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Record W4377227225 · doi:10.1093/jsxmed/qdad060.197

(205) Erectile Dysfunction is an Independent Risk Factor for Major Adverse Cardiovascular Events

2023· article· en· W4377227225 on OpenAlexaff
Uday Mann, Rupinder Brar, Premal Patel

Bibliographic record

VenueThe Journal of Sexual Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineMaceDyslipidemiaInternal medicinePopulationHazard ratioStroke (engine)Retrospective cohort studyDiabetes mellitusProportional hazards modelMyocardial infarctionDiseasePercutaneous coronary interventionEnvironmental healthConfidence interval

Abstract

fetched live from OpenAlex

Abstract Introduction Erectile dysfunction (ED) and cardiovascular disease share similar risk profiles which include: aging, hypertension, diabetes, smoking, obesity, and dyslipidemia. Previous literature has suggested ED as a potential harbinger of future cardiovascular disease. Objective As such, we sought to investigate the association between ED and major adverse cardiovascular events (MACE) using a large population based database. As a secondary objective, we sought to investigate the relationship of place of residence (rural or urban) in regards to the incidence of MACE. Methods A propensity-weighted, retrospective cohort study was conducted by accessing provincial health administrative databases. Eligibility criteria included men 18 years and older, with no prior ED or MACE, who had at least 1 year of provincial health coverage from their index date between June 1st 1996 to March 31st 2018. ED was defined as having at least two ED prescriptions filled within one year (including oral, intraurethral, and/or injection therapies). MACE was defined as myocardial infarction, coronary revascularization procedures, ischemic stroke, or hospitalizations for heart failure. We then classified study groups into ED Urban, ED Rural, No ED Urban and No ED Rural. Multiple logistic regression model that included age categories, socioeconomic status, index year, diabetes, hypertension, dyslipidemia and renal disease was used to determine the propensity score. Stabilized inverse propensity treatment weighting was then applied to the propensity score. A cox proportional hazard model was used to examine our primary outcome of time to a MACE. Results The median time to a MACE was 2721, 2620, 2520, and 2438 days in the ED Urban (N=32,138), ED Rural (N=17,821), No ED Rural (N=145,209) and No ED Urban (N=233,073) study groups, respectfully. The ED Rural, ED Urban and No ED Rural study groups had a 54% (Hazard Ratio [HR] 1.54, 95% CI [1.45 – 1.63]), 26% (HR 1.26, 95% CI[1.20 – 1.32]) and 14% (HR 1.14, 95% CI[1.11 – 1.18]) higher risk of a MACE event as compared to the No ED Urban group, in weighted analyses, respectfully. Among individuals with ED, men from a rural setting had a 22% (HR 1.22, 95% CI[1.14 – 1.32]) higher risk of a MACE event, as compared from an urban setting. Conclusions Our study demonstrates that men diagnosed with ED had a higher risk of MACE as compared to controls. ED is demonstrated to be an independent risk factor for MACE when controlling for comorbidities. In addition, men residing in rural communities had a higher risk of MACE as compared to other urban counterparts. It is imperative for health care professionals who manage patients with ED to discuss the risk of future cardiovascular disease and identify comorbid conditions to mitigate risk. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Boston Scientific.

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 score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.052
GPT teacher head0.350
Teacher spread0.298 · 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
Published2023
Admission routes1
Has abstractyes

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