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Record W4361298705 · doi:10.14740/jocmr4884

Risk Factors Associated With Atrial Fibrillation in Elderly Patients

2023· article· en· W4361298705 on OpenAlexvenueno aff
Jeeyong Shin, Megha Andrews, Lindsey DeJean, Nicole Debski, Alyssa Exarchakis, Julia Fleming, Roshni Gandhi, Christina Hum, Abyson Kalladanthyil, Rohini Maddigunta, Logan Napoli, Cynthia T. Nguyen, Reshma Paul, Nicole Schmalbach, Joseph Sichel, Susan M. Snyder, Matthew Stern, Subhadra Thampi, Jesse Viggiano, Gabriella Yao, Krystal Hunter, Satyajeet Roy

Bibliographic record

VenueJournal of Clinical Medicine Research · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationOdds ratioInternal medicineConfidence intervalHeart failureOddsPopulationCardiologyLogistic regression

Abstract

fetched live from OpenAlex

Background: Atrial fibrillation (AF) is the most common arrhythmia with a growing prevalence worldwide, especially in the elderly population. Patients with AF are at higher risk of serious life-threatening events and complications that may lead to long-term sequelae and reduce quality of life. The aim of our study was to examine the association of additional risk factors and comorbid medical conditions with AF in patients 65 years, or older. Methods: We performed a retrospective electronic medical record review of patients aged 65 years and older, who visited our internal medicine office between July 1, 2020 and June 30, 2021. Results: Among 2,433 patients, 418 patients (17.2%) had AF. Our analysis showed that for each unit increased in age, there was a 4.5% increase in the odds of AF (95% confidence interval (CI) 2.2-6.9%; P < 0.001). Compared to patients of Caucasian descent, African-American patients had significantly decreased odds of AF (odds ratio (OR) 0.274, 95% CI 0.141 - 0.531; P < 0.001). Patients with hypertension had 2.241 greater odds of AF (95% CI 1.421 - 3.534; P = 0.001). Additional comorbidities with significantly greater odds of AF included other cardiac arrhythmias (OR 2.523, 95% CI 1.720 - 3.720; P < 0.001), congestive heart failure (OR 3.111, 95% CI 1.674 - 5.784; P < 0.001), osteoarthritis (OR 3.014, 95% CI 2.138 - 4.247; P < 0.001), liver disease (OR 2.129, 95% CI 1.164 - 3.893; P = 0.014), and colorectal disease (OR 1.500 95% CI 1.003 - 2.243; P = 0.048). Comorbidities with significantly decreased odds of AF included other rheumatological disorder (OR 0.144, 95% CI 0.086 - 0.243; P < 0.001), non-steroidal anti-inflammatory drugs (NSAIDs) use (OR 0.206, 95% CI 0.125 - 0.338; P < 0.001), and corticosteroid use (OR 0.553, 95% CI 0.374 - 0.819; P = 0.003). Conclusions: Increasing age, hypertension, presence of other cardiac arrhythmias, congestive heart failure, osteoarthritis, liver disease, and colorectal disease are associated with increased odds of having AF.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.393
GPT teacher head0.549
Teacher spread0.155 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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