Abstract PD12-04: Long-term risks of cerebrovascular accidents (CVAs) in patients with breast cancer
Bibliographic record
Abstract
Abstract Background: Previous survivorship research has focused primarily on treatment-related risks of coronary artery disease (CAD) and congestive heart failure (CHF) even though they share common pathogenic mechanisms with CVAs. However, there are limited data on the latter. This real-world evidence study aimed to assess the associations of breast cancer treatments with subsequent development of CVAs. Methods: We identified patients diagnosed with stage I-III breast cancer in a large Canadian province from 2004 to 2017. Data from the population-based registry were linked with data from administrative sources to identify a diagnosis of CVA during follow-up after cancer treatment. Adjuvant treatment was classified as receipt of none, one, two or three depending on the number of treatment modalities (chemotherapy, radiotherapy and hormone therapy) administered. Patients with pre-existing cardiovascular disease including CAD, CHF, arrythmias and CVAs were excluded. Multivariable logistic regression analysis was performed to determine the associations of number of adjuvant treatment modalities with CVAs. Results: A total of 23,259 patients were eligible for analysis. The median age was 58 years (interquartile range, 22-101 years) and 0.5% were men. Stage distribution included 49.6% with stage I, 37.1% with stage II, and 13.4% with stage III breast cancer. Chemotherapy, radiotherapy and hormonal therapy was administered in 45.0%, 60.6%, and 68.1% of patients, respectively. While 11.0% received no adjuvant treatment, 28.7%, 35.9% and 24.4% received one, two and three modalities. At a median follow-up of 5.9 years, 1,586 (6.8%) developed new onset CVAs. The median time from diagnosis of breast cancer to CVA was 3.1 years (interquartile range, 2.7-3.5 years). In comparison, the incidence of CVAs was higher in those who received any chemotherapy (8.2% vs 5.1%, P<.001), any radiotherapy (8.3% vs 5.9%, P<.001), and any hormonal therapy (7.8% vs 6.4%, P<.001). The incidence of CVAs was 4.9%, 5.9%, 8.2% and 10.5% in patients who received none, one, two and three adjuvant treatment modalities (P<.001). After adjusting for age, patients who received two or three modalities (odds ratio [OR], 1.20; 95% confidence interval [CI], 1.03-1.41; P=.020 and OR, 1.46; 95% CI, 1.21-1.75; P<.001) experienced a higher likelihood of CVAs, compared to those who received no adjuvant therapy, while those who received one modality were at similar risk (OR,1.00; 95% CI, 0.86-1.17, P=.974). In multivariable Cox regression models adjusting for stage and treatment, patients who developed CVAs were at increased risk of death (hazard ratio, 1.44; 95% CI, 1.30-1.58; P<.001). Conclusions: The risk of CVAs in patients with resected breast cancer increases with adjuvant treatment administration. It was highest in those who received a combination of chemotherapy, radiotherapy and hormone therapy. While breast cancer survivors are monitored for coronary events and cardiomyopathy, they may benefit from surveillance of risk factors for CVAs. Citation Format: Atul Batra, Shiying Kong, Rodrigo Rigo, Winson Y Cheung. Long-term risks of cerebrovascular accidents (CVAs) in patients with breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD12-04.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".