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Record W4397043516 · doi:10.1681/asn.20233411s1868c

Cardiovascular Risk Prediction Improvement Using Algorithm- or Formula-Based Pulse Wave Velocity: Analysis of CARTaGENE

2023· article· en· W4397043516 on OpenAlexaffabout
Louis‐Charles Desbiens, Annie‐Claire Nadeau‐Fredette, Bernhard Hametner, François Madore, Mohsen Agharazii, Rémi Goupil

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsCentre hospitalier universitaire de QuébecHôpital du Sacré-Cœur de MontréalUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsPulse Wave AnalysisPulse wave velocityPulse (music)MedicineAlgorithmInternal medicineMathematicsCardiologyComputer scienceTelecommunicationsBlood pressure

Abstract

fetched live from OpenAlex

Background: Carotid-femoral pulse wave velocity (PWV) is the gold-standard measurement for aortic stiffness and a well-established surrogate marker for cardiovascular disease. Faster and less resource-intensive methods to estimate PWV (using either formulas or integrated pulse wave analysis algorithms) have been developed but their incremental predictive value for cardiovascular outcomes remains unclear. Methods: We studied individuals aged between 40 and 69 from the population based CARTaGENE cohort (Quebec, Canada). Baseline PWV was assessed using a previously described estimation formula (formula- based PWV, or f-PWV; using age, sex, and systolic blood pressure) or estimated with the ARCSolver algorithm from central waveform characteristics obtained with the SphygmoCor device (algorithm-based PWV, or a-PWV). Major adverse cardiovascular events (MACE: cardiovascular mortality, non- fatal stroke, non-fatal myocardial infarction) during a 10-year follow-up were obtained from medico-administrative databases. Cox proportional hazards models were employed to obtain associations between PWV and MACE after adjustment for existing cardiovascular risk prediction scores (ASCVD [from revised pooled cohort equations], SCORE-2). Results: 17,548 individuals were included and 2,263 experienced a MACE during follow-up. Mean PWV values at baseline were 8.4 ± 1.4 m/s (f-PWV) and 7.9 ± 1.3 m/s (a-PWV). Both f-PWV (HR= 1.52, 95% CI [1.47-1.58]) and a-PWV (HR=1.60 [1.54-1.66]) were predictive of MACE in unadjusted models. The association between a-PWV and MACE remained significant after adjustment for ASCVD (HR= 1.14 [1.08-1.20]) and but not after adjustment for SCORE-2 (HR= 1.06 [1.00-1.13]). In contrast, f-PWV was not associated with increased MACE after adjustment for either prediction score (HR= 1.02 [0.97-1.08] for ASCVD; HR= 0.95 [0.89-1.00] for SCORE-2). Similar trends were observed after stratification for tertiles of baseline cardiovascular risk. Conclusions: Algorithm-based PWV, but not formula-based PWV, improves cardiovascular prediction beyond what is achievable with recognized prediction tools.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.244
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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 routes2
Has abstractyes

Explore more

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