Identification of cytokine release syndrome and indicators of severity in retrospective databases among patients receiving immunotherapy
Bibliographic record
Abstract
Cytokine release syndrome (CRS) can occur following cancer immunotherapies, but is most often mild and of limited duration. International Classification of Diseases (ICD)-10 codes allowing identification of CRS were introduced in 2020 but may be underutilized. We evaluated the performance of a published claims-based algorithm to detect CRS (any grade) and high-grade CRS (HG, grades 2-5), as well as identified indicators of HG CRS in retrospective data. Adults with low-grade and HG CRS during an encounter coinciding with administrations of blinatumomab or chimeric antigen receptor-T therapy were identified in three types of retrospective databases (hospital chargemaster data, electronic health records, and administrative claims). The algorithm's sensitivity in detecting any CRS and HG CRS was reported. A least absolute shrinkage and selection operator (LASSO) regression model was developed to identify indicators of HG CRS. Performance of the model was evaluated using area under the curve (AUC). The sensitivity of the algorithm to detect any grade CRS ranged between 77%-100% and between 8%-80% for HG CRS, depending on the type of database. The LASSO model identified hypotension, positive pressure (including mechanical ventilation), tocilizumab, and vasopressors as indicators of HG CRS. AUC varied between 60% and 75%. The algorithm accurately detected any grade CRS for over three-quarters of instances, but was not as reliable for HG CRS. Results varied based on database attributes. Hypotension, vasopressors, positive pressure, and tocilizumab were associated with HG CRS and may be methodologically helpful signals of CRS severity in retrospective data.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".