Differences in Prevalence, Extent, Severity, and Prognosis of Coronary Artery Disease Among Patients With and Without Diabetes Undergoing Coronary Computed Tomography Angiography
Bibliographic record
Abstract
OBJECTIVE: We examined the prevalence, extent, severity, and prognosis of coronary artery disease (CAD) in individuals with and without diabetes (DM) who are similar in CAD risk factors. RESEARCH DESIGN AND METHODS: We identified 23,643 consecutive individuals without known CAD undergoing coronary computed tomography angiography. A total of 3,370 DM individuals were propensity matched in a 1-to-2 fashion to 6,740 unique non-DM individuals. CAD was defined as none, nonobstructive (1-49% stenosis), or obstructive (≥ 50% stenosis). All-cause mortality was assessed by risk-adjusted Cox proportional hazards models. RESULTS: At a 2.2-year follow-up, 108 (3.2%) and 115 (1.7%) deaths occurred among DM and non-DM individuals, respectively. Compared with non-DM individuals, DM individuals possessed higher rates of obstructive CAD (37 vs. 27%) and lower rates of having normal arteries (28 vs. 36%) (P < 0.0001). CAD extent was higher for DM versus non-DM individuals for obstructive one-vessel disease (19 vs. 14%), two-vessel disease (9 vs. 7%), and three-vessel disease (9 vs. 5%) (P < 0.0001 for comparison), with higher per-segment stenosis in the proximal and mid-segments of every coronary artery (P < 0.001 for all). Compared with non-DM individuals with no CAD, risk of mortality for DM individuals was higher for those with no CAD (hazard ratio 3.63 [95% CI 1.67-7.91]; P = 0.001), nonobstructive CAD (5.25 [2.56-10.8]; P < 0.001), one-vessel disease (6.39 [2.98-13.7]; P < 0.0001), two-vessel disease (12.33 [5.622-27.1]; P < 0.0001), and three-vessel disease (13.25 [6.15-28.6]; P < 0.0001). CONCLUSIONS: Compared with matched non-DM individuals, DM individuals possess higher prevalence, extent, and severity of CAD. At comparable levels of CAD, DM individuals experience higher risk of mortality compared with non-DM individuals.
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 | 0.003 |
| 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 | 0.000 |
| 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".