Pre-rehabilitation is an emerging preoperative management strategy designed to mitigate surgical stress responses and expedite postoperative recovery through optimized interventions, which typically includes exercise training, nutritional support, and psychological counseling. For patients undergoing transcatheter aortic valve replacement (TAVR), the implementation of pre-rehabilitation measures is particularly crucial. This article reviews the necessity and principal components of pre-rehabilitation in TAVR patients, and offers suggestions including constructing the best pre-rehabilitation intervention program for TAVR, enhancing patient compliance and engagement in the recovery process, and paying attention to the management of frailty for TAVR patients. The aim is to provide a reference for healthcare professionals seeking to further refine the pre-rehabilitation management model for TAVR patients.
The implantation of left ventricular assist device (LVAD) has significantly improved the quality of life for patients with end-stage heart failure. However, it is assiosciated with the risk of complications, with unplanned readmissions gaining increasing attention. This article reviews the overview, influencing factors, predictive models, and intervention measures for unplanned readmissions among LVAD implantation patients. The aim is to provide scientific guidance for clinical practice, assisting healthcare professionals in accurately assessing patient conditions and formulating appropriate care plans.
ObjectiveTo systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. MethodsA computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. ResultsA total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.67-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. ConclusionAt present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.