Abstract 5489: The cost of delaying therapy for advanced non-small cell lung cancer (NSCLC): a population kinetics assessment
Bibliographic record
Abstract
Abstract Background: Systemic therapy prolongs overall survival (OS) in advanced NSCLC. The best outcome requires the best therapy choice. To choose the best therapy requires baseline diagnostic tests, staging and molecular profiling, but patients are at risk of deteriorating and dying while awaiting testing prior to therapy initiation. OS follows first order kinetics. We used population kinetics assessments to estimate % of patients dying while awaiting therapy initiation. Method: For 1st line studies in advanced NSCLC that included a placebo or best supportive care (BSC) arm we digitized published OS curves, used GraphPad Prism 7 for exponential decay nonlinear regression analysis, calculated OS half-life (t1/2) and assessed data fit to 1 and 2 phase decay models. The proportion of patients “x” surviving after a time of interest tn was calculated by the Excel formula x =EXP(-tn*0.693/t1/2) where * indicates multiplication and 0.693 is the natural logarithm of 2. Results: We identified 7 trials and a meta-analysis. Across studies, the median OS t1/2 with 1st line placebo/BSC was 19.3 weeks. Hence, by 1, 2, 3 and 4 weeks after study entry 4%, 7%, 10% and 13% of patients, respectively, would have died (ie, 4% of the remaining patients with each passing week). This is in keeping with most OS curves showing rapid decline from the outset. OS curves fit 2 phase decay models in 5 studies, indicating a distinct short survival subgroup (on average, 89% of patients in these trials) and a longer surviving subgroup (potentially from having initiated systemic therapy when progression was detected). The short survival subgroup had a median OS t1/2 across studies of 11.3 weeks. The earliest deaths would be expected to occur predominantly in this short survival subgroup, in which 5%, 9%, 13% and 17% had died by 1, 2, 3 and 4 weeks respectively. Conclusions: Since OS follows first order kinetics, OS decline was probably following approximately the same rate prior to patient inclusion on these trials. In addition, since patients may deteriorate rapidly, others may have become too sick to consider therapy even if still alive. Rapid deterioration and short OS help explain why less than 25% of Ontario patients make it on to systemic therapy for advanced NSCLC despite the therapy being government funded. Since diagnostic, staging and molecular profiling procedures are needed before optimal therapy can start, these procedures must happen rapidly. It also illustrates why we must make screening procedures for clinical trial inclusion much faster. Otherwise patients are at risk of deteriorating rapidly or dying while awaiting eligibility assessment. It is also important to not delay initiation of systemic therapy for procedures such as radiotherapy for asymptomatic brain metastases. Any inefficiency that delays systemic therapy initiation may worsen patient outcome. Citation Format: David J. Stewart, Donna Maziak, Marcio Gomes, Michael Fung-Kee-Fung, Carole Dennie, Harman Sekhon, Bryan Lo, John-Peter Bradford, Sara Moore, Neil Reaume. The cost of delaying therapy for advanced non-small cell lung cancer (NSCLC): a population kinetics assessment [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5489.
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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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".