Attrition Rates in Metastatic Renal Cell Carcinoma (mRCC) Following First Line Immunotherapy-Based Treatment: Results From the International mRCC Database Consortium (IMDC)
Bibliographic record
Abstract
BACKGROUND: Attrition rates for patients with mRCC are not well characterized in the era of immunoncology (IO)-based combinations. This study aims to quantify real-world attrition rates by line of therapy, analyze associated clinical predictors, and describe treatment sequencing across multiple international centers. METHODS: IMDC data for patients with mRCC who received first line Nivolumab + Ipilimumab (IO-IO) or IO- Vascular Endothelial Growth Factor receptor targeted therapy (VEGFR TT) (IO-VE) were included. Clinical and pathologic characteristics and outcomes were extracted. Chi-square tests were used to compare categorical variables between patients who received second line and those who did not. A logistic regression model was used to assess predictors of second line therapy initiation. RESULTS: A total of 1411 patients were identified, of whom 995 patients were treated with first line IO-IO and 434 with IO-VE. Of them, 935 (704 first line IO-IO and 231 first line IO-VE) stopped first line and were suitable for second line therapy. Reasons for stopping first line included progressive disease (PD) in 41.1%, toxicity in 24.4%, death in 3.9%, complete response in 1.5% and other in 28.3%. Among second line suitable patients, 544 (58.2%) started any second line whereas 391 (41.8%) did not. Patients who stopped first line for PD were more likely to initiate second line than those who stopped for other reasons (57.9% vs. 17.6%, P < .00001). Patients who received second line were more likely to have clear-cell histology (77.2% vs. 66.8%, P = .04), without sarcomatoid features (57.2 vs. 44.8%, P = .02), a Karnofsky performance score (KPS) of 80 or higher (80.1 vs. 73.9%, P = .01), and bone metastases (39.0 vs. 28.1%, P = .0009). (Table 2). After adjusting for IMDC criteria, only age and reason for stopping first line remained significant predictors of receiving second line therapy. Among 353 patients who stopped second line, 199 (56.4%, overall 21.3%) started third line therapy. Of the 139 patients who stopped third line, 80 (57.6%, overall 8.6%) started fourth line therapy. CONCLUSIONS: In this real-world analysis, we found that just over half of suitable patients received the subsequent line of therapy post first line. We were able to identify age and reason for stopping first line as predictors of second line therapy initiation.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".