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Record W3197547336 · doi:10.1016/j.hroo.2021.08.006

Maximizing detection and optimal characterization of local abnormal ventricular activity in nonischemic cardiomyopathy: LAVAMAX & LAVAFLOW

2021· article· en· W3197547336 on OpenAlexaff
Karl Magtibay, Stéphane Massé, Ahmed Niri, Robert D. Anderson, Ram B. Kumar, D. Curtis Deno, Kumaraswamy Nanthakumar

Bibliographic record

VenueHeart Rhythm O2 · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsToronto General HospitalUniversity Health Network
FundersSt. Jude MedicalAbbott Laboratories
KeywordsLavaAblationCardiologyVoltageVentricular tachycardiaIschemic cardiomyopathyInternal medicineGeologyMedicineBiomedical engineeringPhysicsHeart failureSeismology

Abstract

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BackgroundSites of local abnormal ventricular activation (LAVA) are ventricular tachycardia (VT) ablation targets. In nonischemic cardiomyopathy (NICM), minute and sparse LAVA potentials are mapped with difficulty with direction-sensitive bipolar electrograms (EGM). A method for its optimal characterization independent of electrode orientation has not been explored.ObjectiveMaximize voltages and calculate overall activation direction at LAVA sites, independent of catheter and wave direction, using omnipolar technology (OT) in NICM.MethodsFour diseased isolated human hearts from NICM patients were mapped epicardially using a high-density grid. Bipolar EGMs with at least 2 activation segments separated by at least 25 ms were identified. We used OT to maximize voltages (LAVAMAX) and measured overall wave direction (LAVAFLOW) for both segments. Clinically relevant voltage proportion (CRVP) was used to estimate the proportion of directionally corrected bipoles. Concordance and changes in direction vectors were measured via mean vector length and angular change.ResultsOT provides maximal LAVA voltages (OT: 0.83 ± 0.09 mV vs Bi: 0.61 ± 0.06 mV, P < .05) compared to bipolar EGMs. OT optimizes LAVA voltages, with 32% (CRVP) of LAVA bipoles directionally corrected by OT. OT direction vectors at LAVA sites demonstrate general concordance, with an average of 62% ± 5%. A total of 72% of direction vectors change by more than 35° at LAVA sites.ConclusionThe omnipolar mapping approach allows maximizing voltage and determining the overall direction of wavefront activity at LAVA sites in NICM. Sites of local abnormal ventricular activation (LAVA) are ventricular tachycardia (VT) ablation targets. In nonischemic cardiomyopathy (NICM), minute and sparse LAVA potentials are mapped with difficulty with direction-sensitive bipolar electrograms (EGM). A method for its optimal characterization independent of electrode orientation has not been explored. Maximize voltages and calculate overall activation direction at LAVA sites, independent of catheter and wave direction, using omnipolar technology (OT) in NICM. Four diseased isolated human hearts from NICM patients were mapped epicardially using a high-density grid. Bipolar EGMs with at least 2 activation segments separated by at least 25 ms were identified. We used OT to maximize voltages (LAVAMAX) and measured overall wave direction (LAVAFLOW) for both segments. Clinically relevant voltage proportion (CRVP) was used to estimate the proportion of directionally corrected bipoles. Concordance and changes in direction vectors were measured via mean vector length and angular change. OT provides maximal LAVA voltages (OT: 0.83 ± 0.09 mV vs Bi: 0.61 ± 0.06 mV, P < .05) compared to bipolar EGMs. OT optimizes LAVA voltages, with 32% (CRVP) of LAVA bipoles directionally corrected by OT. OT direction vectors at LAVA sites demonstrate general concordance, with an average of 62% ± 5%. A total of 72% of direction vectors change by more than 35° at LAVA sites. The omnipolar mapping approach allows maximizing voltage and determining the overall direction of wavefront activity at LAVA sites in NICM.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations5
Published2021
Admission routes1
Has abstractyes

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