Hypertension as a Cardiovascular Toxicity of Bruton’s Tyrosine Kinase Inhibitors for Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
In 2014, ibrutinib, a covalent Bruton’s tyrosine kinase inhibitor (BTKi), became available as a treatment for chronic lymphocytic leukemia (CLL) in Canada. It was welcomed with enthusiasm given its oral administration, lower rate of neutropenia and infections, and efficacy in heavily pretreated and high-risk del17p subtypes, at a time when only chemotherapy and monoclonal antibodies were available. Soon adverse events (AE) due to off-target effects emerged; particularly concerning were cardiac arrhythmias, bleeding, and hypertension (HTN). Second-generation BTKis were designed to target BTK more directly, and fortunately, clinical trials reported a lower rate of cardiac AEs. Less attention has been given to HTN despite it being an important modifiable risk factor for subsequent cardiac AEs. This is particularly relevant for CLL, given that the predominantly elderly patient population with CLL tend to survive as long as age-matched peers. This review focuses on HTN as an adverse effect of BTKis, and recommending a management approach. Many contemporary CLL studies with BTKi define HTN as an AE using the Common Terminology Criteria for Adverse Events (CTCAE) v4.0, and typically consider severe HTN to be grade 3-5 (Table 1). HTN is generally defined as a disorder characterized by a pathological increase in blood pressure (BP), which is defined as a repeated elevation in the BP exceeding 140 over 90 mm Hg. It is clinically relevant even at a grade 2 level from a global perspective for most patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".