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Record W4283788841 · doi:10.1093/eurjcn/zvac060.088

Implementation of the cardiovascular assessment screening program (CASP) by nurse practitioners-it's time to focus on early identification of cardiovascular risk

2022· article· en· W4283788841 on OpenAlexaffabout
Jill Bruneau, Donna Moralejo, Katelyn A. Parsons, Catherine Donovan

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineCASPBiorepositoryIntervention (counseling)RandomizationClinical trialExploratory researchRandomized controlled trialFamily medicineEmergency medicineNursingInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Newfoundland and Labrador Support for Patient-Oriented Research (NL SUPPORT) Background There is inconsistent utilization of clinical practice guidelines (CPGs) for cardiovascular disease (CVD) screening and management by advanced practice nurses/nurses practitioners (NPs) to identify CVD risk factors early and to intervene using current recommendations. To address this clinical practice issue and increase utilization of Canadian CPGs, an exploratory multiphase sequential mixed methods study was conducted and the Cardiovascular Assessment Screening Program (CASP) was developed, implemented, and evaluated. Objective Phase 2 of the mixed methods study, a cluster randomized controlled trial (cRCT), evaluated the implementation of CASP by NPs with individuals aged 40-74 years, without established CVD or vascular disease (VD), in order to identify CVD risk factors early, to determine the level of risk for a CV event, to calculate the Heart Age, and to set heart health priorities and management, according to current CPGs in Canada. Methods In a cRCT, block randomization randomly divided ten NPs into the intervention group clusters (IGCs) and the control group clusters (CGCs). In turn, eight NPs recruited 166 patients in their own communities; the four NP IGCs recruited patients (n=67) and the four NP CGCs recruited patients (n=99). The web-based CASP intervention was implemented with patients in the IGCs, and the CGCs received standard care. The CASP intervention consisted of four components: an electronic CVD Database, an NP toolkit, an educational resource for NPs and patients, and a website containing CPGs. The data on CVD risk factors, Framingham Risk Score (FRS), Heart Age, and priorities for heart health were recorded in the CVD Database utilized by the NP IGCs. Results Utilizing the CVD database, CVD risk factors were consistently identified in patients by NPs in the IGCs compared to the NP CGCs. The patients in the IGCs had a high number of risk factors for CVD documented by NPs, including family history of CVD, hypertension, diabetes, obesity, renal dysfunction, and dyslipidemia, and found that 62 patients (91%) were at moderate to high risk for having a CV event in the next 10 years using the FRS. In comparison, NPs in the CGCs did minimal documentation of risk factors; the level of CVD risk was largely unknown (96%) for control group patients as the FRS was only documented on 7 patients (4%). The Heart Age was calculated in the IGCs (92%); in the CGCs, the Heart Age was not calculated. The recommendations made by IGC NPs matched their patients’ priorities 94% of the time; 75% of the intervention group patients developed personalised goals that matched their top priorities for improving heart health. Conclusion The key benefit of the CASP implementation by NPs was the identification of CVD risk factors earlier in individuals without established CVD or VD. Engaging individuals to participate in screening to learn about their CVD risk encourages priority setting for heart health using a patient-focused approach.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.393
Teacher spread0.342 · 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 teacher head, not a consensus.

Study designOther design
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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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