Predicting adverse cardiovascular outcomes in post‐coronary artery bypass grafting patients using novel ECG frequency analysis of the QRS complex
Bibliographic record
Abstract
BACKGROUND: A novel metric called Layered Symbolic Decomposition frequency (LSDf) has been shown to be an independent predictor of ventricular arrhythmia and mortality in patients receiving implantable cardioverter-defibrillator (ICD) devices. This novel index studies the fragmentation of the QRS complex. However, its generalizability to predict cardiovascular events for other cardiac procedures is unknown. Herein, we investigated the applicability of LSDf as a predictive measure for major adverse cardiovascular events (MACE) in patients receiving coronary artery bypass grafting (CABG). METHODS AND RESULTS: One hundred ninety-five patients had high-resolution ECG recorded prior to CABG surgery in 2012/2013 and were followed for a mean duration of 7.32 ± 0.32 years for postoperative cardiovascular outcomes. These outcomes were described as a modified composite of MACE defined as hospitalization for heart failure, ventricular tachycardia, ventricular fibrillation, and cardiovascular death including stroke and cardiac arrest. One hundred seventy-two patients were included for analysis and 18 patients experienced a postoperative cardiovascular outcome. These patients had significantly increased age (71.3 vs. 64.6 years, p = .007), prolonged QRS duration (113.22 vs. 97.35 ms, p = .003), reduced left ventricular ejection fraction (42.7% vs. 56.5%, p < .001), and lower LSDf percent (13.5% vs. 16.9%, p = .002). Patients with an LSDf below 13.25% were 4.8 (OR 1.7-13.5, p < .001) times more likely to experience a MACE and up to 19.4 (OR 4.2-90.3, p < .001) times more likely to experience a MACE when older than 70 years and an ejection fraction below 50%. CONCLUSION: Layered Symbolic Decomposition frequency may be an applicable metric to predict long-term cardiovascular outcomes in patients with ischemic heart disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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".