Pending verification, this rule appears able to support judicious hospitalization and treatment in a wide array of children evaluated for meningitis in an outpatient setting.