Key result
Rapid 90-second OPM-MCG identifies NSTEMI culprit arteries with ~64% accuracy before angiography.
Why the study?
Identifying the infarct-related artery before coronary angiography in NSTEMI is difficult due to limited sensitivity of current approaches.
Does a pre-angiography 90-second OPM-MCG recording analyzed with an XGBoost machine-learning model accurately estimate infarct-related artery probabilities and provide incremental localization value beyond standard care in haemodynamically stable patients with NSTEMI?
Cohort (n=611)
Core-lab IRA readers, ECG cardiologists, echocardiographers, and catheterization teams were blinded to MCG
No
Does a pre-angiography 90-second OPM-MCG recording analyzed with an XGBoost machine-learning model accurately estimate infarct-related artery probabilities and provide incremental localization value beyond standard care in haemodynamically stable patients with NSTEMI?
A rapid 90-second room-temperature OPM-MCG scan paired with machine learning provides informative pre-angiography culprit-artery probabilities in stable NSTEMI patients—particularly for the LAD and RCA—offering complementary diagnostic signal when added to routine ECG, echocardiographic, and clinical assessment.
Answers are grounded in this paper and cite their sources.
May add incremental localization value in stable NSTEMI; leaves open prospective validation before practice change.
Aims Identifying the infarct-related artery (IRA) before coronary angiography in non–ST-segment elevation myocardial infarction (NSTEMI) is difficult because current approaches have limited sensitivity. We evaluated whether a noninvasive 90-second optically pumped magnetometer magnetocardiography (OPM-MCG) recording can estimate pre-angiography IRA probabilities in these patients and provide information beyond standard-care variables. Methods and Results This prospective two-campus study within one hospital system included a Campus 1 development cohort (n=493) and a geographically separate Campus 2 test cohort (n=118). All patients underwent OPM-MCG before angiography. An XGBoost model using 65 prespecified OPM-MCG features was developed with nested cross-validation, locked, and evaluated once in Campus 2. IRA adjudication followed a core-laboratory hierarchy. Discrimination, calibration, and clinical utility were compared with ECG and TTE. Among 611 patients, 43% had LAD, 23% LCx, and 34% RCA IRA. In Campus 2, overall accuracy was 63.6%, macro-F1 was 60.0%; LAD/LCx/RCA vessel-specific areas under the receiver operating characteristic curve were 0.87/0.79/0.79 and top-1 sensitivity was 76.6%/34.5%/69.0%. Accuracy was 51.7% for matched ECG-only XGBoost and 55.9% for integrated standard care. In an exploratory stacked analysis, adding locked OPM-MCG probabilities to standard care increased accuracy to 67.8% (paired difference 11.9%, 95% CI 4.2–19.5; P=0.008). Conclusions In haemodynamically stable patients with NSTEMI already selected for invasive management, a locked OPM-MCG model provided informative vessel-level probabilities in Campus 2 and complementary signal with standard care. Performance was strongest for LAD and RCA. Prospective multicentre, cross-platform testing is needed before clinical use. Trial registration number: ChiCTR2300070211.
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Liu et al. (2026) conducted a cohort in Non-ST-segment elevation myocardial infarction (NSTEMI) (n=611). 90-second optically pumped magnetometer magnetocardiography (OPM-MCG) with a 65-feature XGBoost machine-learning model vs. Combined standard-care model (demographics, history, vitals, laboratory biomarkers, quantitative 12-lead ECG with V7-V9/V3R-V5R, and resting TTE) was evaluated on Pre-angiography infarct-related artery (IRA) three-class localization accuracy (LAD, LCx, or RCA) in the Campus 2 test cohort (Accuracy difference 7.6%, 95% CI -3.4 to 18.6, p=0.222). A locked 90-second OPM-MCG XGBoost model achieved 63.6% accuracy for pre-angiography culprit artery localization in NSTEMI and 67.8% when added to standard care.
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