Objective To systematically evaluate the clinical effects of remote ischaemic preconditioning (RIPC) in elective vascular surgery. Methods Electronic searches were conducted in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, Wanfang Data, VIP Database, and CBM. Relevant randomized controlled trials (RCTs) were screened according to inclusion and exclusion criteria. Meta-analysis was performed using RevMan 5.3 software, and the risk of bias was assessed using the Cochrane risk of bias tool. Results A total of 15 studies involving 1 382 patients were included. The meta-analysis results showed no statistically significant difference between RIPC and non-RIPC groups in reducing perioperative mortality in elective vascular surgery (P>0.05). There were also no statistically significant differences between the two groups of vascular surgery patients regarding the incidence of myocardial infarction, renal injury, postoperative stroke, postoperative length of hospital stay, duration of surgery or total anesthesia time, or the incidence of limb injury, arrhythmia, heart failure, and pneumonia (P>0.05). Conclusion For patients undergoing elective vascular surgery, there are no significant differences between RIPC and non-RIPC in terms of perioperative mortality and other clinical endpoint outcomes.
Objective To systematically evaluate the effectiveness and safety of pulsed field ablation (PFA) and radiofrequency ablation (RFA) in the treatment of atrial fibrillation. Methods Computer searches were conducted on PubMed, EMbase, The Cochrane Library, The Web of Science, China Biomedical Literature Database, CNKI, Wanfang, and VIP databases, with a search period from the establishment of each database until April 2025. Two researchers conducted literature screening, data extraction, and quality evaluation of the included studies based on predetermined inclusion and exclusion criteria. Standardized electronic forms were used for data extraction, with a focus on the balanced dataset after propensity score matching (PSM). Quality evaluation was conducted using the improved Newcastle Ottawa scale (NOS). Meta analysis was conducted using RevMan 5.4 and Stata 18.0 software, and subgroup analysis was performed based on the study type (whether PSM method was used or not). ResultsFinally, 14 studies were ultimately included, of which 6 studies applied the PSM method, with a total sample size of 3 172 cases (PFA group: 1 582 cases; RFA group: 1 590 cases. NOS score≥5 points. The meta-analysis results showed that the PFA group had a lower recurrence rate of atrial fibrillation [OR=0.75, 95%CI (0.63, 0.90), P=0.002], surgical complications [OR=0.63, 95%CI (0.41, 0.98), P=0.04], and surgical time [WMD=–37.32, 95%CI (–45.85, –28.78), P<0.001] compared to the RFA group, and the differences were statistically significant. In addition, compared to the PFA group, the RFA group had a shorter X-ray exposure time [WMD=7.65, 95%CI (4.41, 10.88), P<0.001], and the difference was statistically significant. There was no statistically significant difference between the two groups in terms of re ablation rate [OR=1.17, 95%CI (0.59, 2.31), P=0.65] and acute surgical success rate [OR=0.86, 95%CI (0.22, 3.35), P=0.82]. ConclusionCompared with RFA, PFA treatment for atrial fibrillation can reduce the recurrence rate, shorten the surgical time, and reduce surgical complications, indicating its good effectiveness and safety in the treatment of atrial fibrillation.
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 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.
