A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department

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™), 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.

Feasibility of Artificial Intelligence–Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study

“…In terms of CAD, previous studies have demonstrated the feasibility and fine performance of AI-ECG models as a rapid screening tool for ACS in patients presenting with acute chest pain [ 6 - 8 ]. Although various model structures with different AI algorithms have been used, these models have focused on the prediction of ACS.…”

“…Therefore, there may be difficulties in applying these AI-ECG models in daily clinical practice, as additional software may be required for input signal transformation. In comparison, the QCG analyzer allows 12-lead ECG image data as input, extracting wave signals and vectorizing them through the initial encoding step [ 5 , 6 ]. Previous reports have validated the consistent performance of the QCG analyzer for printed ECG images and ECG photographs obtained as screenshots from a smartphone [ 5 ].…”

“…The development process for the QCG analyzer ( Figure 1 ), which operates on a mobile platform as a smartphone app, has been reported previously [ 5 , 6 ]. The QCG analyzer is composed of an encoder and various task-specific artificial neural network layers.…”

Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis

“… 5 Preliminary evidence has shown the diagnostic capability of STEMI through AI-based ECG analysis to be comparable to that of a skilled cardiologist, potentially allowing for reductions in cardiac catheterization laboratory activation time and door-to-balloon time. 6 …”

“…Some of these challenges may be mitigated through the development of AI capable of analyzing ECG image outputs. 6 9 Nonetheless, the diversity in shape and format of pre-hospital ECG outputs, combined with the high risk of contamination, hampers the direct application of these algorithms to real-world data. Therefore, creating an AI solution that can accurately interpret pre-hospital ECG data to inform the risk of STEMI would represent a significant technological advancement in this field.…”