Abstract 989: Do physician-reported toxicities accurately reflect patient-reported symptom burden? An analysis of ESAS and CTCAE for patients with lung cancer
Bibliographic record
Abstract
Abstract Purpose/Objectives: Symptom adverse events can be quantitatively monitored by oncologists with the U.S. National Cancer Institute’s Common Terminology Criteria for Adverse Events (CTCAE). Symptoms can also be reported by patients through the Edmonton Symptom Assessment Scale (ESAS). There is increasing recognition of the importance of assessing patient-reported symptoms as part of clinical care. The aim of this study is to examine correlation between patient-reported and physician-graded symptoms in patients with lung cancer who underwent external beam thoracic radiotherapy, in an attempt to identify gaps between these parameters. Materials/Methods: Between August 2015 and July 2016, 265 patients with diagnosis of lung cancer had completed ESAS and CTCAE data obtained during weekly clinic visit while undergoing thoracic radiotherapy.The following stratification for symptom severity was used: ESAS (none, 0; mild 1-3, moderate 4-6, severe 7-10) and CTCAE scores (none, 0; mild, 1; moderate, 2; and severe, 3-4). Five associated symptoms were compared: tiredness, nausea, shortness of breath, and other (cough and dysphagia) from ESAS and fatigue, nausea, dyspnea, cough and esophagitis from CTCAE. Frequency tables and boxplots combined with the scatter plots were used to assess the distribution, correlation and to identify possible outliers. Spearman correlation coefficients were analyzed to evaluate rank-associated correlations between associated ESAS domains and CTCAE toxicities. Results: Statistical analysis showed that the associated ESAS symptoms and CTCAE toxicity pairs (tiredness/fatigue, nausea, shortness of breath/dyspnea, dysphagia/esophagitis, cough) were highly correlated (p<0.05), However, analysis showed that ESAS reported by patients screens for more severe symptoms than the toxicities graded by physicians using the CTCAE; this includes fatigue (16.1% as opposed to 0.5%), nausea (5.0% as opposed to 0.0%), dyspnea (11.8% as opposed to 2.4%), cough (2.1% as opposed to 0.2%), and esophagitis (1.5% as opposed to 0.5%). ESAS collected additional symptom domains including overall wellbeing symptoms, depression, anxiety, and spiritual wellbeing, which are not included on CTCAE. ESAS detected 6.2% of patients reporting severe depression, 8.3% with severe anxiety, 9.7% marking severe for poor overall wellbeing, and 8.3% marking severe for poor spiritual wellbeing. Conclusion: This studied demonstrated that while ESAS and CTCAE reports are correlated, patients reported more severe symptoms through ESAS compared to physician-graded toxicities from CTCAE in this group of lung cancer patients who underwent thoracic radiotherapy. Systematic acquisition of patient-reported symptoms is important to optimize clinical care and symptom management. Citation Format: Bansi Savla, Thomas Dilling, Syeda Mahrukh Naqvi, Jae K. Lee, Hsiang-Hsuan M. Yu. Do physician-reported toxicities accurately reflect patient-reported symptom burden? An analysis of ESAS and CTCAE for patients with lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 989. doi:10.1158/1538-7445.AM2017-989
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".