Treatment with PD-1 inhibitors in NSCLC beyond disease progression: Impact on symptoms and cost.
Bibliographic record
Abstract
174 Background: Immunotherapy with PD-1 axis inhibitors has become standard of care in the treatment of patients with advanced non-small cell lung cancer (NSCLC), with improved survival and less toxicity than chemotherapy. Response patterns to PD-1 axis inhibitors can be unconventional, including pseudoprogression. Identifying disease progression using RECIST has been challenging. We explored treatment past progression (TPP) with PD-1 inhibitors and its impact on patient symptoms and costs. Methods: Retrospective analysis of consecutive patients with advanced NSCLC that received single agent PD-1 inhibitors at the Princess Margaret Cancer Centre (PMH) between 2013 and 2017. Patient symptom burden was assessed by Edmonton Symptom Assessment Scale (ESAS) at outpatient clinic visits. Drug acquisition costs were calculated using hospital-based costs for the agents used and both actual and fixed dosing recommendations. Results: The study cohort included 89 patients with advanced NSCLC. Median age was 62, half were female and 75% had adenocarcinoma. Most, 70%, developed disease progression during treatment, 19% stopped due to toxicity. Twenty-one patients received TPP (23% of entire cohort, 34% of those with progression). Demographic characteristics except age and baseline symptom scores were similar between groups. Younger patients (p = 0.046), those with lower symptom burden upon RECIST progression (mean ESAS 14.7 vs. 27.2 non-TPP, p = 0.013) and greater symptom improvement from baseline (mean ESAS change -11.7 vs. -2.3 non-TPP, p = 0.11) were more likely to receive TPP. During TPP, only 1 patient achieved tumor response. TPP represented 29% of total treatment costs. This did not vary significantly between weight- based and fixed dosing. However, the costs of treatment of the entire cohort was 23% higher using fixed dosing compared to weight-based. Conclusions: : Treatment with immunotherapy beyond RECIST progression is common in advanced lung cancer, although patient benefit is rare. This can lead to substantial expenditure without clear value for most patients. Better methods for detecting early failure of immunotherapy in advanced NSCLC are needed, to allow patients to switch to more active treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".