Point-of-Care Bispectral Electroencephalogram (BSEEG) As a New Tool in Detecting Immune Effector Cell Associated Neurotoxicity (ICANS) in Patients Treated with Chimeric Antigen Receptor T-Cell (CAR-T)
Bibliographic record
Abstract
Background: Immune effector cell-associated neurotoxicity syndrome (ICANS) is a clinical and neuropsychiatric syndrome that is seen after chimeric antigen receptor T-cell (CAR-T) treatment, and it is mainly characterized by confusion, behavioral changes and language deficits. The current American Society of Transplantation and Cellular Therapy (ASTCT) guidelines recommend using the Immune Effector Cell Encephalopathy (ICE) score as an initial screening tool for the development of ICANS. However, the subjective nature of the scoring may hamper the precision of the patient assessment and potentially delay intervention. Recently, the Bispectral Electroencephalogram (BSEEG) was developed as an objective assessment tool for delirium. The BSEEG score has excellent detection performance for delirium (Shinozaki et al. Psychiatry Clin Neurosci 2018) and predicting patient outcomes, including hospital length of stay and mortality (Yamanashi et al. Br J Psychiatry 2021). Thus, we aimed to prove whether the BSEEG could detect the onset of ICANS in patients treated with CAR-T therapy. Methods: The BSEEG score was collected for 57 adult patients receiving CAR-T therapy for hematologic malignancies at Stanford Health Care between August 2023 and July 2024 using a portable, thumb-sized two-lead EEG device (ZA, ProAssist, Osaka, Japan). The score was calculated based upon the ratio of low-frequency (3Hz) to high-frequency (10Hz) power spectral density (PSD), such that a higher BSEEG score indicates the presence of more slow waves. ICE scores of 0-2 (ICANS grade 3 and 4) were used to define cases of severe ICANS. A baseline Montreal Cognitive Assessment (MoCA) test was performed for all patients. A Student's t-test was used to compare the averages of the BSEEG score in cases and controls. Results: Mean age of our cohort was 64.9 years, 65% were Caucasians, 18% were Asians, 7% were Black and 4% were Hispanic. 65% had a diagnosis of lymphoma, 25% had multiple myeloma and 10% had acute lymphoblastic leukemia. The CAR-T therapies patients received were axicabtagene ciloleucel (n = 17), brexucabtagene autoleucel (n = 5), lisocabtagene maraleucel (n = 2), tisagenlecleucel (n = 4), ciltacabtagene autoleucel (n = 9), idecabtagene vicleucel (n = 4), others (n = 16). 32% (n = 18) developed all grade ICANS, of which 14% (n = 8) developed severe (grade 3 or 4) ICANS. There were no statistically significant differences in age between the severe ICANS cohort (n = 8) vs control (n = 49) (69.6 vs 64.0, p = 0.36), in sex (p = 0.13), in ethnicities (p = 0.76) or in baseline MoCA score (24.9 vs 25.5, p = 0.72). A total of 147 BSEEG recordings were obtained during their treatment course. The average of the highest recorded BSEEG scores during patient's hospital stay for severe ICANS cohort was 1.84 (SD = 0.33) vs 1.60 (SD = 0.23) in controls (p = 0.013). Furthermore, there was an inverse correlation between ICE score and BSEEG score (rho = -0.20, p = 0.017). Conclusions: We report the potential usefulness of an easy-to-use, portable EEG device that could detect the development of ICANS in patients receiving CAR-T therapy. It could be employed as an objective test to complement the relatively subjective ICE score. Further investigations are needed to validate the BSEEG score as a predictive tool for ICANS, where preventative measures and prompt intervention could be used to improve patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".