A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department
Journal of Korean Medical Science 2022³â 37±Ç 10È£ p.81 ~ p.81
±èµ¿¼º(Kim Dong-Sung) - Seoul National University Bundang Hospital Department of Emergency Medicine
ȲÁöÀº(Hwang Ji-Eun) - Seoul National University Bundang Hospital Department of Emergency Medicine
Á¶¿µÁø(Cho Young-Jin) - Seoul National University Bundang Hospital Department of Cardiology
Á¶Çü¿ø(Cho Hyoung-Won) - Seoul National University Bundang Hospital Department of Cardiology
ÀÌ¿øÀç(Lee Won-Jae) - Seoul National University Bundang Hospital Department of Cardiology
ÀÌÁöÇö(Lee Ji-Hyun) - Seoul National University Bundang Hospital Department of Cardiology
¿ÀÀÏ¿µ(Oh Il-Young) - Seoul National University Bundang Hospital Department of Cardiology
¹é¼ö¹Î(Baek Su-Min) - Seoul National University Bundang Hospital Department of Emergency Medicine
ÀÌÀº°æ(Lee Eun-Kyoung) - Seoul National University Bundang Hospital Department of Emergency Medicine
±èÁßÈñ(Kim Joong-Hee) - Seoul National University Bundang Hospital Department of Emergency Medicine
Abstract
Background: Rapid revascularization is the key to better patient outcomes in ST-elevation myocardial infarction (STEMI). Direct activation of cardiac catheterization laboratory (CCL) using artificial intelligence (AI) interpretation of initial electrocardiography (ECG) might help reduce door-to-balloon (D2B) time. To prove that this approach is feasible and beneficial, we assessed the non-inferiority of such a process over conventional evaluation and estimated its clinical benefits, including a reduction in D2B time, medical cost, and 1-year mortality.
Methods: This is a single-center retrospective study of emergency department (ED) patients suspected of having STEMI from January 2021 to June 2021. Quantitative ECG (QCG¢â), a comprehensive cardiovascular evaluation system, was used for screening. The non-inferiority of the AI-driven CCL activation over joint clinical evaluation by emergency physicians and cardiologists was tested using a 5% non-inferiority margin.
Results: Eighty patients (STEMI, 54 patients [67.5%]) were analyzed. The area under the curve of QCG score was 0.947. Binned at 50 (binary QCG), the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 98.1% (95% confidence interval [CI], 94.6%, 100.0%), 76.9% (95% CI, 60.7%, 93.1%), 89.8% (95% CI, 82.1%, 97.5%) and 95.2% (95% CI, 86.1%, 100.0%), respectively. The difference in sensitivity and specificity between binary QCG and the joint clinical decision was 3.7% (95% CI, ?3.5%, 10.9%) and 19.2% (95% CI, ?4.7%, 43.1%), respectively, confirming the non-inferiority. The estimated median reduction in D2B time, evaluation cost, and the relative risk of 1-year mortality were 11.0 minutes (interquartile range [IQR], 7.3?20.0 minutes), 26,902.2 KRW (22.78 USD) per STEMI patient, and 12.39% (IQR, 7.51?22.54%), respectively.
Conclusion: AI-assisted CCL activation using initial ECG is feasible. If such a policy is implemented, it would be reasonable to expect some reduction in D2B time, medical cost, and 1-year mortality.
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Artificial Intelligence, Myocardial Infarction, Triage, Time-to-Treatment, Myocardial Revascularization
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À¯È¿¼º°á°ú(Recomendation)
AI interpretation of initial ECGs was non-inferior to joint clinical evaluation by EPs and cardiologists in screening STEMI in ED. Therefore, CCL activation based only on AI interpretation of initial ECG is feasible.