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Record W2805081409 · doi:10.7759/cureus.2701

Management of Atrial Fibrillation in Patients on Ibrutinib: A Cleveland Clinic Experience

2018· article· en· W2805081409 on OpenAlexaff
Sidra Khalid, Samin Yasar, Aariez Khalid, Timothy Spiro, Abdo Haddad, Hamed Daw

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineIbrutinibAtrial fibrillationInternal medicineCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Background Ibrutinib is a Bruton’s tyrosine kinase inhibitor, which is United States Food and Drug Administration (FDA)-approved for chronic lymphocytic leukemia, mantle cell lymphoma, and Waldenström’s macroglobulinemia. Ibrutinib is associated with atrial fibrillation and bleeding events. Our aim is to determine the management of prior atrial fibrillation when starting ibrutinib, as well as ibrutinib-induced atrial fibrillation. Our focus is on which rate and rhythm control strategies to use and decisions regarding the use of antiplatelet and anticoagulation agents. Materials and Methods We conducted a retrospective descriptive study of case records over a three-year period from February 2014 to February 2017. We reviewed 597 patient charts from the Cleveland Clinic database. Ibrutinib was started in 43 patients. Of those, 10 had atrial fibrillation prior to starting ibrutinib and four developed atrial fibrillation while on ibrutinib. Data was collected for demographic details, co-morbid conditions, CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke, transient ischemic attack or thromboembolism, vascular disease, age, and sex category) score, HAS-BLED (hypertension, abnormal renal and liver function, stroke, bleeding, labile INR, elderly, and drugs or alcohol) score, and drugs used for antiplatelet effects, for anticoagulation, and for rate and rhythm control. Outcomes for embolic and bleeding events were assessed. Results Of the 43 patients, 14 (32.5%) had or developed atrial fibrillation; 10 (23.26%) had prior atrial fibrillation, and four (9.30%) developed atrial fibrillation after starting ibrutinib. The majority were males (71.42%) and Caucasian (71.42%). The disease breakdown was chronic lymphocytic leukemia (42.86%), mantle cell lymphoma (50%), and Waldenström’s macroglobulinemia (7.14%). The mean starting dose of ibrutinib in patients with prior atrial fibrillation was 569 mg and for patients who developed atrial fibrillation was 420 mg. In the 10 patients who had atrial fibrillation prior to ibrutinib, all 10 were on beta blockers, one was on diltiazem, three were on amiodarone, one was on flecainide, one was on digoxin, and one was on Tikosyn® (Pfizer, Inc., New York, NY). The ibrutinib dose was decreased/discontinued in two patients. In patients who developed atrial fibrillation after starting ibrutinib, three were on beta blockers, two on amiodarone, and one on Tikosyn. Ibrutinib was discontinued in one patient. In patients who had prior atrial fibrillation, three were on warfarin, one on enoxaparin, and two on apixaban. In three patients, aspirin and enoxaparin were discontinued. In patients who developed atrial fibrillation after starting ibrutinib, enoxaparin was given to two and apixaban to one. None of the patients had a stroke, transient ischemic attack (TIA), or bleeding events. Conclusions From our study, we concluded that ibrutinib can be safely given in the presence of atrial fibrillation, and when atrial fibrillation was induced, we further concluded that beta blockers were the preferred agents for rate control. Ibrutinib has many drug interactions with other rate and rhythm control agents; hence, their use was lower. When atrial fibrillation was uncontrolled, ibrutinib was temporarily held and then cautiously restarted. The decision to start or adjust anticoagulation depended on the bleeding and stroke risks as assessed by their physicians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.043
GPT teacher head0.371
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations20
Published2018
Admission routes1
Has abstractyes

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