ObjectiveTo systematically evaluate the differences in efficacy and outcomes between patients with functional mitral regurgitation (SMR) and degenerative mitral regurgitation (DMR) treated with mitral edge-to-edge repair (TEER) using MitraClip. MethodsPubMed, EMbase, the Cochrane Library, Web of Science, China Biomedical Literature Database (CBM), CNKI, Wanfang database, and VIP database were searched in computer. Relevant literature from the database from its establishment to January 2024 was covered. Literature screening, data extraction, and risk of bias assessment for the included studies were performed independently by two researchers. Meta-analysis was performed using Stata18.0 software. ResultsFourteen papers were finally included, including 6 707 patients, including 4 161 patients in the SMR group and 2 241 patients in the DMR group. Meta-analysis results showed that patients in the SMR group had a higher 1-year all-cause mortality rate [OR=1.53, 95%CI (1.30, 1.81), P<0.01, I2=0%] and 1-year readmission rate for heart failure [OR=1.9, 95%CI (1.60, 2.26), P<0.01, I2=0%] after MitraClip treatment than the DMR group patients. Postoperative mitral transvalvular pressure difference [SMD=-0.47, 95%CI (-0.65, -0.30), P<0.01, I2=51%] was lower in patients in the SMR group than in those in the DMR group, and the incidence of subsequent secondary open-heart surgery [OR=0.41, 95%CI (0.20, 0.83), P=0.01, I2=0%] was lower in patients in the SMR group. ConclusionThe results of Meta-analysis showed that after MitraClip treatment, patients in the SMR group showed better efficacy in the short term, but the medium- and long-term efficacy was not as good as that of patients in the DMR group. The specific type of mitral regurgitation should be considered when choosing a MitraClip treatment strategy to more accurately predict efficacy and prognosis.
ObjectiveTo develop a predictive model for acute respiratory distress syndrome (ARDS) following cardiac mechanical valve replacement under cardiopulmonary bypass (CPB) using artificial intelligence algorithms, providing a novel method for early identification of high-risk ARDS patients. MethodsPatients undergoing CPB-assisted cardiac mechanical valve replacement surgery in the Department of Cardiovascular Surgery at the First Hospital of Lanzhou University from January 2023 to March 2025 were retrospectively and consecutively enrolled. Data processing and model construction were performed using Python software. Variables with missing data proportions ≥30% were excluded, while multiple imputation combined with sensitivity analysis and standardization was applied to the remaining dataset. The dataset was randomly partitioned into training (70%) and testing (30%) sets. Feature selection was conducted using the Boruta algorithm and least absolute shrinkage and selection operator regression. The synthetic minority over-sampling technique edited nearest neighbors (SMOTEEN) algorithm was applied to balance samples in the training set. Six machine learning models, including random forest, light gradient boosting machine, extreme gradient boosting, categorical boosting (CatBoost), gradient boosting decision tree, and logistic regression, were developed through 5-fold nested cross-validation for parameter optimization. Model performance was evaluated via area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, average precision, recall rate, and F1 score. The optimal model was determined based on AUC values and validated through Hosmer-Lemeshow (HL) goodness-of-fit test. Decision curve analysis was performed for all models, while SHAP algorithm was employed for feature interpretation and visualization. External validation was conducted using clinical data from patients who underwent CPB-assisted mechanical valve replacement between April 1 and October 1, 2025. ResultsA total of 352 patients were included [training set: n=246, 135 males, 111 females, aged (51.71±11.03) years; testing set: n=106, 62 males, 44 females, aged (53.27±9.67) years], with 34 (9.7%) patients developing early ARDS in ICU. Key predictors included cardioplegia duration, right atrial transverse diameter, right ventricular transverse diameter, indirect bilirubin, and rewarming time. The CatBoost model demonstrated superior performance (AUC=0.828) with HL test P=0.64. In the single-center temporal validation cohort [n=41, 25 males, 16 females, aged (52.18±10.56) years], the CatBoost model achieved AUC=0.771. ConclusionCardiac arrest duration, right atrial transverse diameter, right ventricular transverse diameter, indirect bilirubin, and rewarming time are identified as critical factors influencing postoperative ARDS development after CPB-assisted mechanical valve replacement. The CatBoost model exhibits excellent accuracy, consistency, and clinical applicability.
ObjectiveTo evaluate the clinical outcomes of sutureless aortic valve replacement (SU-AVR) and transcatheter aortic valve implantation (TAVI) for aortic valve disease. MethodsWe conducted a computer-based search of databases including CNKI, WanFang Data, VIP, CBM, PubMed, The Cochrane Library, EMbase and Web of Science from the inception of the databases to March 2024. Two reviewers independently screened articles, extracted data and used the Cochrane bias risk assessment tool to evaluate the quality of the included studies. Meta-analysis was performed using Stata 18 software. ResultsThe included 17 studies using propensity-matched analysis consisted of 6 630 patients, including 3 319 patients in the SU-AVR group and 3 311 patients in the TAVI group. The SU-AVR group had lower mortality than the TAVI group at 1-year ［RR=0.58, 95%CI(0.38, 0.87), P=0.009］, 2-year ［RR=0.61, 95%CI(0.43,0.85), P=0.004］ and 5-year ［RR=0.63, 95%CI(0.50,0.79), P=0.000］. The SU-AVR group had a significantly lower rate of new permanent pacemaker implantation (PPI) ［RR=0.75, 95%CI(0.58, 0.98), P=0.037］, moderate-to-severe paravalvular leak (PVL) ［RR=0.20, 95%CI（0.12, 0.32）, P=0.000］, myocardial infarction(MI)［RR=0.30, 95%CI (0.11,0.80), P=0.017］, more-than-mild residual aortic regurgitation (AR)［RR=0.29, 95%CI（0.17, 0.48）, P=0.000］. In addition, the SU-AVR group had a higher postoperative mean aortic gradient [SMD=0.39, 95%CI (0.17, 0.62), P=0.000］than the TAVI group. Conclusion The early and mid-term clinical outcomes of SU-AVR were superior compared to TAVI.
