Association of Epileptic and Nonepileptic Seizures and Changes in Circulating Plasma Proteins Linked to Neuroinflammation

Objective

To develop a diagnostic test that stratifies epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES) by developing a multimodal algorithm that integrates plasma concentrations of selected immune response–associated proteins and patient clinical risk factors for seizure.

Methods

Daily blood samples were collected from patients evaluated in the epilepsy monitoring unit within 24 hours after EEG confirmed ES or PNES and plasma was isolated. Levels of 51 candidate plasma proteins were quantified using an automated, multiplexed, sandwich ELISA and then integrated and analyzed using our diagnostic algorithm.

Results

A 51-protein multiplexed ELISA panel was used to determine the plasma concentrations of patients with ES, patients with PNES, and healthy controls. A combination of protein concentrations, tumor necrosis factor–related apoptosis-inducing ligand (TRAIL), intercellular adhesion molecule 1 (ICAM-1), monocyte chemoattractant protein-2 (MCP-2), and tumor necrosis factor–receptor 1 (TNF-R1) indicated a probability that a patient recently experienced a seizure, with TRAIL and ICAM-1 levels higher in PNES than ES and MCP-2 and TNF-R1 levels higher in ES than PNES. The diagnostic algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.94 ± 0.07, sensitivity of 82.6% (95% confidence interval [CI] 62.9–93.0), and specificity of 91.6% (95% CI 74.2–97.7). Expanding the diagnostic algorithm to include previously identified PNES risk factors enhanced diagnostic performance, with AUC of 0.97 ± 0.05, sensitivity of 91.3% (95% CI 73.2–97.6), and specificity of 95.8% (95% CI 79.8–99.3).

Conclusions

These 4 plasma proteins could provide a rapid, cost-effective, and accurate blood-based diagnostic test to confirm recent ES or PNES.

Classification of Evidence

This study provides Class III evidence that variable levels of 4 plasma proteins, when analyzed by a diagnostic algorithm, can distinguish PNES from ES with sensitivity of 82.6% and specificity of 91.6%.

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