Cardiovascular events (CVEs) associated with tyrosine kinase inhibitor (TKI) therapy in patients with metastatic renal cell carcinoma (mRCC) at a regional cancer center
Bibliographic record
Abstract
e16040 Background: Recent studies suggest the incidence of CVEs associated with TKIs has been underestimated. Phase III trials have reported low incidences of heart failure, cardiac ischemia and hypertension. A recent observational study reported that one third of patients taking sunitinib or sorafenib experienced a cardiovascular event often without symptoms. We assessed the incidence of CVEs in mRCC patients who received TKIs at our centre. Methods: Eligible mRCC patients were identified from a mRCC database between January 2006 and November 2008. Data was retrospectively extracted including age, sex, diagnosis, histology, past cardiac history, cardiac risk factors, number and type of TKI regimens, and CVEs. A CVE was defined as unexplained death, acute coronary syndrome (ACS), heart failure, or arrhythmia requiring intervention. We also identified any new or exacerbated cases of hypertension after the start of TKI therapy, as a CVE. Results: Eighty-five eligible patients were identified. Average age was 61 years (range, 23–78), 72% were male and 80% were clear cell in origin. A total of 31 CVEs occurred in 28 patients (33%). These events occurred at a median of 5 weeks of TKI therapy (range, 1 - 64 weeks). There were 8 cases of ACS, 2 of heart failure, 2 of arrhythmia, and 3 unknown causes of death. Only 2 of these particular CVEs were associated with new or increased hypertension. There were 16 cases of hypertension alone. Those who had CVEs had a higher mean number of cardiac risk factors. They were also more likely to have an echocardiogram during treatment, and less likely to receive sorafenib following sunitinib. Conclusions: Our study suggests a lower rate of CVEs than recent studies, but the true rate may be underestimated, as routine cardiac studies were not performed in all patients. Rational surveillance strategies for patients receiving TKI therapies should be developed. Prospective trials should address predictive and prognostic factors for CVEs. [Table: see text] [Table: see text]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".