Abstract S5-06: Randomized, placebo-controlled trial of duloxetine for aromatase inhibitor (AI)-associated musculoskeletal symptoms (AIMSS) in early stage breast cancer (SWOG S1202)
Bibliographic record
Abstract
Abstract Background: Adherence to AI therapy for adjuvant treatment of hormone receptor-positive breast cancer is poor, primarily because of AIMSS. Premature discontinuation of AI therapy can lead to increased likelihood of breast cancer recurrence. Duloxetine (dulox) is a serotonin norepinephrine reuptake inhibitor that is FDA-approved for treatment of multiple chronic pain disorders. Phase II data from an open label trial of dulox for treatment of AIMSS demonstrated a 61% improvement in pain. We hypothesized that treatment of AIMSS with dulox would improve average joint pain compared to placebo (plac). Methods: Postmenopausal women with stage I-III breast cancer who had been taking AI therapy for between 3 wks and 36 mo were enrolled. To be eligible, patients were required to have average pain of ≥4/10 using the Brief Pain Inventory (BPI) that developed or worsened since AI therapy initiation, and not have any contraindications to dulox therapy. Patients were randomized 1:1 to dulox 30 mg daily for 7 d then 60 mg daily for 11 wks then 30 mg daily for 7 d, or to matching plac, stratified by baseline pain (4-6 vs 7-10) and prior taxane use (yes vs no). Pain, depression, and quality of life (QoL) were assessed after 2, 6, and 12 wks of therapy, as well as at the 24 wk time point. The primary analysis used linear mixed models to examine average pain through 12 wks by arm, adjusting for the stratification factors and assessment time. Clinically significant change in average pain was defined as a ≥2-point decrease from baseline. Results: 299 patients were randomized between June 2013 and October 2015, 10 of whom were ineligible. 127 dulox-treated and 128 plac-treated patients were evaluable for the primary analysis. No sizeable imbalances in baseline factors were noted by arm. Seventeen pts reported grade 3 adverse events (AEs) (dulox: 12/138 (8.7%), plac: 5/141 (3.5%)), and 40 pts discontinued treatment because of AEs (dulox: 21 (52.5%), plac: 19 (47.5%)). Mean observed average pain, the proportion of pts experiencing clinically significant change in average pain from baseline, and percent reduction in average pain all indicated greater improvement for dulox compared with plac through 12 wks, but were similar by arm at wk 24 (12 wks after completion of intervention; see Table). In multivariable linear mixed model analysis, the BPI average pain was reduced on average by 0.82 points more on dulox compared to plac over the first 12 wks (95% CI -1.24 to -0.40, p=0.0002). Similar patterns were observed for worst pain, pain interference, joint pain, stiffness, and functioning, and QoL. Table: Observed Average Pain Scores by Assessment TimeTime PointBaseline2 weeks6 weeks12 weeks24 weeksduloxplacduloxplacduloxplacduloxplacduloxplacAverage pain5.445.493.514.412.953.962.913.453.373.42Percent reduction--34%20%46%28%46%36%37%37%Patients with clinically significant change--54%44%69%49%69%60%60%59% Conclusions: Treatment with duloxetine was superior to placebo for the treatment of AIMSS among women with early stage breast cancer, was well tolerated, and was associated with improvements in QoL. Clinicaltrials.gov NCT01598298. Citation Format: Henry NL, Unger JM, Schott AF, Fehrenbacher L, Flynn PJ, Prow D, Sharer CW, Lew DL, Moseley A, Fisch MJ, Moinpour C, Hershman DL, Wade III JL. Randomized, placebo-controlled trial of duloxetine for aromatase inhibitor (AI)-associated musculoskeletal symptoms (AIMSS) in early stage breast cancer (SWOG S1202) [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr S5-06.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".