Assessment of cardiovascular risk tools as predictors of cardiovascular disease events in systemic lupus erythematosus
Bibliographic record
Abstract
BACKGROUND: SLE is an independent risk factor for cardiovascular disease (CVD). This study aimed to determine which among QRISK2, QRISK3, Framingham Risk Score (FRS), modified Framingham Risk Score (mFRS) and SLE Cardiovascular Risk Equation (SLECRE) best predicts CVD. METHODS: This is a single-centre analysis on 1887 patients with SLE followed prospectively according to a standard protocol. Tools' scores were evaluated against CVD development at/within 10 years for patients with CVD and without CVD. For patients with CVD, the index date for risk score calculation was chosen as close to 10 years prior to CVD event. For patients without CVD, risk scores were calculated as close to 10 years prior to the most recent clinic appointment. Proportions of low-risk (<10%), intermediate-risk (10%-20%) and high-risk (>20%) patients for developing CVD according to each tool were determined, allowing sensitivity, specificity, positive/negative predictive value and concordance (c) statistics analysis. RESULTS: Among 1887 patients, 232 CVD events occurred. QRISK2 and FRS, and QRISK3 and mFRS, performed similarly. SLECRE classified the highest number of patients as intermediate and high risk. Sensitivities and specificities were 19% and 93% for QRISK2, 22% and 93% for FRS, 46% and 83% for mFRS, 47% and 78% for QRISK3, and 61% and 64% for SLECRE. Tools were similar in negative predictive value, ranging from 89% (QRISK2) to 92% (SLECRE). FRS and mFRS had the greatest c-statistics (0.73), while QRISK3 and SLECRE had the lowest (0. 67). CONCLUSION: mFRS was superior to FRS and was not outperformed by the QRISK tools. SLECRE had the highest sensitivity but the lowest specificity. mFRS is an SLE-adjusted practical tool with a simple, intuitive scoring system reasonably appropriate for ambulatory settings, with more research needed to develop more accurate CVD risk prediction tools in this 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".