Treatment Patterns and Outcomes in Patients With Metastatic Castration-resistant Prostate Cancer in a Real-world Clinical Practice Setting in the United States
Bibliographic record
Abstract
BACKGROUND: Clinical trials have demonstrated the efficacy of several life-prolonging therapies for metastatic castration-resistant prostate cancer (mCRPC); however, real-world data on their use, survival effect, and safety are limited. Using electronic health record data from the Flatiron Health database, we studied real-world treatment patterns and health outcomes in patients with mCRPC. PATIENTS AND METHODS: We conducted a retrospective, non-interventional cohort analysis of electronic health record data of patients with confirmed mCRPC between January 2013 and September 2017. The primary objective was to describe real-world treatment patterns, including treatment type, duration, and sequencing. Secondary objectives included describing patient characteristics and clinical outcomes. RESULTS: Of 2559 patients with mCRPC, 1980 (77%) received at least 1 line of life-prolonging therapy (abiraterone, enzalutamide, docetaxel, cabazitaxel, sipuleucel-T, or radium-223). Of patients receiving first-line therapy, 49% received second-line therapy, and of these, 43% received third-line therapy. Abiraterone/prednisone and enzalutamide accounted for 65% of first-line therapies and 54% of second-line therapies. Docetaxel was the most common third-line therapy (24%). Back-to-back use of abiraterone/prednisone and enzalutamide was common. Radium-223 monotherapy use was 2% in the first-line setting, 3% in the second-line setting, and 8% in the third-line setting. The median overall survival was longer in patients who received life-prolonging therapies (23.7 months; 95% confidence interval: 22.3-25.1 months) than in those who did not (10.1 months; 95% confidence interval: 9.1-11.5 months). CONCLUSION: These real-world insights on over 2500 patients with mCRPC supplement findings from randomized controlled trials and may help to inform clinical trial design, treatment guidelines, and clinical decision-making.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".