Use of downstream stress imaging tests for risk stratification of patients presenting to the emergency department with chest pain and low HEART score
Bibliographic record
Abstract
BACKGROUND: Patients with low HEART (History, Electrocardiogram, Age, Risk factors, and Troponin level) risk scores who are discharged from the emergency department (ED) may present clinical challenges and diagnostic dilemmas. The use of downstream non-invasive stress imaging (NISI) tests in this population remains uncertain. Therefore, this study aims to investigate the value of NISI in risk stratification and predicting cardiac events in patients with low-risk HEART scores (LRHSs). METHODS: We prospectively included 1384 patients with LRHSs between March 2019 and March 2021. All the patients underwent NISI (involving myocardial perfusion imaging/stress echocardiography). The primary endpoints included cardiac death, non-fatal myocardial infarction and unplanned coronary revascularisation. Secondary endpoints encompassed cardiovascular-related admissions or ED visits. RESULTS: The mean patient age was 64±14 years, with 670 (48.4%) being women. During the 634±104 days of follow-up, 58 (4.2%) patients experienced 62 types of primary endpoints, while 60 (4.3%) developed secondary endpoints. Multivariable Cox models, adjusted for clinical and imaging variables, showed that diabetes (HR: 2.38; p=0.008), HEART score of 3 (HR: 1.32; p=0.01), history of coronary artery disease (HR: 2.75; p=0.003), ECG changes (HR: 5.11; p<0.0001) and abnormal NISI (HR: 16.4; p<0.0001) were primary endpoint predictors, while abnormal NISI was a predictor of secondary endpoints (HR: 3.05; p<0.0001). CONCLUSIONS: NISI significantly predicted primary cardiac events and cardiovascular-related readmissions/ED visits in patients with LRHSs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".