Economic Impact of Next-Generation Sequencing Versus Single-Gene Testing to Detect Genomic Alterations in Metastatic Non–Small-Cell Lung Cancer Using a Decision Analytic Model
Bibliographic record
Abstract
PURPOSE The aim of the current study was to assess the economic impact of using next-generation sequencing (NGS) versus single-gene testing strategies among patients with metastatic non–small-cell lung cancer (mNSCLC) from the perspective of the Centers for Medicare & Medicaid Services (CMS) and US commercial payers. METHODS A decision analytic model considered patients who were newly diagnosed with mNSCLC who received programmed death ligand 1 and genomic alteration tests— EGFR, ALK, ROS1, BRAF, MET, HER2, RET, and NTRK1—using upfront NGS (all alterations tested simultaneously plus KRAS), sequential testing (sequence of single-gene tests), exclusionary testing ( KRAS plus sequential testing), and hotspot panels ( EGFR, ALK, ROS1, and BRAF tested simultaneously plus single-gene tests or NGS for MET, HER2, RET, and NTRK1). Model outcomes for each strategy were time-to-test results, the proportion of patients identified harboring alterations with or without US Food and Drug Administration–approved therapies, and total testing costs. A budget impact analysis assessed the economic effects of increasing the proportion of NGS-tested patients. RESULTS In a hypothetical 1,000,000-member health plan, 2,066 Medicare-insured patients and 156 commercially insured patients were estimated to have mNSCLC and to be eligible for testing. Time-to-test results were 2.0 weeks for NGS and the hotspot panel, faster than exclusionary and sequential testing by 2.7 and 2.8 weeks, respectively. NGS was associated with cost savings for both CMS ($1,393,678; $1,530,869; and $2,140,795 less than exclusionary, sequential testing, and hotspot panels, respectively) and commercial payers ($3,809; $127,402; and $250,842 less than exclusionary, sequential testing, and hotspot panels, respectively). Increasing the proportion of NGS-tested patients translated into substantial cost savings for both CMS and commercial payers. CONCLUSION Use of upfront NGS testing in patients with mNSCLC was associated with substantial cost savings and shorter time-to-test results for both CMS and commercial payers.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".