ObjectiveTo construct a preoperative objective index-based model for predicting the mortality risk of aortic dissection, aiming to provide a quick risk assessment tool for primary healthcare. MethodsA total of 271 patients with thoracic aortic dissection from the Medical Information Mart for Intensive Care (MIMIC-Ⅳ) database between 2008 and 2019 were included. These patients were randomly divided into a training set, a validation set, and a test set at a ratio of 7:2:1. Based on the Akaike information criterion (AIC), forward regression was used to select the risk factors for patients with post-dissection mortality, and the XGBoost algorithm was employed to establish the prediction model. The SHAP (SHapley Additive exPlanation) theory was used for interpretive analysis. ResultsOut of the 271 patients of aortic dissection, 158 were males and 113 were females, with a median age of 70.3 (58.8, 79.5) years. The training set, validation set, and test set consisted of 189, 54, and 28 patients respectively. During the follow-up period, 99 deaths (36.5%) occurred. Using the forward stepwise regression based on the AIC criterion, 18 preoperative independent predictors were identified. An XGBoost prediction model was constructed accordingly. After grid search optimization, the model demonstrated good discrimination and calibration in both the validation set [area under the curve (AUC)=0.681] and the test set (AUC=0.735). The SHAP analysis indicated that age (SHAP=0.081), activated partial thromboplastin time (SHAP=0.065), and red cell distribution width (SHAP=0.038) were the top three predictive contributors. ConclusionThe aortic dissection mortality risk prediction model constructed based on the XGBoost algorithm can effectively predict the incidence of mortality outcomes. Characteristic indicators such as age, activated partial thromboplastin time, and red cell distribution width can assist clinicians in identifying high-risk patients, making triage referral decisions, and optimizing preoperative interventions within the golden time window, ultimately aiming to reduce the mortality rate of patients with aortic dissection.
ObjectiveTo investigate effectiveness and safety of transcatheter aortic valve replacement in the treatment of aortic regurgitation. Methods PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, Wanfang Data and VIP were searched from inception to August 2021. According to the criteria of inclusion and exclusion, two reviewers independently screened the literature, extracted the data and evaluated the quality of the included studies. Then, Stata 16.0 software was used for meta-analysis. Subgroup meta-analysis of valve type used and study type was performed. ResultsTwenty-five studies (12 cohort studies and 13 single-arm studies) were included with 4 370 patients. Meta-analysis results showed that an incidence of device success was 87% (95%CI 0.81-0.92). The success rate of the new generation valve subgroup was 93% (95%CI 0.89-0.96), and the early generation valve subgroup was 66% (95%CI 0.56-0.75). In addition, the 30-day all-cause mortality was 7% (95%CI 0.05-0.10), the 30-day cardiac mortality was 4% (95%CI 0.01-0.07), the incidence of pacemaker implantation was 10% (95%CI 0.08-0.13), and the incidence of conversion to thoracotomy was 2% (95%CI 0.01-0.04). The incidence of moderate or higher paravalvular aortic regurgitation was 6% (95%CI 0.03-0.09). Conclusion Transcatheter aortic valve replacement for aortic regurgitation is safe and yields good results, but some limitations can not be overcome. Therefore, multicenter randomized controlled trials are needed to confirm our results.
