Impact of an immuno-oncology (IO) education/monitoring program on patient’s self-efficacy and adverse event reporting from immune checkpoint inhibitors (ICIs).
Bibliographic record
Abstract
2032 Background: ICIs have unique side effects of immune related adverse events (irAEs). For early detection and management of irAEs, at a large community hospital we implemented a standard IO nursing baseline assessment, education and monitoring program. We studied it’s impact on a patient’s irAE reporting and self-efficacy (confidence to manage symptoms) of ICIs. Methods: Prospective study conducted at William Osler Health System, Brampton, Canada from May 2018-December 2019. Patients aged > = 18, English speaking that received an ICI for cancer were included. Patients underwent a standardized baseline nursing assessment and education class. Patients identified at the assessment as high risk (risk of grade 3/4 irAE >20%) had weekly nurse proactive calls. Cancer Behaviour Inventory – Brief Version (CBI-B) (Heitzmann et al, 2011) was used to evaluate patient’s self-efficacy. Results: Eighty patients were enrolled. Median follow up of 4.1 months. Baseline demographics: median age 69, 70% males, 77% Caucasian, 81% ECOG 0/1, 66% had English as their first language and 19% highest education was elementary, 30% high school, 26% trade diploma and 21% post-secondary. Fourty-one percent had limited cancer health literacy (measured by CHLT6 (Dumenci et al, 2014)). ICIs prescribed were 70% monotherapy anti-PD1/PDL1, 13% combination nivolumab/ipilimumab, 17% anti-PD1/PDL1 + chemotherapy/other therapies. Majority had a diagnosis of non-small cell lung cancer (55%), melanoma (19%) and renal cell carcinoma (9%). A statistically significant improvement in the average CBI-B scores were found pre and post baseline assessment/education (p < 0.001) and this improvement was maintained over time at follow-up visits (non-significant change in scores from post education results). Fourty-three percent of patient’s experienced > 1 irAE. Most were grade 1/2 at time of detection (65%). Method of detection was mainly by patient self-reporting (62%), followed by proactive calls (27%). Only 3 patients had detection of an irAE with an ER visit. Rate of discontinuation of ICIs due to toxicity was 8.8%. Conclusions: In this diverse patient population with almost half of patients having limited cancer health literacy, a standardized IO baseline assessment, education and monitoring program resulted in improved patient self-efficacy with most irAEs detected by self-reporting and proactive calls. Our IO program can be a model for other oncology programs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".