Background and Purpose An insertable cardiac monitor (ICM) has been demonstrated to be a useful tool for detecting subclinical atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS). This study aimed to identify the clinical predictors of AF in ESUS patients with ICMs. Methods We retrospectively selected consecutive patients with an ICM implanted for AF detection following ESUS. The primary endpoint was defined as any AF episode lasting for longer than 5 min. The atrial ectopic burden (AEB) was calculated as the percentage of the number of conducted QRS from atrial ectopy on Holter monitoring. Results This study included 136 patients. AF lasting ≥5 min was detected in 20 patients (14.7%) during a median follow-up period of 6.6 months (interquartile range, 3.3–10.8 months). AF patients had a higher AEB (0.20% vs. 0.02%, p <0.001) and a larger left atrial diameter (LAD, 41.0 mm vs. 35.3 mm, p <0.001) than those without AF. The areas under the receiver operating characteristic curves were 0.795 and 0.816 for the LAD and log-transformed AEB, respectively, for the best cutoff values of 38.5 mm for LAD and 0.050% for AEB. AF lasting ≥5 min was detected in 34.6% (9/26) of patients with LAD ≥38.5 mm and AEB ≥0.050%, and in 0% (0/65) of those with LAD <38.5 mm and AEB <0.050%. Conclusions AF was detected in a significant proportion of ESUS patients during a 6.6-month follow-up. The LAD and AEB are good predictors of AF and might be useful for AF risk stratification in ESUS patients.

Abdominal wall hematoma is a rare but potentially serious vascular complication that may develop after coronary angiographic procedures. In particular, an oblique muscle hematoma caused by an injury of the circumflex iliac artery is very rare, yet can be managed by conservative treatment including hydration and transfusion. However, when active bleeding continues, angiographic embolization or surgery might be needed. In this study, we report an uncommon case of injury to the circumflex iliac artery by an inappropriate introduction of the hydrophilic guidewire during the performance of a percutaneous coronary intervention.

Background An accurate quantitative analysis of coronary artery stenotic lesions is essential to make optimal clinical decisions. Recent advances in computer vision and machine learning technology have enabled the automated analysis of coronary angiography. Objective The aim of this paper is to validate the performance of artificial intelligence–based quantitative coronary angiography (AI-QCA) in comparison with that of intravascular ultrasound (IVUS). Methods This retrospective study included patients who underwent IVUS-guided coronary intervention at a single tertiary center in Korea. Proximal and distal reference areas, minimal luminal area, percent plaque burden, and lesion length were measured by AI-QCA and human experts using IVUS. First, fully automated QCA analysis was compared with IVUS analysis. Next, we adjusted the proximal and distal margins of AI-QCA to avoid geographic mismatch. Scatter plots, Pearson correlation coefficients, and Bland-Altman were used to analyze the data. Results A total of 54 significant lesions were analyzed in 47 patients. The proximal and distal reference areas, as well as the minimal luminal area, showed moderate to strong correlation between the 2 modalities (correlation coefficients of 0.57, 0.80, and 0.52, respectively; P<.001). The correlation was weaker for percent area stenosis and lesion length, although statistically significant (correlation coefficients of 0.29 and 0.33, respectively). AI-QCA tended to measure reference vessel areas smaller and lesion lengths shorter than IVUS did. Systemic proportional bias was not observed in Bland-Altman plots. The biggest cause of bias originated from the geographic mismatch of AI-QCA with IVUS. Discrepancies in the proximal or distal lesion margins were observed between the 2 modalities, which were more frequent at the distal margins. After the adjustment of proximal or distal margins, there was a stronger correlation of proximal and distal reference areas between AI-QCA and IVUS (correlation coefficients of 0.70 and 0.83, respectively). Conclusions AI-QCA showed a moderate to strong correlation compared with IVUS in analyzing coronary lesions with significant stenosis. The main discrepancy was in the perception of the distal margins by AI-QCA, and the correction of margins improved the correlation coefficients. We believe that this novel tool could provide confidence to treating physicians and help in making optimal clinical decisions.

BACKGROUND Accurate quantitative analysis of coronary artery stenotic lesions is essential to make optimal clinical decisions. Recent advances in computer vision and machine learning technology have enabled the automated analysis of coronary angiography. OBJECTIVE To validate the performance of artificial intelligence-based quantitative coronary angiography (AI-QCA) in comparison with that of intravascular ultrasound (IVUS). METHODS This retrospective study included patients who underwent IVUS-guided coronary intervention at a single center in Korea. Proximal and distal reference areas, minimal luminal area (MLA), percent plaque burden, and lesion length (LL) were measured by AI-QCA and human experts using IVUS. Scatter plots, Pearson correlation coefficients, and Bland-Altman were used to analyze the data. RESULTS A total of 54 significant lesions were analyzed in 47 patients. The proximal and distal reference areas, and MLA showed an acceptable correlation between the two modalities (correlation coefficients of 0.57, 0.80, and 0.52, respectively). The correlation was weaker for percent area stenosis and LL, although statistically significant (correlation coefficients, 0.29 and 0.33, respectively). AI-QCA tended to measure reference vessel areas smaller and lesion lengths shorter than IVUS did. The biggest cause of bias originated from the geographic mismatch of AI-QCA with IVUS. Discrepancies in the proximal and/or distal lesion margins were observed between the two modalities, which were more frequent at the distal margins. CONCLUSIONS AI-QCA showed a good correlation and acceptable accuracy compared with IVUS in analyzing coronary lesions with significant stenosis. We believe that this novel tool could provide confidence to treating physicians and help in making optimal clinical decisions.

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