Cerebrospinal fluid cell count variability is a major confounding factor in external ventricular drain-associated infection surveillance diagnostics: a prospective observational study

Abstract

Background: External ventricular drain (EVD)-related infections (EVDIs) are feared complications that are difficult to rapidly and correctly diagnose, which can lead to unnecessary treatment with broad-spectrum antibiotics. No readily available diagnostic parameters have been identified to reliably predict or identify EVDIs. Moreover, intraventricular hemorrhage is common and affect cerebrospinal fluid (CSF) cellularity. The relationship between leukocytes and erythrocytes is often used to identify suspected infection and triggers the use of antibiotics pending results of cultures, which may take days. Cell count based surveillance diagnostics assumes a homogeneous distribution of cells in the CSF. Given the intraventricular sedimentation of erythrocytes on computed tomography scans this assumption may be erroneous and could affect diagnostics.

Aims: To evaluate the consistency of cell counts in serially sampled CSF from EVDs, with and without patient repositioning, to assess the effect on infection diagnostics.

Methods: We performed a prospective single-center study where routine CSF sampling was followed by a second sample after 10 min, allocated around a standard patient repositioning, or not. Changes in absolute and pairwise cell counts and ratios were analyzed, including mixed regression models.

Results: Data from 51 patients and 162 paired samples were analyzed. We observed substantial changes in CSF cellularity as the result of both resampling and repositioning, with repositioning found to be an independent predictor of bidirectional cellular change. Glucose and lactate levels were affected, however clinically non-significant. No positive CSF cultures were seen during the study. Thirty percent (30%) of patients changed suspected EVDI status, as defined by the cell component of local and national guidelines, when resampling after repositioning.

Conclusions: CSF cell counts are not consistent and are affected by patient movement suggesting a heterogeneity in the intraventricular space. The relationship between leukocytes and erythrocytes was less affected than absolute changes. Importantly, cell changes are found to increase with increased cellularity, often leading to changes in suspected EVDI status. Faster and more precise diagnostics are needed, and methods such as emerging next generation sequencing techniques my provide tools to more timely and accurately guide antibiotic treatment. Trial Registration NCT04736407, Clinicaltrials.gov, retrospectively registered 2nd February 2021.

Keywords: Cell counts; Cerebrospinal fluid; External ventricular drain; External ventricular drain associated infections; Infection diagnostics.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2021. The Author(s).

Figures

Fig. 1

Study flowchart. All burn-in patients were repositioned (R) before resampling. All others were allocated to R or Control (C) with a 10 min wait time before resampling but no repositioning

Fig. 2

Paired samples per patient. R-samples (light gray) and C-Samples (dark gray) per patient. The first eight patients were all repositioned before resampling (burn-in period). The number of samples per patient was influenced by length of stay which varied significantly between patients

Fig. 3

Variability of paired samples. Density plots of the pair-wise Δ distributions for R-samples (light gray) and C-samples (dark gray) for all CSF parameters, respectively. The x-axis shows the pair-wise differences or Δ. The area under each will be sum-able to 1, defining the y-axis values. One R-sample with a Δ of − 3505 was excluded from the granulocyte plot in order to improve visualization. R-samples: paired samples around a patient repositioning. C-samples: paired non-repositioned samples

Fig. 4

Correlations of paired samples. Scatter plots comparing sample 1 on the x-axis with sample 2 on the y-axis for each CSF parameter. The R2 of linear regression is given. The correlations are seen weaker for cells exhibiting a greater variability of paired samples

Fig. 5

Variability in relation to initial levels. Plots of sample 1 levels versus the pair-wise Δ for each CSF parameter. Albumin, granulocytes, erythrocytes, and the LE ratio have been log-transformed in order to improve visualization. Cells show a clear heteroscedasticity potentially impacting diagnostics that is not seen for glucose and lactate, but to some extent for albumin. This suggests a heterogeneity of larger molecules in the sampled volumes. Δ: pair-wise difference