Literature DB >> 28298047

Development of a prediction rule for diagnosing postoperative meningitis: a cross-sectional study.

Olga Helena Hernández Ortiz1,2,3, Héctor Iván García García1, Fabián Muñoz Ramírez4,5, Juan Sebastián Cardona Flórez4,6, Bladimir Alejandro Gil Valencia3,4,6, Salvador Ernesto Medina Mantilla2,7, María Juliana Moreno Ochoa2,7, Jorge Eliécer Sará Ochoa3, Fabián Jaimes1.   

Abstract

OBJECTIVE Diagnosing nosocomial meningitis (NM) in neurosurgical patients is difficult. The standard CSF test is not optimal and when it is obtained, CSF cultures are negative in as many as 70% of cases. The goal of this study was to develop a diagnostic prediction rule for postoperative meningitis using a combination of clinical, laboratory, and CSF variables, as well as risk factors (RFs) for CNS infection. METHODS A cross-sectional study was performed in 4 intensive care units in Medellín, Colombia. Patients with a history of neurosurgical procedures were selected at the onset of febrile symptoms and/or after an increase in acute-phase reactants. Their CSF was studied for suspicion of infection and a bivariate analysis was performed between the dependent variable (confirmed/probable NM) and the identified independent variables. Those variables with a p value ≤ 0.2 were fitted in a multiple logistic regression analysis with the same dependent variable. After determining the best model according to its discrimination and calibration, the β coefficient for each selected dichotomized variable obtained from the logistic regression model was used to construct the score for the prediction rule. RESULTS Among 320 patients recruited for the study, 154 had confirmed or probable NM. Using bivariate analysis, 15 variables had statistical associations with the outcome: aneurysmal subarachnoid hemorrhage (aSAH), traumatic brain injury, CSF leak, positioning of external ventricular drains (EVDs), daily CSF draining via EVDs, intraventricular hemorrhage, neurological deterioration, age ≥ 50 years, surgical duration ≥ 220 minutes, blood loss during surgery ≥ 200 ml, C-reactive protein (CRP) ≥ 6 mg/dl, CSF/serum glucose ratio ≤ 0.4 mmol/L, CSF lactate ≥ 4 mmol/L, CSF leukocytes ≥ 250 cells, and CSF polymorphonuclear (PMN) neutrophils ≥ 50%. The multivariate analysis fitted a final model with 6 variables for the prediction rule (aSAH diagnosis: 1 point; CRP ≥ 6 mg/dl: 1 point; CSF/serum glucose ratio ≤ 0.4 mmol/L: 1 point; CSF leak: 1.5 points; CSF PMN neutrophils ≥ 50%: 1.5 points; and CSF lactate ≥ 4 mmol/L: 4 points) with good calibration (Hosmer-Lemeshow goodness of fit = 0.71) and discrimination (area under the receiver operating characteristic curve = 0.94). CONCLUSIONS The prediction rule for diagnosing NM improves the diagnostic accuracy in neurosurgical patients with suspicion of infection. A score ≥ 6 points suggests a high probability of neuroinfection, for which antibiotic treatment should be considered. An independent validation of the rule in a different group of patients is warranted.

Entities:  

Keywords:  AUROC = area under the ROC curve; CI = confidence interval; CRP = C-reactive protein; EVD = external ventricular drain; ICU = intensive care unit; IQR = interquartile range; NM = nosocomial meningitis; NPV = negative predictive value; OR = odds ratio; PMN = polymorphonuclear; PPV = positive predictive value; RF = risk factor; ROC = receiver operating characteristic; TBI = traumatic brain injury; VPS = ventriculoperitoneal shunt; aSAH = aneurysmal subarachnoid hemorrhage; infection; lactate; nosocomial meningitis; postoperative meningitis; prediction rule

Mesh:

Substances:

Year:  2017        PMID: 28298047     DOI: 10.3171/2016.10.JNS16379

Source DB:  PubMed          Journal:  J Neurosurg        ISSN: 0022-3085            Impact factor:   5.115


  10 in total

Review 1.  Healthcare-Associated Infections in the Neurocritical Care Unit.

