Kim D, et al. J Korean Med Sci. 2022 Mar;37(10):e81. https://doi.org/10.3346/jkms.2022.37.e81

Kim D, et al. J Korean Med Sci. 2023 Oct;38:e322. https://doi.org/10.3346/jkms.2023.38.e322

Background: Acute pulmonary embolism (PE) is a critical condition where the timely and accurate assessment of right ventricular (RV) dysfunction is important for patient management. Given the limited availability of echocardiography in emergency departments (EDs), an artificial intelligence (AI) application that can identify RV dysfunction from electrocardiograms (ECGs) could improve the treatment of acute PE. Methods: This retrospective study analyzed adult acute PE patients in an ED from January 2021 to December 2023. We evaluated a smartphone application which analyzes printed ECGs to generate digital biomarkers for various conditions, including RV dysfunction (QCG-RVDys). The biomarker’s performance was compared with that of cardiologists and emergency physicians. Results: Among 116 included patients, 35 (30.2%) were diagnosed with RV dysfunction. The QCG-RVDys score demonstrated significant effectiveness in identifying RV dysfunction, with a receiver operating characteristic–area under the curve (AUC) of 0.895 (95% CI, 0.829–0.960), surpassing traditional biomarkers such as Troponin I (AUC: 0.692, 95% CI: 0.536–0.847) and ProBNP (AUC: 0.655, 95% CI: 0.532–0.778). Binarized based on the Youden Index, QCG-RVDys achieved an AUC of 0.845 (95% CI: 0.778–0.911), with a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 91.2% (95% CI: 82.4–100%), 77.8% (95% CI: 69.1–86.4%), 63.3% (95% CI: 54.4–73.9%), and 95.5% (95% CI: 90.8–100%), respectively, significantly outperforming all the expert clinicians, with their AUCs ranging from 0.628 to 0.683. Conclusions: The application demonstrates promise in rapidly assessing RV dysfunction in acute PE patients. Its high NPV could streamline patient management, potentially reducing the reliance on echocardiography in emergency settings.

Lee SH, et al. Yonsei Med J. 2024 Mar;65(3):174-180. https://doi.org/10.3349/ymj.2023.0341

Acute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary arteries in OHCA patients who regained spontaneous circulation. Conducted from 1 July 2019 to 30 June 2022 at a tertiary university hospital emergency department, it involved 82 OHCA patients, with 58 qualifying after exclusions. The AI used was the Quantitative ECG (QCG™) system, which provides a STEMI diagnostic score ranging from 0 to 100. The QCG score’s diagnostic performance was compared to assessments by two emergency physicians and three cardiologists. Among the patients, coronary occlusion was identified in 24. The QCG score showed a significant difference between occlusion and non-occlusion groups, with the former scoring higher. The QCG biomarker had an area under the curve (AUC) of 0.770, outperforming the expert group’s AUC of 0.676. It demonstrated 70.8% sensitivity and 79.4% specificity. These findings suggest that the AI-based ECG biomarker could predict coronary occlusion in resuscitated OHCA patients, and it was non-inferior to the consensus of the expert group.

Kim JH, et al. Yonsei Med J. 2024 Nov;66:e6. https://doi.org/10.3349/ymj.2024.0148