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Record W4403818600 · doi:10.1093/eurheartj/ehae666.551

Long-term patient clinical benefits and healthcare utilization after pulsed field ablation in paroxysmal atrial fibrillation: sub-analyses from the multicenter inspIRE trial

2024· article· en· W4403818600 on OpenAlexaff
Tom De Potter, Maria Grimaldi, Mattias Duytschaever, Ante Anić, Johan Vijgen, P. Neuzil, Hugo Van Herendael, Atul Verma, Allan C. Skanes, Douglas S. Scherr, Helmut Pürerfellner, Gediminas Račkauskas, Pierre Jaı̈s, Vidyavathi Reddy

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcGill University Health Centre
FundersBiosense Webster
KeywordsMedicineParoxysmal atrial fibrillationAtrial fibrillationCardiologyAblationClinical trialTerm (time)Catheter ablationInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Abstract Background/Introduction The treatment of atrial fibrillation (AF) with pulsed field ablation (PFA) aims to prevent AF recurrence while reducing complications from the ablation modality. Similar to thermal ablation, PFA has proven to be safe and effective for treatment of paroxysmal AF (PAF); however, there is scarce evidence on patient-related clinical benefits, including healthcare utilization, using this new energy modality. Purpose The objective of this study is to assess long-term clinical improvements in patient care, symptoms, and quality-of-life (QOL) after PFA ablation in PAF. Methods InspIRE was a multicentre study evaluating the safety and efficacy of variable loop PFA catheters integrated with a 3D mapping system for treatment of symptomatic PAF. Patients were followed for up to 12 months for the following QOL and healthcare utilization endpoints: Atrial Fibrillation Effect on Quality-of-Life (AFEQT) score, Class I/III AAD utilization, repeat ablation, incidence of direct current cardioversion (DCCV), and cardiovascular (CV) hospitalization. Post-hoc Kaplan-Meier estimates for clinical benefit success at 12 months were provided. Success was defined as composite freedom from occurrence of the following events post-blanking: repeat ablation, CV hospitalization, AAD utilization, and DCCV post-blanking. Predictors of clinical benefit success were explored via multivariate logistic regression analysis. Results A total of 186 enrolled patients (59±10 years; 70.0% male; CHA2DS2-VASC score 1.3±1.2) underwent PVI. Compared to baseline: 1) improvements in the AFEQT composite score were seen at 12 months (P<0.001); 2) Class I/III AAD use was reduced from 81.7% to 20.1% at 6-12 months (P<0.05); and 3) the proportion of subjects with DCCV decreased from 13.7% up to 12 months before the index procedure to 3.3% up to 12 months after the index procedure (P<0.001, Figure 1). Minimal clinically important difference in QoL (≥5 points improvement in AFEQT) was achieved in 85.2% of patients. Patients free from 12-month AF/AT/AFL recurrence (asymptomatic and symptomatic) had significantly greater improvements in AFEQT scores from baseline to 12 months versus those with recurrence (P=0.01). The clinical benefit success rate at 12 months was 87.5% (Figure 2). Patients who failed the clinical benefit endpoint were counted, including those with repeat ablation (7.5%), those on new or higher dose of AAD (6.5%), those hospitalized for CV (4.8%), and those who had DCCV post-blanking (1.6%). Multivariate logistic regression analysis showed that left ventricle ejection fraction <60% (OR: 0.22; 95% CI: 0.08-0.62; P<0.05) and incidence of diabetes (OR: 7.25; 95% CI: 2.00, 26.3; p<0.05) were associated with a higher risk of clinical benefit failure. Conclusions PFA ablation of PAF patients led to a clinical benefit success rate of 87.5%, with clinically meaningful improvement in QOL, as well as a reductions in AAD use, cardioversion, and hospitalization.Figure 1.Improvements in QoL and HealthFigure 2.Kaplan-Meier Analysis of Time

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)0.009
metaresearch head score (Gemma)0.007
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 score0.009
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.006
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
GPT teacher head0.442
Teacher spread0.185 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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