Abstract P1-15-06: The impact of musculoskeletal toxicity on adherence to endocrine therapy in women with early stage breast cancer– observations in a non-trial setting
Bibliographic record
Abstract
Abstract Background: Aromatase inhibitor (AI) use is standard of care in the treatment of postmenopausal (PM) women with early stage breast cancer (EBC). Approximately 35% of patients (pts) discontinue their initially prescribed AI due to toxicity, the most commonly reported reason being musculoskeletal toxicity (MSKT). We report on the discontinuation rates of AI therapy based on MSKT in PM women with EBC treated at a tertiary care cancer centre. Methods: PM women with hormone receptor positive EBC treated with endocrine therapy (ET) that included an AI (upfront or after tamoxifen) at The Ottawa Hospital Cancer Center between 01/99 and 02/06. Data included: demographics, type of ET, duration of treatment, incidence of patient-reported MSKT and treatment of MSKT. Comparisons between ETs were analyzed using Chi-square and Fischer's t-tests. Results: A total of 626 pts, median 59 years (r: 30–92), median follow-up 98 months, with stage: I (196 pts; 31%), II (341 pts; 54%) or III (89 pts; 14%s) EBC. Treatment strategies included: AI(s) only (251 pts; 40%); tamoxifen (TAM) followed by AI(s) (323 pts; 51.6%); AI(s) followed by TAM (16 pts; 2.6%); TAM-AI(s)-TAM (24 pts; 3.8%) and unknown (12 pts). Patient-reported MSKT was experienced by significantly more women treated with AIs than TAM (64% vs 36%, p < 0.0001). Women on exemestane experienced significantly less MSKT (47%) compared to anastrozole (64%; p = 0.011) or letrozole (68% p = 0.003). Mean time to appearance of patient-reported MSKT was 20 months (23 for anastrozole; 9 for exemestane; 21 for letrozole; 23 for TAM). Longer duration of AI use did not significantly correlate with higher risk of MSKT (p = 0.65). Significantly more pts discontinued ET due to MSKT from AIs (106/469; 22%) than TAM (21/141; 14%) (P < 0.0001); and more pts stopped letrozole due to MSKT (40/149; 27%) than the other two AIs (P < 0.0001). Treatment strategies for MSKT (n = 359 cases) included: discontinuation of AI (55; 15%), switching AI (19; 5%), treatment with medication (e.g. NSAIDs, Acetaminophen, Cox 2 inhibitors, narcotics) (75; 21%), physiotherapy and medication (2; 0.6%), and no treatment (208; 58%). Treatment strategies for MSKT did not differ significantly between ETs. Women taking anastrozole received more acetaminophen and codeine (p = 0.02 vs TAM; 0.041 vs exemestane; 0.015 vs letrozole) compared to other AI's. MSKT treatment increased adherence from 43–46% with TAM (p = 0.76); 60–72% with anastrozole (p = 0.17); 33–80% with exemestane (p < 0.0001); but did not affect adherence with letrozole (52%; p = 1.0). Conclusions: This large cohort study reports similar MSKT rates with AI therapy as reported in the literature. AIs have higher incidence of self-reported MSKTs (64%) compared to TAM (36%). Patients discontinued treatment due to MSKT significantly more with AI (22%) than TAM (14%) but adherence to AI therapy increased when treated for MSKT. These encouraging results reflect the real life experience of women exposed to ET. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P1-15-06.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".