Abstract P4-12-03: Assessment of quality of life (QoL) in patients with metastatic breast cancer (MBC) in clinical practice: A real-world multi-country survey
Bibliographic record
Abstract
Abstract Background: Patient (pt) QoL is a key factor in the treatment (tx) of ABC. There are gaps in real-world evidence of how QoL is assessed in clinical practice. We examined the importance of QoL, its influence on tx decisions, and how it is discussed in a clinical setting from pts’ and healthcare professionals’ (HCPs) perspectives. Methods: The survey was designed by a steering committee of medical oncologists and patients and ethically approved to survey HCPs and patients. Data were collected from July 2020 to May 2021 via a cross-sectional online survey of HCPs (oncologists [Onc] and oncology nurses [OncNu]) and pts with HR+/HER2- ABC in Australia, Brazil, Egypt, Germany, Italy, South Korea, and USA. HCPs were recruited via a 3rd party and surveyed on the management of ABC including the importance of QoL and its assessment in clinical practice. Pts were recruited via HCPs and advocacy groups and surveyed on the importance of QoL, how tx impacts QoL, daily activities, and work, and the frequency of QoL discussions with HCPs. All observations were assessed using a 4-point Likert scale; data were analysed descriptively. Results: 277 Onc, 225 OncNu, and 467 pts with ABC took part in the survey. 221 pts had stage III advanced (locoregionally recurrent not amenable to curative therapy) and 229 had stage IV metastatic disease. 142 pts were receiving first line (1L), 116 second line (2L), and 209 third or later line (3L+) tx. Most HCPs, 88% of Onc and 96% of OncNu, reported asking about QoL at follow-up appointments, where fewer pts report being asked about QoL by Onc (64%) and OncNu (43%). Pts at later line of therapy (LoT) less frequently reported QoL discussions with Onc; 43% of pts at 1L (n=140) reported they were always asked about QoL at follow-up appointments vs 21% at 2L (n=113) and 16% at 3L+ (n=206). Among Onc, importance of QoL for tx decisions increased with LoT; 48% reported that QoL was very important at 1L, 57% at 2L, 79% at 3L, and 85% at 4th line. In contrast, importance of QoL among pts decreased with LoT; while only 39% were comfortable discussing QoL with Onc, 73% at 1L agreed that QoL was important vs 45% at 2L and 40% at 3L+. Among pts who experienced a side effect (SE) that was not discussed with their HCP (n=96), 40% agreed this was because HCPs did not ask about it, and 28% at least moderately agreed they do not report SE as they do not want their Onc to change their tx. 81% of HCPs did not completely agree with the statement “I have enough time to discuss QoL with my pts”. Among those who reported asking pts about their QoL, 93% of HCPs (n=481) reported using questions of their own, while 11% of Onc and 30% of OncNu reported using QoL questionnaires. Only 11% of HCPs agreed that available QoL tools were specific enough to customise for their pts, and only 12% agreed they had access to QoL tools that were integrated with electronic health record systems. Among HCPs (n=341) who were familiar with QoL tools used in clinical trials for ABC, only 10% agreed that these tools were able to accurately reflect QoL, and only 11% agreed that the tools were able to capture improvements in QoL when tx delayed disease progression. Conclusion: We found disconnects between pts with ABC and HCPs around the importance of discussions around QoL; pts report a lower frequency of these discussions in later LoT than HCPs. QoL assessment tools were infrequently used by HCPs, as those currently available were not considered adequate for ABC. QoL should be formally assessed regularly using an ABC-specific QoL assessment tool. This would allow HCPs to address pt issues around QoL through focused discussions to help inform tx decisions. To ensure clinical value, a tool should be quick and easy to use, be able to be completed outside of an appointment setting, yield results that are easy to interpret and compare, and be integrated into medical records. Citation Format: Fatima Cardoso, Julie Rihani, Dawn Aubel, Adam Moore, Victoria Harmer, Nadia Harbeck, Ana Casas, Sina Haftchenary, Purnima Pathak, Eva Schumacher-Wulf. Assessment of quality of life (QoL) in patients with metastatic breast cancer (MBC) in clinical practice: A real-world multi-country survey [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-12-03.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".