Background: In academic hospitals, cardiology fellows may be the first point of contact for patients presenting with suspected STE elevation myocardial infarction (STEMI) or acute coronary syndrome (ACS). In this study, we sought to determine the role of handheld ultrasound (HHU) in patients with suspected acute MI (AMI) when used by fellows in training, its association with year of training in cardiology fellowship, and its influence on clinical care. Methods: This prospective study’s sample population was comprised of patients who presented to Loma Linda University Medical Center Emergency Department with suspected acute STEMI. On call cardiology fellows performed bedside cardiac HHU at time of AMI activation. All patients subsequently underwent standard TTE. The impact of the detection of WMA on HHU in regards to clinical decision making, including whether the patient would undergo urgent invasive angiography, was also evaluated. Results: 82 patients (mean age 65 years, 70% male) were included. The use of HHU by cardiology fellows resulted in a concordance correlation coefficient of 0.71 (95% confidence interval: 0.58 - 0.81) between HHU and TTE for LVEF, and a concordance correlation coefficient of 0.76 (0.65 - 0.84) for wall motion score index (WMSI). Patients with WMA on HHU were more likely to undergo invasive angiogram during hospitalization (96% vs 75%, p<0.01). The time interval between performance of HHU to initiation of cardiac catheterization (time-to-cath) was shorter in patients with abnormal vs normal HHU exams (58 ± 32 minutes versus 218 ± 388 min, p=0.06). Lastly, among patients who underwent angiography, those with WMA were more likely to undergo angiography within 90 minutes of presentation (96% vs 66%, p<0.001). Conclusion: HHU can be reliably used by cardiology fellows in training for measurement of LVEF and assessment of wall motion abnormalities, with good correlation to findings obtained via standard TTE. HHU-identified WMA at first contact were associated with higher rates of angiography as well as sooner angiography compared to patients without WMA.

Several algorithms based on 12-lead ECG measurements have been proposed to identify right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originated. However, a clinical-grade artificial intelligence algorithm is not available yet, which can automatically analyze characteristics of 12-lead ECGs and predict RVOT to LVOT origins of VT and PVC. We randomly sampled training, validation, and testing datasets from 420 patients who underwent successful catheter ablation (CA) to treat VT or PVCs, containing (340, 80%), (38, 9%), and (42, 10%) patients, respectively. We iteratively trained an AI algorithm that was supplied with 1,600,800 features extracted from 12-lead ECGs of the patients in the training cohort. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated from the internal validation dataset to choose an optimal discretization cutoff threshold. After running on the testing dataset, the proposed approach attained the following performance metrics and 95% CIs (confidence intervals), accuracy (ACC) of 97.62 (87.44 -99.99), weighted F1-score of 98.46 (90-100), AUC of 98.99 (96.89-100), sensitivity (SE) of 96.97 (82.54-99.89), and specificity (SP) of 100 (62.97-100). The proposed multi-stage diagnostic scheme attained clinical-grade precision of prediction for LVOT and RVOT locations of VT origin with fewer applicability restrictions than prior studies.