Incidence of heart failure following exposure to a protein kinase inhibitor, a French population‐based study
Bibliographic record
Abstract
AIMS: Pharmacovigilance signals of heart failure (HF) following exposure to protein kinase inhibitors (PKIs) have been detected in recent years. Our aim was to identify the PKIs most frequently associated with the development of HF. METHODS: Using the French National Healthcare Database, all patients newly exposed to a PKI between January 2011 and June 2014 were followed up for 18 months. Specific hospitalization diagnosis and long-term HF-related disease codes were used to identify HF patients. HF incidence rate ratios (IRRs) were measured and adjusted hazard ratios (aHRs) were estimated using a Cox model. Sensitivity analyses were performed to limit the potential indication and competitive risk bias. RESULTS: Thirteen PKIs were studied. Among the 49 714 new PKI users registered during the study period, the mean IRR of HF was 3.38 per 100 person-years, with a median time to onset of 155 days. We found a significant increase in the incidence of HF for six medicinal products: pazopanib (aHR = 2.42, 95% confidence interval [CI] 1.67-3.52), dasatinib (aHR = 2.22, 95% CI 1.42-3.44), ruxolitinib (aHR = 2.11, 95% CI 1.69-2.64), crizotinib (aHR = 1.71, 95% CI 1.07-2.72), everolimus (aHR = 1.45, 95% CI 1.26-1.67) and vemurafenib (aHR = 1.37, 95% CI 1.01-1.86). Sensitivity analyses were consistent with our primary analysis. CONCLUSIONS: The current study provides knowledge on HF following exposure to a PKI. Additional studies could confirm these results for dasatinib, everolimus, pazopanib and ruxolitinib, and particularly for the two medicinal products with results slightly above the significance threshold, namely, crizotinib and vemurafenib, in our sensitivity analyses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".