Objective To systematically evaluate the prognostic prediction model for chronic heart failure patients in China, and provide reference for the construction, application, and promotion of related prognostic prediction models. Methods A comprehensive search was conducted on the studies related to prognostic prediction model for Chinese patients with chronic heart failure published in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, VIP, Wanfang, and the China Biological Medicine databases from inception to March 31, 2023. Two researchers strictly followed the inclusion and exclusion criteria to independently screen literature and extract data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models. Results A total of 25 studies were enrolled, including 123 prognostic prediction models for chronic heart failure patients. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.690 to 0.959. Twenty-two studies mostly used random splitting and Bootstrap for internal model validation, with an AUC range of 0.620-0.932. Seven studies conducted external validation of the model, with an AUC range of 0.720-0.874. The overall bias risk of all models was high, and the overall applicability was low. The main predictive factors included in the models were the N-terminal pro-brain natriuretic peptide, age, left ventricular ejection fraction, New York Heart Association heart function grading, and body mass index. Conclusion The quality of modeling methodology for predicting the prognosis of chronic heart failure patients in China is poor, and the predictive performance of different models varies greatly. For developed models, external validation and clinical application research should be vigorously carried out. For model development research, it is necessary to comprehensively consider various predictive factors related to disease prognosis before modeling. During modeling, large sample and prospective studies should be conducted strictly in accordance with the PROBAST standard, and the research results should be comprehensively reported using multivariate prediction model reporting guidelines to develop high-quality predictive models with strong scalability.
ObjectiveTo systematically evaluate the methodological quality and predictive performance of acute kidney injury (AKI) prediction models following coronary artery bypass grafting (CABG), aiming to identify reliable tools for clinical practice and provide evidence-based guidance for developing higher-quality models in future. MethodsA systematic literature search was conducted across CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, EMbase, and Cochrane Library databases from inception to October 2025. Two independent reviewers screened studies, extracted data, and performed prediction model risk of bias assessment. Qualitative synthesis was followed by meta-analysis using STATA 15.0 software. ResultsA total of 21 studies involving 55 prediction models were included. The majority of the studies demonstrated good applicability, but exhibited high overall risk of bias. The models showed favorable discriminative ability, with areas under the receiver operating characteristic curves ranging from 0.707 to 0.958 in training cohorts, and a pooled area under the curve of 0.79 [95%CI (0.76, 0.82)]. The area under the receiver operating characteristic curve in the validation set ranged from 0.55 to 0.90, with a pooled area under the curve of 0.80 [95%CI (0.78, 0.81)]. Most models were presented as Nomograms. Common predictors included age, serum creatinine, estimated glomerular filtration rate, hemoglobin, uric acid, cardiopulmonary bypass, and intra-aortic balloon pump. ConclusionCurrent prediction models demonstrate satisfactory discrimination performance but are limited by single-center development, insufficient external validation, and methodological biases. Future multicenter prospective studies should optimize variable processing and model validation strategies to enhance clinical applicability and generalizability of predictive tools.