Real-World Treatment Patterns and Overall Survival of Patients with Metastatic Castration-Resistant Prostate Cancer in the US Prior to PARP Inhibitors
Bibliographic record
Abstract
INTRODUCTION: Therapeutic options for metastatic castration-resistant prostate cancer (mCRPC) patients are continuously advancing. We described mCRPC treatment patterns in the US from 2013 to 2019. METHODS: Patients with a confirmed mCRPC diagnosis and adenocarcinoma histology were included in the US Flatiron Health Electronic Health Record-derived de-identified database. Treatment patterns [including treatment per lines of therapies (LOTs), LOT sequences, and time on treatment] and overall survival (OS) have been described in mCRPC settings. RESULTS: Of 5213 patients (mean age: 72.6 years), 4374 (83.9%) were treated with ≥ 1 LOT post-mCRPC diagnosis (among those with ≥ 1 LOT, 55.3%, 29.5%, 14.7%, and 6.7% had ≥ 2, 3, 4, and 5 LOTs, respectively). In first line (1L), the main treatment class was next-generation hormonal agents (NHA; 62.5% of patients with ≥ 1 LOT), while the shortest and longest time on 1L were observed for chemotherapy (median 2.8 months) and NHA (median 5.1 months), respectively. The most common LOT sequences were NHA → NHA (29.4% of patients with ≥ 2 LOTs) and NHA → NHA → chemotherapy (16.7% of patients with ≥ 3 LOTs). In Kaplan-Meier analyses, the median OS was 19.4, 14.6, and 11.1 months post-1L, 2L, and 3L start, respectively. Patients who moved rapidly through LOTs had an increased risk of death. CONCLUSIONS: NHA were widely used as 1L therapy in mCRPC patients from 2013 to 2019, but time on 1L NHA treatment was on average < 6 months. While NHA → NHA was the most observed 1L → 2L LOT sequence, a plethora of other LOT sequences were observed. OS was poor, highlighting an unmet need for life-prolonging treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".