MétaCan
Menu
Back to cohort

Neurocognitive testing to predict ICANS post-CAR T.

2024· article· en· W4399481588 on OpenAlexaboutno aff
William Wesson, Lauren J Scott, Nahid Suleman, Shaun DeJarnette, Al‐Ola Abdallah, Forat Lutfi, Sunil Abhyankar, Leyla Shune, Muhammad Umair Mushtaq, Anurag Kumar Singh, Haitham Abdelhakim, Joseph P. McGuirk, Muhammad Nashatizadeh, Elizabeth Muenks, Hannah Katz, Nausheen Ahmed

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicine

Abstract

fetched live from OpenAlex

e19003 Background: Since the approvals of chimeric antigen receptor T-cell (CAR T) therapy in acute lymphoblastic leukemia (ALL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM), it has been increasingly offered in centers nationwide. Immune effector cell-associated neurotoxicity syndrome (ICANS) poses a potentially fatal risk with CAR-T. Identifying patients at risk of having high grade ICANS is a thus a priority. Scoring systems such as the EASIX scores have accounted for disease and comorbidity factors but haven’t addressed neurocognitive function. The St. Louis University Mental Status (SLUMS) Exam and the Montreal Cognitive Assessment (MoCA) have not been examined for validity and reliability in this population, so we performed a retrospective review of CAR T patients at the University of Kansas Medical Center to identify if incidence or severity of ICANS could be correlated with neurocognitive testing performed in the pre-infusion period. Methods: Patients receiving CAR T targeting CD19 or BCMA between December 2017 and December 2023 were included. Neurocognitive testing was performed by trained onco-psychology, neurology, or hematology-oncology staff. The SLUMS exam is an 11 question exam scored on a 30 point scale to predict normal neurocognitive function (27-30 points), mild neurocognitive disorders (21-26 points), and dementia (0-20 points). The MoCA is an 11 question exam scored on a 30 point scale to predict normal cognition (26-30 points), mild cognitive impairment (18-25 points), moderate cognitive impairment (10-17 points), and severe cognitive impairment (<10 points). Incidence of ICANS and severity of ICANS were compared between patients who had any detected cognitive dysfunction and those who had none, as well as between the tiered result system unique to each test. Results: We included 360 patients in this analysis, (257 NHL, 84 MM and 19 ALL) leading to comparisons of 276 CD19-directed CAR T patients and 84 BCMA-directed CAR T patients. In CD19-directed patients, there were no differences in ICANS incidence according to the SLUMS scores. There was a significant increase in ICANS incidence for patients who had a positive MoCA exam, p=0.006. Additionally, ICANS incidence was correlated with more severe MoCA results, p=0.01. No differences in ICANS severity were observed using any neurocognitive test. In BCMA-directed patients, no differences in ICANS were observed using the SLUMS or MoCA. Conclusions: Our results suggest that while the SLUMS exam does not predict ICANS, the MoCA may independently predict incidence of ICANS. Patients with a higher score correlating with normal cognition are less likely to develop ICANS and may be appropriate for outpatient therapy. For those with lower scores correlating with more severe neurocognitive disorders, inpatient monitoring and early intervention will continue to be important.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.204
GPT teacher head0.531
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical OncologySame topicMedical Imaging Techniques and ApplicationsFrench-language works237,207