Authors:  Katharina M Busl
Journal:  Curr Neurol Neurosci Rep       Date:  2019-08-27       Impact factor: 5.081

2.  Longitudinal Analysis of Risk Factors for Clinical Outcomes of Enterobacteriaceae Meningitis/Encephalitis in Post-Neurosurgical Patients: A Comparative Cohort Study During 2014-2019.

Authors:  Yi-Jun Shi; Guang-Hui Zheng; Ling-Ye Qian; Rasha Alsamani Qsman; Guo-Ge Li; Guo-Jun Zhang
Journal:  Infect Drug Resist       Date:  2020-07-06       Impact factor: 4.003

3.  A sequential guide to identify neonates with low bacterial meningitis risk: a multicenter study.

Authors:  Yan Chen; Zhanghua Yin; Xiaohui Gong; Jing Li; Wenhua Zhong; Liqin Shan; Xiaoping Lei; Qian Zhang; Qin Zhou; Youyan Zhao; Chao Chen; Yongjun Zhang
Journal:  Ann Clin Transl Neurol       Date:  2021-04-09       Impact factor: 4.511

4.  Risk Prediction of Central Nervous System Infection Secondary to Intraventricular Drainage in Patients with Intracerebral Hemorrhage: Development and Evaluation of a New Predictive Model Nomogram.

Authors:  Yanfeng Zhang; Qingkao Zeng; Yuquan Fang; Wei Wang; Yunjin Chen
Journal:  Ther Innov Regul Sci       Date:  2022-04-24       Impact factor: 1.337

5.  Investigating related factors with mortality rate in patients with postoperative meningitis: One longitudinal follow up study in Iran.

Authors:  Arezoo Chouhdari; Kaveh Ebrahimzadeh; Omidvar Rezaei; Mohammad Samadian; Giv Sharifi; Mohammadreza Hajiesmaeili
Journal:  Iran J Neurol       Date:  2018-04-04

6.  Development and verification of a discriminate algorithm for diagnosing post-neurosurgical bacterial meningitis-A multicenter observational study.

Authors:  Guanghui Zheng; Xufeng Ji; Xiaochen Yu; Min Liu; Jing Huang; Lina Zhang; Dawen Guo; Guojun Zhang
Journal:  J Clin Lab Anal       Date:  2019-10-10       Impact factor: 2.352

7.  Evaluation of a micro/nanofluidic chip platform for diagnosis of central nervous system infections: a multi-center prospective study.

Authors:  Guanghui Zheng; Yan Zhang; Lina Zhang; Lingye Qian; Yumeng Cai; Hong Lv; Xixiong Kang; Dawen Guo; Xiaoming Wang; Jing Huang; Zhixian Gao; Xiuru Guan; Guojun Zhang
Journal:  Sci Rep       Date:  2020-01-31       Impact factor: 4.379

Review 8.  Current Perspectives on the Diagnosis and Management of Healthcare-Associated Ventriculitis and Meningitis.

Authors:  Marios Karvouniaris; Alexandros Brotis; Konstantinos Tsiakos; Eleni Palli; Despoina Koulenti
Journal:  Infect Drug Resist       Date:  2022-02-28       Impact factor: 4.003

9.  Establishment of a predictive model for purulent meningitis in preterm infants.

Authors:  Xinru Cheng; Zhaoqin Fu; Qian Zhang; Zanyang Shi; Peige Xia; Yanan Zhang; Fengxia Mao; Qianya Xu; Xiaomin Yan; Li Wang
Journal:  Transl Pediatr       Date:  2022-06

10.  Evaluation of the Diagnostic and Prognostic Value of CSF Presepsin Levels in Patients with Postneurosurgical Ventriculitis/Meningitis.

Authors:  Guanghui Zheng; Chenxi Zhang; Guojun Zhang; Chunqing Shao
Journal:  Infect Drug Resist       Date:  2021-07-27       Impact factor: 4.003

  10 in total

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