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Prediction of Lyme meningitis based on a logistic regression model using clinical and cerebrospinal fluid analysis: a European study.
Pediatr Infect Dis J. 2009 May; 28(5):394-7.PI

Abstract

BACKGROUND

A prediction model based on clinical and cerebrospinal fluid (CSF) analysis has been proposed for the differentiation of Lyme meningitis (LM) from non-Lyme aseptic meningitis (NLAM) in the United States. No similar model has ever been proposed for European patients. The objective of our study was to develop a prediction model to differentiate LM from NLAM based on clinical and CSF biologic data.

METHODS

The medical charts of all children older than 2 years of age admitted to our hospital from 1996 through 2006 with a definite diagnosis of LM were analyzed and compared retrospectively with those having a diagnosis of NLAM. Chart review included the duration of symptoms, the presence of cranial neuropathy, and CSF analysis.

RESULTS

A total of 93 patients were included (LM: 26 patients; NLAM: 67 patients) in the study. Patients with LM had statistically more frequent cranial neuropathy (73% vs. 4%), displayed a longer duration of symptoms before admission (8.8 vs. 1.8 days), had a higher CSF protein (71 vs. 38 mg/d), and had a lower percentage of neutrophil cells in the CSF (3.4% vs. 51%) than patients with NLAM. A predicted probability was derived from these 4 variables. At a cutoff point of >0.432, the model had a negative predictive value of 100% and a positive predictive value of 92.3%, with a sensitivity of 100% and a specificity of 97%.

CONCLUSIONS

We report the first European prediction model for LM. Owing to its high negative predictive value, this model may assist physicians in managing aseptic meningitis (AM) while awaiting serologic tests, especially in Lyme endemic regions.

Authors+Show Affiliations

Département de Pédiatrie, Universitaires de Mont-Godinne, Yvoir, Belgium. david.tuerlinckx@aclouvain.beNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Evaluation Study
Journal Article

Language

eng

PubMed ID

19295463

Citation

Tuerlinckx, David, et al. "Prediction of Lyme Meningitis Based On a Logistic Regression Model Using Clinical and Cerebrospinal Fluid Analysis: a European Study." The Pediatric Infectious Disease Journal, vol. 28, no. 5, 2009, pp. 394-7.
Tuerlinckx D, Bodart E, Jamart J, et al. Prediction of Lyme meningitis based on a logistic regression model using clinical and cerebrospinal fluid analysis: a European study. Pediatr Infect Dis J. 2009;28(5):394-7.
Tuerlinckx, D., Bodart, E., Jamart, J., & Glupczynski, Y. (2009). Prediction of Lyme meningitis based on a logistic regression model using clinical and cerebrospinal fluid analysis: a European study. The Pediatric Infectious Disease Journal, 28(5), 394-7. https://doi.org/10.1097/INF.0b013e318191f035
Tuerlinckx D, et al. Prediction of Lyme Meningitis Based On a Logistic Regression Model Using Clinical and Cerebrospinal Fluid Analysis: a European Study. Pediatr Infect Dis J. 2009;28(5):394-7. PubMed PMID: 19295463.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of Lyme meningitis based on a logistic regression model using clinical and cerebrospinal fluid analysis: a European study. AU - Tuerlinckx,David, AU - Bodart,Eddy, AU - Jamart,Jacques, AU - Glupczynski,Youri, PY - 2009/3/20/entrez PY - 2009/3/20/pubmed PY - 2009/7/8/medline SP - 394 EP - 7 JF - The Pediatric infectious disease journal JO - Pediatr Infect Dis J VL - 28 IS - 5 N2 - BACKGROUND: A prediction model based on clinical and cerebrospinal fluid (CSF) analysis has been proposed for the differentiation of Lyme meningitis (LM) from non-Lyme aseptic meningitis (NLAM) in the United States. No similar model has ever been proposed for European patients. The objective of our study was to develop a prediction model to differentiate LM from NLAM based on clinical and CSF biologic data. METHODS: The medical charts of all children older than 2 years of age admitted to our hospital from 1996 through 2006 with a definite diagnosis of LM were analyzed and compared retrospectively with those having a diagnosis of NLAM. Chart review included the duration of symptoms, the presence of cranial neuropathy, and CSF analysis. RESULTS: A total of 93 patients were included (LM: 26 patients; NLAM: 67 patients) in the study. Patients with LM had statistically more frequent cranial neuropathy (73% vs. 4%), displayed a longer duration of symptoms before admission (8.8 vs. 1.8 days), had a higher CSF protein (71 vs. 38 mg/d), and had a lower percentage of neutrophil cells in the CSF (3.4% vs. 51%) than patients with NLAM. A predicted probability was derived from these 4 variables. At a cutoff point of >0.432, the model had a negative predictive value of 100% and a positive predictive value of 92.3%, with a sensitivity of 100% and a specificity of 97%. CONCLUSIONS: We report the first European prediction model for LM. Owing to its high negative predictive value, this model may assist physicians in managing aseptic meningitis (AM) while awaiting serologic tests, especially in Lyme endemic regions. SN - 0891-3668 UR - https://www.unboundmedicine.com/medline/citation/19295463/Prediction_of_Lyme_meningitis_based_on_a_logistic_regression_model_using_clinical_and_cerebrospinal_fluid_analysis:_a_European_study_ L2 - https://doi.org/10.1097/INF.0b013e318191f035 DB - PRIME DP - Unbound Medicine ER -