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Record W2547142484 · doi:10.1161/circep.110.959791

Predictors of Short-Term Complications After Implantable Cardioverter-Defibrillator Replacement

2011· article· en· W2547142484 on OpenAlexafffundabout
Andrew D. Krahn, Douglas S. Lee, David Birnie, Jeff S. Healey, Eugene Crystal, Paul Dorian, Christopher S. Simpson, Yaariv Khaykin, Douglas Cameron, Amir Janmohamed, Raymond Yee, Peter C. Austin, Judy Hardy, Jack V. Tu

Bibliographic record

VenueCirculation Arrhythmia and Electrophysiology · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsHealth Sciences CentreHamilton Health SciencesToronto General HospitalLondon Health Sciences CentreThe Scarborough HospitalUniversity of OttawaSunnybrook Health Science CentreSouthlake Regional Health CenterKingston General HospitalSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesHeart and Stroke Foundation of Canada
KeywordsMedicineContext (archaeology)Hazard ratioComplicationImplantable cardioverter-defibrillatorSurgeryPopulationCanadian Cardiovascular SocietyProspective cohort studyInternal medicineAnginaMyocardial infarctionConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Complications after implantable cardioverter-defibrillator (ICD) replacement are often clinically devastating, particularly when infection or reoperation occurs. Identifying factors contributing to complications may permit identification of high-risk individuals that warrant incremental monitoring and therapy to attenuate risk. In addition, replacement may be a discretionary decision in the context of an advisory or borderline device performance and patient, device, and implanter factors that predict adverse outcome may assist in clinical decision-making. METHODS AND RESULTS: In a prospective, multicenter, population-based registry of all ICD patients at 18 centers in Ontario, Canada, we examined 45-day complication and all-cause mortality rates from February 2007 to August 2009 in patients undergoing ICD generator replacement. Complications were determined longitudinally and were categorized as major or minor. ICD replacement was performed in 1081 of 5176 patients (20.8%) undergoing ICD implantation (age, 64.3±12.7 years; 78.5% men). In patients undergoing ICD replacement, 47 patients (4.3%) had a complication within 45 days, with 47 major complications in 28 patients (2.6%), most commonly infection (n=23), lead revision (n=35), electrical storm (n=14), and pulmonary edema (n=13). Minor complications occurred in 2.3% of patients, most commonly incisional infection (n=10) and pocket hematoma (n=10). On multivariable analysis, risk factors associated with major complications were Canadian Cardiovascular Society angina class (adjusted hazard ratio [HR], 3.70 for class 2 to 4 versus 0 to 1; P=0.027) and multiple previous procedures on the pocket (adjusted HR, 3.35 for >1 versus 1; P=0.058). Risk factors associated with any complication were the use of antiarrhythmic therapy (adjusted HR, 6.29; P=0.001), implanter volume (adjusted HR 10.4 for <60/y versus >120/y, P=0.026), and Canadian Cardiovascular Society angina class (adjusted HR, 3.00 for class 2 to 4 versus 0 to 1; P=0.031). In a Cox model with a time-dependent variable of major complication within 45 days after replacement, major complications after ICD replacement were associated with an increased risk of mortality at 45, 90, and 180 days (adjusted HR, 9.61, 12.69, and 6.41, respectively; P=0.002 to 0.039). CONCLUSIONS: Risk factors associated with complications after ICD replacement include the presence of angina, antiarrhythmic therapy, increased number of previous procedures, and low implanter volume. Major complications may be associated with increased risk of subsequent mortality.

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)0.006
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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".

Quick stats

Citations165
Published2011
Admission routes3
Has abstractyes

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