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.
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.
Objective To systematically evaluate the impact of pulmonary hypertension (PH) on the prognosis of patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Methods A computerized search was conducted in CNKI, Wanfang Data, VIP, CBM, PubMed, The Cochrane Library, EMbase, and Web of Science databases from inception to June 2023 for cohort studies on the prognostic impact of PH in severe AS patients undergoing TAVR. Two researchers independently screened the literature, extracted data, and assessed the quality of included studies. Stata 17.0 software was used for meta-analysis. Results A total of 16 cohort studies were included, all with Newcastle-Ottawa Scale scores≥7. Meta-analysis results showed that, compared with AS patients without PH, those with PH had significantly higher 1-year all-cause mortality after TAVR [OR=2.10, 95%CI (1.60, 2.75), P<0.01], 30-day all-cause mortality [OR=2.09, 95%CI (1.54, 2.83), P<0.01], and cardiovascular mortality [OR=1.49, 95%CI (1.18, 1.90), P<0.01]. The differences between the two groups in major bleeding events, stroke, myocardial infarction, pacemaker implantation, and postoperative renal failure were not statistically significant. For outcome indicators with significant heterogeneity, subgroup analyses were performed based on PH measurement methods, diagnostic criteria, and different types of PH. The results showed that most subgroup combined results were consistent with the overall findings and that heterogeneity was significantly reduced. Conclusion PH significantly increases the 30-day all-cause mortality, 1-year all-cause mortality, and cardiovascular mortality in patients with severe AS undergoing TAVR.