042 EPICARDIAL POTENTIALS DERIVED FROM THE BODY SURFACE POTENTIAL MAP USING INVERSE ELECTROCARDIOGRAPHY IMPROVE DIAGNOSIS OF ACUTE MYOCARDIAL INFARCTION: A PROSPECTIVE STUDY
Bibliographic record
Abstract
Background Epicardial potentials (EP) derived from body surface potentials using a thoracic volume conductor model (TVCM) improve acute myocardial infarction (AMI) diagnosis. In this study, we compared EP derived from the 80-lead body surface potential map (BSPM) using a TVCM developed from CT imaging with other electrocardiographic techniques in AMI diagnosis. Methods In this prospective study, consecutive patients presenting to both the ED and pre-hospital coronary care unit between August 2009 and August 2011 with acute ischaemic-type chest pain at rest were enrolled. At first medical contact a 12-lead electrocardiogram (ECG) and BSPM were recorded. Cardiac troponin-T (cTnT) was sampled 12 h after symptom onset. AMI was diagnosed when cTnT ≥0.1 µg/l. Patients were excluded from analysis if they had bundle branch block, permanent pacemaker, left ventricular hypertrophy by voltage criteria or concomitant digitalis therapy. A cardiologist assessed the 12-lead ECG for STEMI by Minnesota criteria and the BSPM. BSPM ST-elevation (STE) was ≥0.2 mV in anterior, ≥0.1 mV in lateral, inferior, right ventricular (RV) or high right anterior and ≥0.05 mV in posterior territories indicating AMI. To derive EP, the 80-lead BSPM data were interpolated (Laplacian method) to yield values at 352-nodes of a Dalhouse torso. Using an inverse solution based on the boundary element method employing Tikhonov regularisation, EP at 98 cardiac nodes positioned within a standard TVCM were estimated. EP ≥0.3 mV defined STE. A cardiologist blinded to both the 12-lead ECG and BSPM interpreted the EP map. Results Enrolled were 400 patients (age 62±13 years; 57% male): 80 patients had exclusion criteria. Of the remaining 320 patients, 180 (56%) had AMI. Of these 180 patients, 117 had STEMI by Minnesota criteria (sensitivity 65%, specificity 89%) and 146 had BSPM STE (sensitivity 81%, specificity 90%). EP STE occurred in 158 patients (sensitivity 88%, specificity 95%, p<0.001). Of those with non-STEMI by Minnesota criteria on 12-lead ECG and AMI (n=63), 29 (46%) patients had STE detected by BSPM with a further 12 (19%) patients having STE detected only using EP derived from the BSPM using a TVCM. Overall, 41 (65%) patients with both a non-diagnostic 12-lead ECG at presentation and AMI had STE detected only by BSPM or derived EP. In 32/41 (78%) patients, STE was detected in the posterior or RV territories. All 41 patients had AMI diagnosed by EP. Conclusions Among those with an initial non-diagnostic 12-lead ECG, EP derived from BSPM using a TVCM significantly improves AMI diagnosis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".