Prevalence of Bacteremia and Bacterial Meningitis in Febrile Neonates and Infants in the Second Month of Life

Biondi, Eric; Lee, Brian; Ralston, Shawn L.; Winikor, Jared; Lynn, Justin; Dixon, Angela; McCulloh, Russell J.

doi:10.1001/jamanetworkopen.2019.0874

Cited by 52 publications

(41 citation statements)

References 22 publications

(52 reference statements)

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“…Approximately 1% of infants #60 days of age who undergo cerebrospinal fluid (CSF) testing in the emergency department (ED) have bacterial meningitis. 1 While awaiting bacterial culture results, which can take up to 48 hours to return, clinicians need to make treatment decisions on the basis of the infant' s CSF profile. Young infants with bacterial meningitis may have normal CSF white blood cell (WBC) counts and protein and glucose levels.…”

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confidence: 99%

Cerebrospinal Fluid Profiles of Infants ≤60 Days of Age With Bacterial Meningitis

Fleischer¹,

Neuman

Wang

et al. 2019

Hospital Pediatrics

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We aimed to describe the cerebrospinal fluid (CSF) profiles of infants #60 days old with bacterial meningitis and the characteristics of infants with bacterial meningitis who did not have CSF abnormalities. METHODS:We included infants #60 days old with culture-positive bacterial meningitis who were evaluated in the emergency departments of 11 children's hospitals between July 1, 2011, and June 30, 2016. From medical records, we abstracted clinical and laboratory data. For infants with traumatic lumbar punctures (CSF red blood cell count of $10 000 cells per mm 3 ), we used a red blood cell count/white blood cell (WBC) count correction factor of 1000:1 to determine the corrected CSF WBC count. We calculated the sensitivity for bacterial meningitis of a CSF Gram-stain and corrected CSF pleocytosis ($16 WBCs per mm 3 for infants #28 days old and $10 WBCs per mm 3 for infants 29-60 days old).RESULTS: Among 66 infants with bacterial meningitis, the sensitivity of a CSF Gram-stain was 71.9% (95% confidence interval [CI]: 59.2-82.4), and the sensitivity of corrected CSF pleocytosis was 80.3% (95% CI: 68.7-89.1). The sensitivity of combining positive Gram-stain results with corrected CSF pleocytosis was 86.4% (95% CI: 75.7-93.6). Of 9 infants with meningitis who had a negative Gram-stain result and no corrected CSF pleocytosis, 8 (88.9%) had either an abnormal peripheral WBC count (.15 000 or ,5000 cells per mL) or bandemia .10%. CONCLUSIONS:Most infants #60 days old with bacterial meningitis have CSF pleocytosis or a positive Gramstain result. Infants with no CSF pleocytosis and a negative Gram-stain result are unlikely to have bacterial meningitis in the absence of other laboratory abnormalities.

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confidence: 99%

Cerebrospinal Fluid Profiles of Infants ≤60 Days of Age With Bacterial Meningitis

Fleischer¹,

Neuman

Wang

et al. 2019

Hospital Pediatrics

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“…Currently, rapid diagnosis of CNSI caused by bacteria and fungi is a crucial challenge in the fields of neurology and neurosurgery. The literature reports that the incidence of post-neurosurgical CNSI is as high as 0.3%-25% 19 , and the proportion of spontaneous CNSI-related meningitis is also high, which can lead to serious health consequences and a significant increase in patient mortality 20 . At present, the clinical diagnosis of CNSI is mainly based on the combination of clinical symptoms and laboratory tests.…”

