Prevalence and associated factors of resting electrocardiogram abnormalities among systemic lupus erythematosus patients without cardiovascular disease
Bibliographic record
Abstract
BACKGROUND: Electrocardiogram (ECG) cardiovascular disease (CVD) abnormalities (ECG-CVD) are predictive of subsequent CVD events in the general population. Systemic lupus erythematosus (SLE) patients are vulnerable to CVD. We aimed to determine the prevalence of ECG-CVD in SLE patients and to examine the risk factors associated with ECG-CVD. METHODS: A 12-lead resting supine ECG was performed on consecutive adult patients attending the clinic. One cardiologist interpreted the ECGs. ECG-CVD were defined as the presence of one or more of the following 4 elements (ECG-4): ST-segment and/or T-wave abnormalities, left ventricular hypertrophy (LVH), left axis deviation (LAD), left bundle branch block (LBBB) and right bundle branch block (RBBB). ECG-5 included the same elements as ECG-4 and the Q-wave. Repeated measurement data were created and the associations between ECG-4/ECG-5 and demographics were evaluated with univariate and multivariate Cox regression models. RESULTS: Of 487 SLE patients, 104 (21.4%) and 118 (24.2%) patients had one or more of the ECG-4 and ECG-5 elements, respectively. A higher prevalence of ECG-CVD was found in patients with a longer SLE disease duration, and the burden of ECG-CVD elements increased with age. Increased age, active SLE disease, and damage were associated with ECG4 and ECG-5, while treatment of hyperlipidemia was protective. CONCLUSION: A high prevalence of ECG-4 (21.4%) and ECG-5 (24.2%) was found in this SLE cohort. Controlling SLE disease activity is important since it was associated with ECG-4 and ECG-5. Early identification of ECG-4 and ECG-5 in SLE patients might allow for better stratification and risk management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".