Re-admission risk prediction models for patients with heart failure after discharge: A systematic review
- VernacularTitle:心力衰竭患者出院后再入院风险预测模型的系统评价
- Author:
Ruilei GAO
1
;
Dan WANG
2
;
Guohua DAI
2
;
Wulin GAO
2
;
Hui GUAN
2
;
Xueyan DONG
3
Author Information
1. The First Clinical Medical College of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China
2. Department of Geriatric Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China
3. Department of Hematology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China
- Publication Type:Journal Article
- Keywords:
Heart failure;
readmission;
risk;
prediction model;
systematic review
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(05):677-684
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically evaluate the predictive models for re-admission in patients with heart failure (HF) in China. Methods Studies related to the risk prediction model for HF patient re-admission published in The Cochrane Library, PubMed, EMbase, CNKI, and other databases were searched from their inception to April 30, 2024. The prediction model risk of bias assessment tool was used to assess the risk of bias and applicability of the included literature, relevant data were extracted to evaluate the model quality. Results Nineteen studies were included, involving a total of 38 predictive models for HF patient re-admission. Comorbidities such as diabetes, N-terminal pro B-type natriuretic peptide/brain natriuretic peptide, chronic renal insufficiency, left ventricular ejection fraction, New York Heart Association cardiac function classification, and medication adherence were identified as primary predictors. The area under the receiver operating characteristic curve ranged from 0.547 to 0.962. Thirteen studies conducted internal validation, one study conducted external validation, and five studies performed both internal and external validation. Seventeen studies evaluated model calibration, while five studies assessed clinical feasibility. The presentation of the models was primarily in the form of nomograms. All studies had a high overall risk of bias. Conclusion Most predictive models for HF patient re-admission in China demonstrate good discrimination and calibration. However, the overall research quality is suboptimal. There is a need to externally validate and calibrate existing models and develop more stable and clinically applicable predictive models to assess the risk of HF patient re-admission and identify relevant patients for early intervention.
