Biomarkers of microvascular retinal perfusion in OCTA indicative of coronary heart disease
Bibliographic record
Abstract
OBJECTIVE: To assess changes in retinal perfusion and microvascular architecture associated with coronary artery stenosis (CAS) due to coronary heart disease (CHD) and establish optical coherence tomography angiography (OCTA) as a noninvasive screening tool for CHD. METHODS: In this cross-sectional exploratory study, 982 eyes of 512 patients that underwent coronary angiography due to suspected CHD were included. All patients underwent OCTA to quantify perfusion density (PD) and fractal dimension (FD) within the superficial and deep vascular plexus in 1 mm, 3 mm, and 6 mm rings placed over the macula and optic nerve head (ONH). RESULTS: The study investigated the association between maximal stenosis and retinal microvascular parameters in 512 patients. In the superficial retinal plexus, significant negative associations were found between cardiac stenosis and retinal perfusion density (PD) (p = 0.0091) and fractal dimension (FD) (p = 0.0014) in the inner ring. Similar associations were observed in the deep plexus (PD: p = 0.047; FD: p = 0.013). In the outer ring, mean PD and FD were lower in the left compared to the right eye (PD, superficial: p < 0.0001, deep: p < 0.0001; FD, superficial: p < 0.0001, deep: p = 0.0012). However, no significant associations were found in the optic nerve head. Overall, the study suggests that retinal microvascular parameters may serve as indicators of stenosis presence, especially in specific retinal regions and layers, providing insights into potential noninvasive methods for cardiovascular risk assessment. CONCLUSIONS: Retinal microvasculature parameters, such as PD and FD, may serve as indicators of CAS. Retinal imaging with OCTA might offer a noninvasive screening modality for cardiovascular risk assessment.
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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 | 0.000 |
| 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".