Heart rate variability and turbulence to determine true coronary artery disease in patients with ST segment depression without angina during exercise stress testing
Bibliographic record
Abstract
PURPOSE: ST segment depression without angina during an exercise stress test causes diagnostic problems, particularly in non-diabetic patients. Heart rate variability (HRV) and heart rate turbulence (HRT) are used to evaluate the changes in cardiac autonomic functions and are also both decreased in patients with coronary artery disease. The aim of this study was determine the values of HRV and HRT that discriminate true coronary artery disease from false positive stress test results. METHODS: Ninety non-diabetic patients who underwent diagnostic coronary angiography (CA) due to suspected coronary artery disease after ST segment depression without angina during an exercise stress test were enrolled in the study. Prior to CA, 24 hour ambulatory electrocardiogram recordings were taken and HRV and HRT parameters were calculated. RESULTS: Patients were divided into three groups according to the severity of their coronary lesions: (group 1 normal, group 2 non-obstructive and group 3 obstructive. There were no differences among the groups with regards to age, sex, medical history, medications, systolic and diastolic blood pressures, body mass index, fasting glucose, anemia and thyroid status, lipid profile and creatinine clearance. HRV parameters and turbulence slope (TS) were significantly lower while turbulence onset (TO) was significantly higher in group 3 than groups 1 and 2. According to the cut-off values calculated using ROC analysis, SDNN≤69.63 msec, TO > 0.14%, and TS≤2.78 msec/RR have high diagnostic accuracy for predicting obstructive coronary artery disease. CONCLUSION: HRV and HRT parameters may provide additional information for discriminating between patients who do and do not truly need CA.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".