Real-World Treatment Patterns, Healthcare Resource Utilization, and Healthcare Costs in the First-Line Treatment of Metastatic Non-Small Cell Lung Cancer in the US
Bibliographic record
Abstract
This study characterizes real-world treatment patterns and economic and healthcare resource utilization (HCRU) burden associated with first-line (1L) treatment of metastatic non-small cell lung cancer (NSCLC) without actionable alterations in the United States. This retrospective observational study used Optum Clinformatics® data. A total of 15,659 patients with metastatic NSCLC who started 1L treatment between January 2020 and March 2023 were included (52% male; mean age at the start of 1L treatment 71.7 years; 86% Medicare Advantage). The most frequent 1L regimens were immune checkpoint inhibitor (ICI) + platinum-based chemotherapy (PBCT) (47%), PBCT only (26%), and ICI only (20%). The median 1L treatment duration was 4.2 months (range 2.7–6.5) and was shorter with chemotherapy-only regimens. Outpatient visits accounted for the majority of HCRU (mean 6.6 visits per patient per month [PPPM]). Outpatient, inpatient, and emergency department visits were highest for chemotherapy-only regimens. Mean total (all-cause) healthcare costs were $32,215 PPPM and were highest for ICI + chemotherapy ($34,741–38,454 PPPM). Inpatient costs PPPM were highest for PBCT ($4725) and ICI + non-PBCT ($4648). First-line treatment of metastatic NSCLC without actionable alterations imposes a notable HCRU and cost burden, underscoring the need for better treatment options to improve outcomes and reduce economic impact.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".