Abstract 13421: Spontaneous Coronary Artery Dissection in Patients With and Without Chronic Systemic Inflammatory Diseases
Bibliographic record
Abstract
Background: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of myocardial infarction (MI). Chronic systemic inflammatory diseases (CIDs) are common amongst SCAD patients. Whether they contribute to the cause of SCAD or were simply bystanders remain unknown. Methods: We compared the baseline demographics, presentation characteristics and cardiovascular outcomes between patients with and without CIDs in the Canadian SCAD Study. CIDs included systemic inflammatory disorders as well as autoimmune diseases. Patients completed questionnaires along with detailed history of CIDs. Blood biomarkers for inflammation (e.g. C-reactive protein [CRP], erythrocyte sedimentation rate [ESR], white blood cell counts) were obtained at the discretion of the treating physicians. Major adverse cardiovascular events (MACE) were defined as the composite of all-cause mortality, stroke or transient ischemic attack, MI, hospitalization for heart failure and unplanned revascularization. Results: Of 1225 patients with SCAD, 96 (7.8%) had a history of CIDs. Patients with CIDs were of similar age (52.5 vs.51.8 yrs, p=0.51), but were more likely to be women (96.9% vs 89.2%, p=0.02). There were no significant differences in baseline demographics, ECG, or angiographic findings between CID or non-CID cohorts. Among patients with biomarkers performed, there was no difference in CRP (9.6 vs. 12.9mg/L, p=0.54), ESR (10 vs. 13.9, p=0.42), white cell count (12.6 vs. 11.3, p=0.80), neutrophil count (5.1 vs. 5.6, p=0.70), or lymphocyte count (1.7 vs. 2.0, p=0.41), in CID versus non-CID groups, respectively. The proportion of patients treated conservatively or with revascularization were not different between groups. At median follow up of 3.0yrs (IQR 2.0-3.8), in hospital and overall MACE were not significantly different between CID and non-CID patients (8.4% vs. 6.1%, p=0.38) and (23% vs. 16.8%, p=0.12), respectively. Conclusion: In our large SCAD cohort, there were no significant differences in presentation or outcomes in SCAD patients with or without CID. Importantly, there was no difference in levels of inflammatory biomarkers, supporting the hypothesis that active inflammation was not an important contributor of SCAD.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".