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Record W4298002020 · doi:10.3389/fcvm.2022.934214

Overdrive pacing in the acute management of osimertinib-induced ventricular arrhythmias: A case report and literature review

2022· article· en· W4298002020 on OpenAlexaff
Yanyu Zhang, Xingtong Wang, Yilin Pan, Beibei Du, Kumaraswamy Nanthakumar, Ping Yang

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsToronto General HospitalUniversity Health Network
FundersDepartment of Science and Technology of Jilin ProvinceNational Natural Science Foundation of China
KeywordsMedicineQT intervalOsimertinibCardiologyProlongationElectrocardiographyInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

QT interval prolongation and ventricular arrhythmias (VAs) induced by osimertinib, a third-generation epidermal growth factor receptor tyrosine kinase inhibitor, are life-threatening complications. However, no consensus has been achieved regarding their management. Overdrive pacing has been shown to be effective in shortening the QT interval and terminating torsade de pointes (TdP). Here, we report a case of osimertinib-induced QT prolongation accompanied by frequent VAs and TdP. Osimertinib was immediately discontinued after it was identified as the etiology for QT prolongation and VAs. A temporary pacemaker and overdrive pacing were used after other anti-arrhythmia treatments had failed and successfully shortened the QTc interval and terminated VAs. Repeated Holter monitoring at 1 week showed no remaining VAs or TdP, and the pacemaker was removed. Routine electrocardiography (ECG) surveillance was conducted afterward, and three- and 6-month follow-ups showed good recovery and normal ECG results. Vigilance is required for rare vital arrhythmias in patients taking osimertinib, and ECG surveillance should be conducted.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.287
Teacher spread0.277 · 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 designCase report
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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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