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Record W4411884403 · doi:10.3899/jrheum.2025-0314.59

The Incidence Rate and Risk Factors of Arrhythmias in Patients with Psoriatic Arthritis

2025· article· en· W4411884403 on OpenAlexaffvenueabout
Abdulrahman Almansouri, Ali Alhadri, Keith Colaco, Jiayi Li, Paula Harvey, Shadi Akhtari, Richard J. Cook, Vinod Chandran, Dafna Gladman, Lihi Eder

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsToronto Western HospitalKrembil FoundationWomen's College HospitalUniversity of WaterlooUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineInternal medicineAtrial fibrillationHazard ratioCardiologyCumulative incidenceProportional hazards modelVentricular tachycardiaAtrial flutterIncidence (geometry)Confidence intervalCohort

Abstract

fetched live from OpenAlex

Objectives To estimate the cumulative incidence rates (CIRs) and risk factors for cardiac tachy- and bradyarrhythmias in patients with psoriatic arthritis (PsA). Methods We performed an analyses of the University of Toronto PsA prospective cohort from its inception in 1978 to April 2024. Patients were assessed every 6 to 12 months following a standard protocol where demographic, clinical, comorbidity and medication data were collected. Arrhythmia events were identified via record linkage with provincial hospitalization databases and review of medical records by physicians. Arrhythmia endpoints were classified as atrial (defined as atrial fibrillation, flutter or supraventricular tachycardia), ventricular (defined as ventricular tachycardia, fibrillation or placement of cardiac resynchronization therapy with a defibrillator) and bradyarrhythmias (defined as second- or third-degree atrioventricular block or placement of pacemaker). Cox proportional hazard regression models were used to evaluate the association between PsA measures of disease activity (measured as time-varying (TV) or cumulative average (CA)) and each arrhythmia endpoint separately. Each model was hierarchically adjusted for potential confounders as follows: Model 1: adjusted for age and sex, model 2: also adjusted for cardiovascular risk factors, and model 3: added information on PsA therapies. Results Of 1670 PsA patients (mean age 46.35 years, 54.2% male) included in this analysis, a total of 80 atrial, 17 brady and 11 ventricular arrhythmias were identified. By 70 years of age, the overall CIRs were 0.08 (95% confidence interval (CI) 0.06-0.10), 0.01 (95% CI 0.00-0.01) and 0 (95% CI 0.00-0.01) for atrial, ventricular and bradyarrhythmias, respectively. In the fully adjusted multivariable model (Model 3, Table 1), higher Disease Activity Index for PsA (DAPSA) was associated with higher risk of atrial arrhythmia. The following variables were significantly associated with higher risk of atrial arrhythmia: higher DAPSA score, hazard ratio (HR) 1.15, 95% CI (1.03-1.29) was associated with a 10-unit higher value for CA covariate; and higher 3-visual analog scale (3-VAS) with HR of 1.18, 95% CI (1.04-1.33) for TV and 1.22, 95% CI (1.04-1.44) for CA covariates. On the other hand, remission/low vs high disease activity measured by DAPSA with HR of 0.49, 95% CI (0.26-0.92) for TV and 0.46, 95% CI (0.23-0.91) for CA covariates were significantly associated with lower risk of atrial arrhythmia. Table 1: Risk of developing atrial arrhythmia in patients with PsA using Cox proportional hazards models (N=1670, 80 events). Conclusion Higher burden of disease activity in PsA is associated with higher atrial arrhythmia risk. These findings reinforce the importance of controlling inflammation in PsA to optimize cardiac health.

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)0.003
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.239
Teacher spread0.234 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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