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Record W4322631811 · doi:10.1016/j.hrthm.2023.02.025

Mechanics of lumenless pacing lead strength during extraction procedures based on laboratory bench testing

2023· article· en· W4322631811 on OpenAlexaff
Pierce J. Vatterott, Blandine Mondésert, Mark Marshall, Thomas Lulic, Bruce L. Wilkoff

Bibliographic record

VenueHeart Rhythm · 2023
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsMontreal Heart Institute
FundersServierBoston Scientific CorporationCook MedicalMedtronicPfizer
KeywordsMedicineLead (geology)Extraction (chemistry)Chromatography

Abstract

fetched live from OpenAlex

BackgroundWith the advent of conduction system pacing, use of the Medtronic SelectSecure Model 3830 lead has increased substantially. However, with this increased use, the potential need for lead extraction also will increase. Lumenless lead construction requires an understanding of both applicable tensile forces as well as lead preparation techniques that can influence consistent extraction.ObjectiveThe purpose of this study was to use bench testing methodologies to characterize the physical properties of lumenless leads and to describe related lead preparation methods that support known extraction techniques.MethodsMultiple 3830 lead preparation techniques, commonly used in extraction practices, were compared on the bench to assess rail strength (RS) in simple traction and use conditions with simulated scar. Retention of the IS1 connector vs severing the lead body preparation techniques were compared. Distal snare and rotational extraction tools were evaluated.ResultsThe retained connector method provided higher RS compared to the modified cut lead method: mean 11.42 lbf (9.85–12.73 lbf) vs 8.51 lbf (1.66–14.32 lbf), respectively. Snare use distally did not significantly affect RS: mean 11.05 lbf (8.58–13.95 lbf). Lead damage occurred with the TightRail extraction tool at angles ≥90°, which could occur with right-sided implants.ConclusionsWhen extracting SelectSecure leads, the retained connector method to maintain cable engagement benefits preservation of the extraction RS. Limiting traction force to <10 lbf (4.5 kgf) and avoiding poor lead preparation methods are critical to consistent extraction. Femoral snaring does not change RS when needed and offers a method to regain lead rail in cases of distal cable fracture. With the advent of conduction system pacing, use of the Medtronic SelectSecure Model 3830 lead has increased substantially. However, with this increased use, the potential need for lead extraction also will increase. Lumenless lead construction requires an understanding of both applicable tensile forces as well as lead preparation techniques that can influence consistent extraction. The purpose of this study was to use bench testing methodologies to characterize the physical properties of lumenless leads and to describe related lead preparation methods that support known extraction techniques. Multiple 3830 lead preparation techniques, commonly used in extraction practices, were compared on the bench to assess rail strength (RS) in simple traction and use conditions with simulated scar. Retention of the IS1 connector vs severing the lead body preparation techniques were compared. Distal snare and rotational extraction tools were evaluated. The retained connector method provided higher RS compared to the modified cut lead method: mean 11.42 lbf (9.85–12.73 lbf) vs 8.51 lbf (1.66–14.32 lbf), respectively. Snare use distally did not significantly affect RS: mean 11.05 lbf (8.58–13.95 lbf). Lead damage occurred with the TightRail extraction tool at angles ≥90°, which could occur with right-sided implants. When extracting SelectSecure leads, the retained connector method to maintain cable engagement benefits preservation of the extraction RS. Limiting traction force to <10 lbf (4.5 kgf) and avoiding poor lead preparation methods are critical to consistent extraction. Femoral snaring does not change RS when needed and offers a method to regain lead rail in cases of distal cable fracture.

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 score0.365
Threshold uncertainty score0.473

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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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