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Record W2971102449 · doi:10.2147/vhrm.s209003

<p>PROspective evaluation of coronary FLOW reserve and molecular biomarkers in patients with established coronary artery disease the PROFLOW-trial: cross-sectional evaluation of coronary flow reserve</p>

2019· article· en· W2971102449 on OpenAlexaboutno aff
Inger Haraldsson, Li-Ming Gan, Sara Svedlund, Kristina Törngren, Helena U. Westergren, Björn Redfors, Maria Lagerström‐Fermér, Oskar Angerås, Truls Råmunddal, Pétur Pétursson, Jacob Odenstedt, Per Albertsson, David Erlinge, Elmir Ömerovic

Bibliographic record

VenueVascular Health and Risk Management · 2019
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronary flow reserveCardiologyInternal medicineMaceCoronary artery diseaseMyocardial infarctionFractional flow reserveAngioplastyProspective cohort studyAnginaCanadian Cardiovascular SocietyPercutaneous coronary interventionCoronary angiography

Abstract

fetched live from OpenAlex

BACKGROUND: Survivors of myocardial infarction (MI) are at high risk of new major adverse cardiovascular events (MACE). Coronary flow reserve (CFR) is a strong and independent predictor of MACE. Understanding the prevalence of impaired CFR in this patient group and identifying risk markers for impaired CFR are important steps in the development of personalized and targeted treatment for high-risk individuals with prior MI. METHODS: PROFLOW is a prospective, exploratory, cross-sectional open study. We used information from the SCAAR (Swedish Coronary Angiography and Angioplasty Registry) to identify high-risk patients with a history of type-1 MI. We measured CFR non-invasively in a left anterior descending artery (LAD) using transthoracic Doppler echocardiography. Coronary flow velocity was measured at rest and at maximal flow after induction of hyperemia by intravenous infusion of adenosine (140 μg/kg/min). Independent predictors of CFR were assessed with multiple linear regression. RESULTS: We included 619 patients. The median age was 69 (IQR 65-73), and 114 (18.4%) were women. Almost one-half of the patients, 285 (46.0%) had the multi-vessel disease, and 147 (23.7%) were incompletely revascularized. The majority were on optimal standard treatment eg ASA (93.1%), statins (90.0%), ACEI/ARB (82.6%) and beta-blockers (80.8%). The majority, 547 (88.4%) had no angina pectoris, and 572 (92.2%) were in NYHA class I. Evaluation of CFR was possible in 611 (98.7%) patients. Mean CFR was 2.74 (±0.79 (mean ± SD)). A substantial number of patients (39.7%) had CFR ≤2.5. In a multiple linear regression model age, dyslipidemia, smoking, hypertension, body mass index, incomplete revascularization, and treatment with angiotensin receptor blockers were independent predictors of CFR. CONCLUSION: In this high-risk group of patients with prior MI, the prevalence of impaired CFR was high. Further risk stratification with CFR in addition to traditional cardiovascular risk factors may improve predictive accuracy for future MACE in this patient population.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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