Epicardial potentials computed from the body surface potential map using inverse electrocardiography and an individualised torso model improve sensitivity for acute myocardial infarction diagnosis
Bibliographic record
Abstract
INTRODUCTION: Epicardial potentials (EPs) derived from the body surface potential map (BSPM) improve acute myocardial infarction (AMI) diagnosis. In this study, we compared EPs derived from the 80-lead BSPM using a standard thoracic volume conductor model (TVCM) with those derived using a patient-specific torso model (PSTM) based on body mass index (BMI). METHODS: Consecutive patients presenting to both the emergency department 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, 12-lead electrocardiograms and BSPMs were recorded. The BMI for each patient was calculated. Cardiac troponin T (cTnT) was sampled 12 hours after symptom onset. Patients were excluded from analysis if they had any ECG confounders to interpretation of the ST-segment. A cardiologist assessed the 12-lead ECG for ST-segment elevation myocardial infarction by Minnesota criteria and the BSPM. BSPM ST-elevation (STE) was ⩾0.2 mV in anterior, ⩾0.1 mV in lateral, inferior, right ventricular or high right anterior and ⩾0.05 mV in posterior territories. To derive EPs, the BSPM data were interpolated to yield values at 352 nodes of a Dalhousie torso. Using an inverse solution based on the boundary element method, EPs at 98 cardiac nodes positioned within a standard TVCM were derived. The TVCM was then scaled to produce a PSTM using a model developed from computed tomography in 48 patients of varying BMIs, and EPs were recalculated. EPs >0.3 mV defined STE. A cardiologist blinded to both the 12-lead ECG and BSPM interpreted the EP map. AMI was defined as cTnT ⩾0.1 µg/L. RESULTS: . Of these, 180 (56%) had AMI. Overall, 132 had Minnesota STE on ECG (sensitivity 65%, specificity 89%) and 160 had BSPM STE (sensitivity 81%, specificity 90%). EP STE occurred in 165 patients using TVCM (sensitivity 88%, specificity 95%; p < 0.001) and in 206 patients using PSTM (sensitivity 98%, specificity 79%; p < 0.001). Of those with AMI by cTnT and EPs ⩽0.3 mV using TVCM ( n = 22), 18 (82%) patients had EPs >0.3 mV when an individualised PSTM was used. CONCLUSION: Among patients presenting with ischaemic-type chest pain at rest, EPs derived from BSPM using a novel PSTM significantly improve sensitivity for AMI diagnosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".