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 systematically evaluate the differences in outcomes between functional mitral regurgitation (FMR) and degenerative mitral regurgitation (DMR) in patients treated with transcatheter edge-to-edge repair (TEER) using the MitraClip device. MethodsA systematic literature search was conducted in PubMed, Embase, the Cochrane Library, Web of Science, the CNKI, Wanfang Database, VIP Database, and the CBM from their inception to January 2024. Two researchers independently performed study selection, data extraction, and risk of bias assessment. The quality of cohort studies was evaluated using the Newcastle-Ottawa Scale (NOS). A meta-analysis was performed using Stata 18.0 software. ResultsA total of 13 cohort studies involving 6 402 patients were included, comprising 4 161 patients in the FMR group and 2 241 in the DMR group. All included studies had NOS scores of ≥6 points. The meta-analysis revealed that compared to the DMR group, the FMR group had a higher 1-year all-cause mortality rate [OR=1.53, 95%CI (1.30, 1.81), P＜0.01] and a higher 1-year rehospitalization rate for heart failure [OR=1.90, 95%CI (1.60, 2.26), P＜0.01]. Conversely, the FMR group had a lower post-procedural mean transmitral gradient [SMD=–0.47, 95%CI (–0.65, –0.30), P＜0.01] and a lower rate of subsequent mitral valve surgery [OR=0.41, 95%CI (0.20, 0.83), P=0.01]. ConclusionFollowing MitraClip therapy, patients with FMR exhibit favorable short-term outcomes, but their mid- to long-term outcomes are inferior to those of patients with DMR. When determining the treatment strategy with MitraClip, the specific etiology of mitral regurgitation should be considered for a more accurate prediction of therapeutic efficacy and prognosis.
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.