Abstract P2-11-08: Impact of COVID-19 on patients undergoing neoadjuvant therapy: A pre/post pandemic analysis and assessment of quality of care delivered
Bibliographic record
Abstract
Abstract Background: When the first wave of COVID-19 hit globally in early 2020, concerns were raised about access to surgical interventions for cancer patients. It was considered that neoadjuvant therapy (NAT) although conventionally given to locally advanced breast cancer may need to also be provided to earlier-stage disease. In addition, due to the temporary closure of breast cancer screening programs during the pandemic, concerns were raised about patients presenting with later-stage disease at initial diagnosis. This project aims to assess the impact of COVID-19 on the volume of neoadjuvant referrals at a large cancer centre, as well as any stage migration, impact on treatment timelines and impact on outcomes for breast cancer patients compared to the pre-pandemic population. Methods: The BC Cancer Vancouver centre has a neoadjuvant breast cancer program to ensure high quality of care is maintained. This program’s prospective database of breast cancer patients referred for and treated with NAT between the years 2012-2021 was queried to assess data on neoadjuvant referrals, clinical stage, receptor status, treatment timelines, and outcomes between January 1, 2019 - December 31, 2020. Data from the years 2019 and 2020 were compared to evaluate the impact of COVID-19 on NAT. Summary data available from earlier years were also utilized as reference. Results: The COVID-19 pandemic resulted in a 51% increase in the number of patients referred to the neoadjuvant program, with 102 patients referred for NAT in 2019, whereas 154 patients were referred in 2020. This proportional increase in referrals is higher than any other year since the database inception. Of note, during 2020 there were no COVID related closures for cancer surgeries in the province. The proportion of patients referred who received NAT remained similar between 2019 and 2020 (69.1% vs 70.8% in 2020). The trend in referrals by month varied between the two years. In 2019, the majority of patients were referred between April to July with the lowest proportion of referrals in October to December. In 2020, the opposite occurred with the lowest proportion of referrals transpiring between January - June, and the greatest proportion in October to December. The proportion of patients who presented with de-novo metastatic disease was consistent between the two years (7.8% in 2019 vs 9.7% in 2020). Despite the closure of all screening mammography programs between March-June of 2020, the clinical stage and receptor status are equivalent between 2019 and 2020. With regards to treatment timelines, there was a 3 day increase in the median time between referral date and medical oncology consultation in 2020 compared to 2019. No other treatment timeline delays were found between 2019 and 2020. With regards to outcomes, 34.9% of patients achieved pCR in 2019, but only 24.1% achieved pCR in 2020, despite similar stage and receptor subtypes. Conclusion: During the COVID-19 pandemic in 2020, a higher volume of patients were referred for NAT than had ever before been referred, despite the fact that there were no closures of operating rooms in our province for COVID-19. From a quality of care perspective there was a delay in referral to consultation for medical oncology, but no delay on referral to treatment, treatment to surgery, or surgery to radiation. However, and a significantly lower pCR rates was seen in 2020 compared to 2019. The 10% decrease in pCR rates may have resulted from increased complexity in breast cancer cases. This trend may continue, as the impact of COVID-19 on breast cancer outcomes will likely take many years to fully appreciate. Attention should be paid to encouraging women to return to regular breast screening programs to decrease the number of patients needing neoadjuvant therapy. Citation Format: Lauren Corke, Omar Hajjaj, Kaylie Willemsma, Stephen Chia, Christine Simmons. Impact of COVID-19 on patients undergoing neoadjuvant therapy: A pre/post pandemic analysis and assessment of quality of care delivered [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 P2-11-08.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".