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Record W1587451461 · doi:10.1186/ar3984

High score on PREDICTS is associated with 10-fold increased odds for the progression of subclinical atherosclerosis in SLE

2012· article· en· W1587451461 on OpenAlexfundno aff
Maureen McMahon, Lori Sahakian, JM Grossman, Brian J. Skaggs, John FitzGerald, Christina Charles‐Schoeman, Nagesh Ragavendra, Alan Gorn, George Karpouzas, Michael H. Weisman, Daniel J. Wallace, Beatrice H. Hahn

Bibliographic record

VenueArthritis Research & Therapy · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsnot available
FundersNational Institutes of HealthCanadian Arthritis NetworkNational Institute of Arthritis and Musculoskeletal and Skin DiseasesArthritis SocietyLupus Research AllianceArthritis Foundation
KeywordsRheumatologyMedicineSubclinical infectionInternal medicineOddsFold (higher-order function)CardiologyOdds ratioLogistic regressionComputer science

Abstract

fetched live from OpenAlex

Increased oxidative stress is a major contributor to atherosclerosis (ATH). Patients with SLE demonstrate high oxidative stress and increased ATH. Our group and others have reported several biomarkers and demographic variables associated with increased oxidative stress, including proinflammatory HDL (piHDL), elevated leptin, homocysteine, and increased age - each associated with subclinical ATH in SLE. Can these biomarkers of oxidative stress be combined into a risk profile that better predicts progression of atherosclerosis? Female SLE subjects not taking statins had B-mode and Doppler scanning of carotid arteries at baseline and 18 to 36 months later. Many biomarkers were tested and those separating plaque presence were chosen for additional analysis. Antioxidant function of HDL was measured as the change in fluorescence intensity caused by oxidation of DCFH after test HDL was added to standardized normal LDL. Values >1.0 indicated piHDL. Plasma levels of leptin and sTWEAK were measured by ELISA; homocysteine was determined by HPLC. Follow-up ultrasounds were completed on 210 SLE women. Overall, 21% (38) of SLE patients had new or larger plaques. Factors associated with plaque progression on bivariate analysis included the baseline presence of plaque ( P < 0.001), increased age ( P < 0.001), piHDL ( P = 0.003), high leptin levels ( P = 0.004), high sTWEAK levels ( P = 0.004), and diabetes ( P = 0.003). Although piHDL was the strongest predictor for plaque progression on multivariate analysis (OR = 5.8, 95% CI = 2.1 to 16.7), with a negative predictive value of 89%, the positive predictive value was only 46%. We used a random forests model to determine which variables were most predictive of plaque progression, and the most significant cutpoints to dichotomize each variable. The most significant predictors were age >48, piHDL, high leptin values ≥34 ng/dl, high sTWEAK >373 pg/ml and high homocysteine (≥12). We then created a PREDICTS cardiovascular risk variable, with low oxidative stress risk defined as zero to two predictors, and high stress defined as ≥3 predictors or one predictor plus diabetes. The high PREDICTS variable had a negative predictive value for plaque progression of 88%, but the positive predictive value was 63%. In multivariate analysis controlling for other cardiac risk factors and disease factors, patients with high PREDICTS had a 10.2-fold increased odds for plaque progression (95% CI = 3.9 to 27.0), and 2.1-fold increased odds for the highest quartile of IMT progression/year (95% CI = 1.05 to 4.4). Formation of a cardiovascular risk model that incorporates several biomarkers and age may provide a more complete means to identify SLE patients at risk for progression of atherosclerosis.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.096
GPT teacher head0.374
Teacher spread0.278 · 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
Published2012
Admission routes1
Has abstractyes

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