Purpose: This study aimed to evaluate the outcomes of isolated common femoral endarterectomy with patch angioplasty (IFEA) in the endovascular era. Materials and Methods: In 2012-2022, 189 limbs underwent femoral endarterectomy with patch angioplasty. Of them, 45 IFEAs were included. We evaluated safety based on early complications; efficacy with primary patency (PP) and reintervention, above-ankle amputation, or stenosis (RAS)-free survival. We also evaluated lesion characteristics and outcomes according to the inclusion criteria (IC) of vascular mimetic implant-common femoral artery (VMI-CFA) stenting trial. Results: Forty-one patients were male, and 30 IFEAs were required for claudication. No cases of early mortality occurred. Ten limbs (22%) developed localonvascular complications (hematoma, 3; lymphocele, 5; wound infection, 2), of which 8 resolved spontaneously. The overall PP and secondary patency rates were 100% at 1 year and 87% and 97% at 3 years, respectively. Twenty-one lesions (47%) did not meet the IC. The PP within the IC was 100% at 1 and 3 years, and the PP outside the IC was 100% at 1 year and 73% at 3 years (P=0.068). The overall RAS-free survival rates were 91% at 1 year and 81% at 3 years. All cases of RAS occurred in lesions outside the IC. The multivariate analysis showed that dialysis was associated with poor RAS-free survival (adjusted odds ratio, 8.56; 95% confidence interval, 1.9-35.5; P=0.005). Conclusion The recent VMI-CFA trial results should be interpreted with caution.IFEA is a low-risk and durable procedure; however, careful follow-up is warranted in patients undergoing dialysis.

Coral reef aorta (CRA) is a rare condition characterized by the distribution of rockhard calcifications in the visceral part of the aorta, leading to potentially lifethreatening symptoms, such as hypertension, congestive heart failure, and limb and visceral ischemia. The patient was a 54-year-old female who presented with leg claudication and was diagnosed with CRA using computed tomography. CRA affected the descending thoracic and abdominal aortas, including the visceral portion, leading to reduced perfusion of both limbs and the left kidney. The surgical intervention involved bypass surgery from the descending thoracic aorta proximal to the CRA to the aortic bifurcation, including reimplantation of the left renal artery. Postoperative recovery was successful and the symptoms resolved. However, the patient experienced decreased right renal function due to CRA progression three years postoperatively. Given the uncertainty regarding the optimal surgical approach for CRA, long-term considerations are crucial for its management.

Objectives: The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. Methods: A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. Results: The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.