Prognostic prediction model for Chinese patients with chronic heart failure: A systematic review
- VernacularTitle:中国慢性心力衰竭患者预后预测模型的系统评价
- Author:
Yingying JIA
1
;
Huanting HU
2
;
Jingni HU
2
;
Min YOU
3
;
Tianman YUAN
2
;
Jianping SONG
4
Author Information
1. 1. Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310000, P. R. China 2. Department of Nursing, Zhejiang University School of Medicine, Hangzhou, 310000, P. R. China
2. School of Nursing, Zhejiang Chinese Medical University, Hangzhou, 310053, P. R. China
3. School of Nursing, Huzhou University, Huzhou, 313000, Zhejiang, P. R. China
4. Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310000, P. R. China
- Publication Type:Journal Article
- Keywords:
Chronic heart failure;
prognosis;
prediction model;
systematic review
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2024;31(11):1645-1654
- CountryChina
- Language:Chinese
-
Abstract:
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.