Section: Discussionmentioning

confidence: 99%

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

Zheng

Zhang

et al. 2020

Sci Rep

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Central nervous system infection (CNSI) is a significant type of infection that plagues the fields of neurology and neurosurgical science. Prompt and accurate diagnosis of CNSI is a major challenge in clinical and laboratory assessments; however, developing new methods may help improve diagnostic protocols. This study evaluated the second-generation micro/nanofluidic chip platform (MNCP-II), which overcomes the difficulties of diagnosing bacterial and fungal infections in the CNS. The MNCP-II is simple to operate, and can identify 44 genus or species targets and 35 genetic resistance determinants in 50 minutes. To evaluate the diagnostic accuracy of the second-generation micro/nanofluidic chip platform for CNSI in a multicenter study. The limit of detection (LOD) using the second-generation micro/nanofluidic chip platform was first determined using six different microbial standards. A total of 180 bacterium/fungi-containing cerebrospinal fluid (CSF) cultures and 26 CSF samples collected from CNSI patients with negative microbial cultures were evaluated using the MNCP-II platform for the identification of microorganism and determinants of genetic resistance. The results were compared to those obtained with conventional identification and antimicrobial susceptibility testing methods. The LOD of the various microbes tested with the MNCP-II was found to be in the range of 250-500 copies of DNA. For the 180 CSF microbe-positive cultures, the concordance rate between the platform and the conventional identification method was 90.00%; eight species attained 100% consistency. In the detection of 9 kinds of antibiotic resistance genes, including carbapenemases, ESBLs, aminoglycoside, vancomycin-related genes, and mecA, concordance rates with the conventional antimicrobial susceptibility testing methods exceeded 80.00%. For carbapenemases and ESBLs-related genes, both the sensitivity and positive predictive values of the platform tests were high (>90.0%) and could fully meet the requirements of clinical diagnosis. MNCP-II is a very effective molecular detection platform that can assist in the diagnosis of CNSI and can significantly improve diagnostic efficiency.Central nervous system infection (CNSI) is a significant type of infection that currently plagues the fields of neurology and neurosurgery 1 . The primary symptoms of CNSI are meningitis and encephalitis syndrome. Common pathogens that cause CNSI include various species of bacteria, fungi, and viruses 2 , and some insects have been identified as vectors for CNSI-associated pathogens 3,4 . Of them, bacteria and fungi are the most significant Additionally, the MNCP-II-A test was performed on 26 CSF microbial culture-negative specimens, and the specimens were subjected to Sanger sequencing to compare the consistency of the test results.AST of bacteria of MNCP-II. ASTs of all microbes isolated from the CSF positive broth was performed in the clinical diagnosis departments of each of the four neurological centers by applying a standardized protocol. All of the isolates w...

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“…As predictors of some auxiliary tests (complete blood count [CBC], C‐reactive protein [CRP], and procalcitonin [PCT] testing) individually have limited value owing to low sensitivity, many investigators are devoted to developing predictive models for the identification of young infants at low risk of invasive bacterial infection (IBI), including meningitis, using rapidly available biomarkers. However, most existing models were derived from infants with fever and/or across a wide age range, which may not have applicability in the neonatal population, especially afebrile newborns 9–11 . There is currently no widespread consensus on the LP indication for neonates.…”

Section: Introductionmentioning

confidence: 99%

“…However, most existing models were derived from infants with fever and/or across a wide age range, which may not have applicability in the neonatal population, especially afebrile newborns. [9][10][11] There is currently no widespread consensus on the LP indication for neonates.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Yin

Gong

et al. 2021

Ann Clin Transl Neurol

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Objective To derive and validate a predictive algorithm integrating clinical and laboratory parameters to stratify a full‐term neonate's risk level of having bacterial meningitis (BM). Methods A multicentered dataset was categorized into derivation (689 full‐term neonates aged ≤28 days with a lumbar puncture [LP]) and external validation (383 neonates) datasets. A sequential algorithm with risk stratification for neonatal BM was constructed. Results In the derivation dataset, 102 neonates had BM (14.8%). Using stepwise regression analysis, fever, infection source absence, neurological manifestation, C‐reactive protein (CRP), and procalcitonin were selected as optimal predictive sets for neonatal BM and introduced to a sequential algorithm. Based on the algorithm, 96.1% of BM cases (98 of 102) were identified, and 50.7% of the neonates (349 of 689) were classified as low risk. The algorithm’s sensitivity and negative predictive value (NPV) in identifying neonates at low risk of BM were 96.2% (95% CI 91.7%–98.9%) and 98.9% (95% CI 97.6%–99.6%), respectively. In the validation dataset, sensitivity and NPV were 95.9% (95% CI 91.0%–100%) and 98.8% (95% CI 97.7%–100%). Interpretation The sequential algorithm can risk stratify neonates for BM with excellent predictive performance and prove helpful to clinicians in LP‐related decision‐making.

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Prevalence of Bacteremia and Bacterial Meningitis in Febrile Neonates and Infants in the Second Month of Life

Cited by 52 publications

References 22 publications

Cerebrospinal Fluid Profiles of Infants ≤60 Days of Age With Bacterial Meningitis

Cerebrospinal Fluid Profiles of Infants ≤60 Days of Age With Bacterial Meningitis

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

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

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