Non–ECG-gated cardiac CT angiography in acute stroke is feasible and detects sources of embolism
Bibliographic record
Abstract
BACKGROUND: A significant portion of cryptogenic stroke is hypothesized to be secondary to cardiac embolism. However, transthoracic echocardiogram is usually delayed after stroke, and more detailed cardiac imaging is not routinely done. AIMS: This study aimed to determine whether non-ECG-gated cardiac CT angiography (cCTA) during hyperacute stroke would provide diagnostic quality images and act as an adjunct modality of cardiac imaging to detect sources of emboli. METHODS: In this single-center prospective cohort study, modified Code Stroke imaging was implemented with a 64-slice CT scanner, where the longitudinal axis of CT angiography was extended from the carina to the diaphragm. The primary outcomes of image quality, recruitment feasibility, impact on hyperacute time metrics, and additional radiation dose were assessed. Secondary outcomes consisted of detection of high-risk cardiac sources of embolism, mediastinal or lung pathology, and impact on etiologic classification. RESULTS: One hundred and twenty eligible patients were enrolled, of which 105 (87.5%) had good/moderate quality images for motion artifact and 119 (99.2%) for contrast opacification. Total CT time, door-to-needle time, and door-to-groin puncture time were unchanged with the addition of cCTA. Eighty-nine patients received a final diagnosis of ischemic stroke, of which 12/89 (13.5%) had high-risk cardioembolic findings on cCTA. Incidental findings, such as pulmonary embolism (PE) (7/89, 7.9%) and malignancy (6/89, 6.7%), were observed. cCTA led to changes in management for 19/120 (15.8%) of all patients, and reclassification of stroke etiology for 8/89 (9%) of patients. CONCLUSIONS: Non-ECG-gated cCTA can be feasibly incorporated into Code Stroke and provide diagnostic quality images without delays in hyperacute time metrics. It can detect high-risk cardiac sources, and other findings impacting patient care. This may help reclassify a subset of cryptogenic stroke cases and improve secondary prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".