Clinical and Hematological Predictors of High-Grade Immune-Related Adverse Events Associated With Immune Checkpoint Inhibitors
Bibliographic record
Abstract
BACKGROUND: Life-threatening immune-related adverse events (irAEs) that require hospital admission are not uncommon in patients treated with immune checkpoint inhibitors (ICIs). The clinical and hematological parameters are attractive biomarkers as potential predictors of irAE. METHODS: This is a retrospective study of patients with melanoma and lung cancer treated with ICIs between 2015 and 2019 at the University of South Alabama Mitchell Cancer Institute. Fisher's exact test, Pearson Chi-squared test, log-rank test, and Cox proportional hazard model were used to evaluate clinical and hematological parameters as possible predictors of irAE. RESULTS: The cohort consisted of 160 patients treated with at least two doses of ICI, of which 54 (33.8%) patients had melanoma and 106 (66.3%) had lung cancer. Incidence of irAE did not have any bearing on the overall survival (OS) or progression-free survival (PFS) of the cohort. The clinical factors associated with irAE were dual-agent therapy (ipilimumab/nivolumab combination) and high disease burden (≥ 2 metastatic sites). The irAE-group had a lower mean platelet-to-lymphocyte ration (PLR, 200 vs. 257, P = 0.04). Although not statistically significant at the level of 0.05, other factors such as type of cancer (lung cancer > melanoma (P = 0.06)), stage at treatment (stage IV > stage II and III disease (P = 0.06)), and higher absolute lymphocyte counts (P = 0.07) showed a considerable association with irAE and warrants further review with different patient data. CONCLUSIONS: Irrespective of ICI used to treat lung cancer and melanoma, patients with high disease burden and dual-agent ICI therapy were more prone to irAE. The only hematological parameter that may predict the incidence of irAE is low baseline PLR.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".