Thrombotic risk in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer treated with CDK 4/6 inhibitors: a meta-analysis
Bibliographic record
Abstract
Abstract Background Breast cancer is ranked as the most common cancer worldwide. The use of CDK4/6 inhibitors has improved the prognosis and has become a new strategy for hormone receptor -positive, human epidermal growth factor receptor-2-negative breast cancer; however, such drugs have been found to increase the risk of thrombosis in randomized controlled trials (RCTs), and this risk may be higher in the real-world setting. This study aimed to compare the thromboembolic risk of CDK4/6 inhibitors plus endocrine therapy (ET) and ET alone in RCTs and determine the incidence of thromboembolic events associated with the use of CDK4/6 inhibitors in RCTs and in the real world. Methods PubMed and EMBASE databases were searched up to December 31, 2022, for RCTs and cohort studies of CDK4/6 inhibitors in patients with breast cancer. The quality of the literature was assessed using the Cochrane Handbook and Newcastle–Ottawa Scale, and meta-analysis was performed using Review Manager 5.4 and R version 4.2.2. Results A total of 13 RCTs and 9 real-world studies were identified and included in this analysis. RCTs only reported venous thromboembolic events (VTEs); VTEs occurred in 192 patients (2.1%) in the CDK4/6 inhibitor group and 55 patients (0.7%) in the control group. Compared with ET alone, receiving CDK4/6 inhibitors plus ET increased the risk of VTEs in patients with breast cancer, with an odds ratio of 2.67 (95% confidence interval [CI]: 1.98, 3.59, p < 0.001). In real-world studies, the aggregate incidence rate of thromboembolic events was found to be 4.5% (95% CI: 2.2, 7.5). Conclusions CDK4/6 inhibitors combined with ET are associated with a significantly increased risk of VTEs in women with breast cancer compared with ET alone. The incidence of thromboembolic events was higher with CDK4/6 inhibitors in the real world than in RCTs.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".