WITHDRAWN: Novel Risk Factors for Predicting Immune Effector Cell-Associated Neurotoxicity Syndrome
Bibliographic record
Abstract
ICANS is a common form of neurological immunotoxicity from CAR T-cell therapy (CAR-T). While high tumor burden, product type and cell dose are established risk factors, there are many unknowns. Our objective was to characterize novel neurological and non-neurological risk factors for the development of ICANS in subjects who received CAR-T. We retrospectively identified 93 subjects (60% men, mean age 60) who had undergone CD19 or BCMA-targeting CAR-T for hematological malignancy from 2018 to 2023 at a large academic hospital. Incidence of ICANS was 31.2%, high-grade in 9.7%. A low baseline MOCA score (p=0.008) was associated with ICANS when controlled for baseline ferritin, KPS, and age; loss of points on specific cognitive sub-scores was also significant, with poor attention testing of particular concern. Presence of preexisting cerebrovascular disease, active autoimmune disease, and neurological tumor involvement were not associated with increased risk. ICANS was also associated with older age (p=0.024), elevated baseline ferritin (p=0.006), low KPS (p=0.004), and preceding or concurrent CRS of any grade (<0.001). Increasing ferritin between baseline and Day 5+ (p=0.002) was associated with development of high-grade ICANS, along with prior tocilizumab exposure (p=0.015). Subjects who developed any grade of ICANS had higher 90-day mortality than those who did not (p<0.001). Identification of these additional baseline risk factors for ICANS will help identify high-risk patients ahead of treatment and allow for improved preventative planning and early identification of ICANS. Key Points: Low baseline MOCA score is an independent risk factor for ICANS. Impaired baseline attention testing is of particular concern.Baseline cerebrovascular disease, neurological exam focality, and active autoimmune disease are not associated with ICANS.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".