PB2497: HEMATOLOGIC TOXICITIES WITH IMMUNOTHERAPY IN CANCER PATIENTS AT THE ALLAN BLAIR CANCER CENTRE: RETROSPECTIVE DATA COLLECTION STUDY
Bibliographic record
Abstract
Topic: 25. Gene therapy, cellular immunotherapy and vaccination - Clinical Background: Immunotherapy, particularly checkpoint inhibitors, has demonstrated significant clinical benefits in the treatment of various malignancies. However, these novel therapies can also lead to serious and potentially life-threatening toxicities. This necessitates further investigation into the incidence and nature of these toxicities in clinical practice. Aims: The aim of this retrospective chart review study was to identify the proportion and context of patients treated at the Allan Blair Cancer Centre who experienced immunotherapy-related toxicities. We focused specifically on hematologic toxicities in order to provide clinicians with a better understanding of the risks associated with this drug class. Methods: We reviewed a total of 486 patient charts from the Saskatchewan Cancer Agency, pertaining to patients who received immunotherapy for any indication between 2013, when immunotherapy began being administered within the system, and May 30th, 2020. Patients with various tumour types who received anti-programmed cell death 1 (PD-1) and/or anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), with or without chemotherapy, and experienced anemia, neutropenia, or thrombocytopenia within a time frame of therapy were included. Breast, gastrointestinal, sarcoma, and neuroendocrine tumours were excluded. We collected data on the incidence of hematologic toxicities and the management of these toxicities in clinical practice, using a data collection tool in REDCap. Results: Among the charts identified, 430 patients met the inclusion criteria. The mean age was 70.3±11.2 years, and 50.8% were female. The overall rate of immunotherapy-related reactions was 47.3%, whereas anemia, thrombocytopenia and neutropenia were 93.5%, 21.3% and 11.3%, respectively. Patients with combination immunotherapy and chemotherapy had a lower incidence of anemia (4.1% versus 15.3%, P<0.001), thrombocytopenia (6.9% versus 28.6%, P<0.001) and neutropenia (5.9% versus 65.4%, P<0.001). Summary/Conclusion: The incidence of immunotherapy-related anemia was the most significant finding in our data. Most of the reactions were less common in the combination therapy cohort. The outcome of this research may assist clinicians in identifying the risks burdening patients undergoing cancer treatment and establishing best management practices to reduce patient morbidity and mortality. Keywords: Toxicity, Cancer immunotherapy
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".