Clinical significance of the hemodynamic gain index in patients undergoing exercise stress testing and coronary computed tomography angiography
Bibliographic record
Abstract
BACKGROUND: Many hemodynamic parameters provide limited information regarding obstructive coronary artery disease (CAD) during exercise stress testing particularly when exercise is suboptimal. Hemodynamic gain index (HGI) is a recent sensitive indicator of ischemia and has been associated with increased mortality. This study evaluated the clinical impact of HGI in patients who underwent concomitant exercise stress testing and coronary computed tomography angiography (CCTA). METHODS: A total of 284 consecutive patients from the executive health program between 2010 and 2018 were identified. Resting and peak heart rate (HR) as well as systolic blood pressure (SBP) measurements were recorded. Framingham risk score (FRS), Duke treadmill score (DTS) and HGI [Formula: see text] were calculated. The latter was divided into quartiles. CCTA was used as a reference test to detect any CAD. Multivariate analysis and artificial neural network were used to determine the independent predictors of obstructive CAD. RESULTS: Mean age was 53 ± 12 years with 83% male. Mean HGI was 1.74 ± 0.67, with cut-off value of severely blunted HGI ≤ 1.25 (Quartile 4). Patients with severely blunted HGI were older, had higher FRS, and worse DTS. Patients with obstructive CAD had lower HGI when compared to those with normal CCTA/non-obstructive CAD (1.36 ± 0.53 vs. 1.77 ± 0.67, P = 0.005), and showed a higher prevalence of severely blunted HGI (44% vs. 22%, P = 0.019). After adjusting for traditional risk factors, HGI remained an independent predictor of obstructive CAD while severely blunted HGI was associated with threefold increased odds of having obstructive CAD (P = 0.05). Using artificial intelligence analysis, severely blunted HGI independently predicted obstructive CAD with an area under the curve of 0.83 and 0.96, and normalized importance of HGI of 100% and 63%, respectively for different models. CONCLUSIONS: Among patients who underwent concomitant exercise stress testing and CCTA, severely blunted HGI independently predicted obstructive CAD after multivariate adjustment for traditional risk factors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| 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".