Introduction and objectives: This study aimed to retrospectively analyze the anatomical characteristics and classification of multiple coronary artery fistulas (MCAFs), and to compare the outcomes of transcatheter closure between MCAFs and single fistulas.
Methods: All patients who underwent attempts at transcatheter closure of coronary artery fistulas (CAFs) at Fuwai Hospital from 2010 to 2023 were retrospectively reviewed. Patients were categorized into single fistula and MCAFs groups, and anatomical characteristics and transcatheter closure outcomes were compared between the 2 groups.
Results: This retrospective study included 146 patients who underwent attempted transcatheter closure of CAFs, with a 14.38% failure rate. Among the 146 patients with CAFs, 32.19% were identified as having MCAFs, with types I, II, and III constituting 40.43%, 42.55%, and 17.02%, respectively. Unlike single fistulas, which predominantly originated from the right coronary artery and terminated in the left ventricle, MCAFs mainly had simultaneous origins from the right coronary artery and left anterior descending artery (29.79%), and predominantly drained into the pulmonary artery (70.21%), with a notable prevalence of plexus-like morphology (38.3% vs 2.02%, P<.001). The success rate of transcatheter closure was significantly lower for multiple fistulas compared with single fistula (64.29% vs 84.34%, P=.011). Multivariate regression analysis indicated that the risk of closure failure for MCAFs was 2.64 times that of single fistulas.
Conclusions: MCAFs are common among CAFs and can be classified into 3 types based on the number and location of their origins and terminations. The risk of failure of transcatheter closure is significantly higher in MCAFs than in single fistulas.
Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU.
This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation.
We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883).
Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU.