Time trends of drug specific adverse events among patients on androgen receptor antagonists: Implications for remote monitoring.
Bibliographic record
Abstract
40 Background: In light of the global pandemic, reducing patient exposure via remote monitoring is desirable. Currently, advanced prostate cancer patients prescribed Abiraterone or Enzalutamide are scheduled for an in-person appointment monthly, to screen for adverse events (AEs). We set out to determine time trends of drug specific AEs in order to determine whether reducing in-person visits for patients taking either Abiraterone or Enzalutamide is feasible. Methods: This chart review was conducted on 667 unique advanced prostate cancer patients, being either metastatic hormone sensitive or castration resistant and utilizing Abiraterone or Enzalutamide. Patients who switched courses of treatment and received both drugs were included twice in the data, resulting in 828 “subjects” overall. Data were collected via accessing electronic patient records, to determine the first sign of an AE related to either Abiraterone or Enzalutamide. These AEs include; hypertension, elevated liver enzymes (bilirubin, AST, ALT) or hypokalemia. Survival analysis was used to determine the time to adverse event. All grade AEs are included in this analysis. Results: In this study, 425 and 403 patients received Enzalutamide and Abiraterone, respectively. In total, 36.3% of those who took Enzalutamide experienced an AE, compared to 43.4% of patients on Abiraterone. For patients utilizing Abiraterone, cumulative incidence of AEs at 3,6,9 and 12 months were: 65.0%, 81.2%, 90.9% and 93.9%, respectively. Among Enzalutamide users, cumulative incidence of AEs at 3,6,9 and 12 months were: 46.8%, 67.5%, 81.2% and 88.3%, respectively. The primary first AEs associated with Enzalutamide consumption were hypertension and liver dysfunction (77.48% and 22.52%). In the Abiraterone group, the first associated AEs were liver dysfunction (48.78%), hypertension (46.34%), and hypokalemia (4.88%). Conclusions: These data suggest that the likelihood of attaining AEs associated with Abiraterone or Enzalutamide utilization decreases over time and tend to occur within the first 6 months of therapy. Furthermore, the vast majority of these AEs can be remotely monitored via outside laboratories and remote blood pressure monitoring. In light of the COVID-19 crisis, remote monitoring after 6 months of taking Abiraterone or Enzalutamide would appear appropriate. Efforts to further safely reduce in person visits should be explored.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".