Abstract 432: Sarcopenia predicts dose-limiting toxicity in metastatic pancreatic cancer treated with gemcitabine plus nab-paclitaxel
Bibliographic record
Abstract
Abstract Introduction: Gemcitabine (GEM) plus nab-paclitaxel (nab) has been shown to improve overall survival (OS) compared to GEM monotherapy in patients with metastatic pancreatic cancer. However, GEM/nab is associated with increased toxicity. Our study evaluated whether sarcopenia increased the likelihood of chemotherapy toxicity in pancreatic cancer treated with GEM/nab. Methods: A retrospective review was performed of all patients who received GEM/nab as first-line therapy for metastatic pancreatic cancer at a northern Alberta cancer institute (Canada) from 2014-2017. Patients were included if a computed tomography (CT) scan of the abdomen and pelvis was performed within 60 days of starting chemotherapy. Skeletal muscle surface area was measured at L3 on baseline CT scans and normalized for height to determine skeletal muscle index (SMI, cm2/m2). Dose-limiting toxicity (DLT) was defined as dose reduction or treatment discontinuation due to toxicity. Optimal stratification was used to establish sex-specific SMI cut-offs with DLT as an outcome. Results: 152 patients were included in the study. 88 patients (57.8%) were male and median age was 66.5 years (range 34-95). 62 patients (40.8%) experienced DLT. SMI cut-offs were determined as <48.0 cm2/m2 in males and <39.55 cm2/m2 in females. Sarcopenia prevalence using these cut-offs was 54.6%. DLT incidence was significantly higher in sarcopenic versus non-sarcopenic patients (55.4 vs. 23.2% respectively, p<0.001). In multivariate logistic regression accounting for advanced age (>65), sex, BMI category, and performance status (PS), sarcopenia significantly increased the likelihood of DLT (OR 7.21, 95% CI 3.02-17.24, p<0.001). Sarcopenia did not impact OS (HR 1.30, 95% CI 0.94-1.80, p=0.118) or progression-free survival (HR 0.66, 95% CI 0.42-1.03, p=0.071). Conclusion: In pancreatic cancer treated with GEM/nab, sarcopenic patients are significantly more likely to experience DLT, independent of age, sex, BMI, and PS. These findings could have implications for reduced chemotherapy dosing in sarcopenic patients. Citation Format: Susie Youn, Angela Chen, Vincent Ha, Carole Chambers, Dean T. Eurich, Michael McCall, Michael B. Sawyer. Sarcopenia predicts dose-limiting toxicity in metastatic pancreatic cancer treated with gemcitabine plus nab-paclitaxel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 432.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".