Performance of Risk Score Calculators in the Identification of Coronary Artery Calcification in Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
Objective We aimed to evaluate the performance of cardiovascular (CV) risk prediction tools in identifying patients with systemic lupus erythematosus (SLE) with coronary artery calcification (CAC). Methods We conducted a post hoc analysis of a prior case-control study of adult female patients with SLE and matched controls. We excluded patients who already met cardiology guidelines for recommended statin use. We risk-stratified patients per Atherosclerotic CV Disease (ASCVD) Risk Score and SLE-specific CV Risk (SLECRISK) score and determined rates of abnormal CAC. We used 2-sample t tests, 2-sample Wilcoxon rank-sum (Mann-Whitney) tests, and chi-square tests to compare various characteristics between subgroups, and used logistic regression to assess adjusted risk for patients with SLE compared to controls. Results We analyzed 128 patients with SLE and 138 controls. Rates of abnormal CAC in the SLE and control groups were 31.3% (40/128) and 12.3% (17/138), respectively, with similar mean ASCVD Risk Scores (2.2% vs 1.9%). Both the ASCVD Risk Score and SLECRISK score had relatively high specificity (95-100%) for abnormal CAC. The ASCVD score demonstrated poor sensitivity in both control (17.6%) and SLE (15%) groups, with sensitivity doubling to 30.8% among patients with SLE with the use of the SLECRISK score. Among participants with SLE who had low ASCVD risk, those with abnormal CAC were more likely to be older and have lower glomerular filtration rate, longer disease duration, higher insulin resistance, and higher low-density lipoprotein and cholesterol levels. Conclusion The poor performance of conventional and even modified risk scores suggests a continued need for screening approaches, including CAC, to determine CV disease risk in the SLE patient population.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".