ObjectiveTo systematically evaluate the risk prediction model of anastomotic fistula after radical resection of esophageal cancer, and to provide objective basis for selecting a suitable model. MethodsA comprehensive search was conducted on Chinese and English databases including CNKI, Wanfang, VIP, CBM, PubMed, EMbase, Web of Science, The Cochrane Library for relevant studies on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer from inception to April 30, 2023. Two researchers independently screened literatures and extracted data information. PROBAST tool was used to assess the risk of bias and applicability of included literatures. Meta-analysis was performed on the predictive value of common predictors in the model with RevMan5.3 software. ResultsA total of 18 studies were included, including 11 Chinese literatures and 7 English literatures. The area under the curve (AUC) of the prediction models ranged from 0.68 to 0.954, and the AUC of 10 models was >0.8, indicating that the prediction performance was good, but the risk of bias in the included studies was high, mainly in the field of research design and data analysis. ConclusionThe study on the risk prediction model of anastomotic fistula after radical resection of esophageal cancer is still in the development stage. Future studies can refer to the common predictors summarized by this study, and select appropriate methods to develop and verify the anastomotic fistula prediction model in combination with clinical practice, so as to provide targeted preventive measures for patients with high-risk anastomotic fistula as soon as possible.
ObjectiveTo systematically review the risk prediction models for readmission within 30 days after discharge in patients with chronic obstructive pulmonary disease (COPD), and provide a reference for clinical selection of risk assessment tools. MethodsDatabases including CNKI, Wanfang Data, VIP, CBM, PubMed, Embase, Web of Science, and Cochrane Library were searched for literature on this topic. The search time was from the inception of the database to April 25, 2023. Literature screening and data extraction were performed by two researchers independently. The risk of bias and applicability of the included literature were evaluated using the risk of bias assessment tool for predictive model studies. ResultsA total of 8 studies were included, including 14 risk prediction models for 30-day readmission of COPD patients after discharge. The total sample size was 125~8 263, the number of outcome events was 24~741, and the area under the receiver operating characteristic curve was 0.58~0.918. The top five most common predictors included in the model were smoking, comorbidities, age, education level, and home oxygen therapy. Although five studies had good applicability, all eight studies had a certain risk of bias. This is mainly due to the small sample size of the model, lack of reporting of blinding, lack of external validation, and inappropriate handling of missing data. ConclusionThe overall prediction performance of the risk prediction model for 30-day readmission of patients with COPD after discharge is good, but the overall research quality is low. In the future, the model should be continuously improved to provide a scientific assessment tool for the early clinical identification of patients with COPD at high risk of readmission within 30 days after discharge.