GAO Ruilei 1 , WANG Dan 2 , DAI Guohua 2 , GAO Wulin 2 , GUAN Hui 2 , DONG Xueyan 3
  • 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;
DAI Guohua, Email: daigh2004@163.com
Heart failure; readmission; risk; prediction model; systematic review
10.7507/1007-4848.202409075
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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, Web of Science, CNKI, VIP, Wanfang, and CBM were searched from their inception to April 30, 2024. The prediction model risk of bias assessment tool (PROBAST) was used to assess the risk of bias and applicability of the included literature, extract relevant data, and 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, chronic renal insufficiency, left ventricular ejection fraction, New York Heart Association cardiac function classification, N-terminal pro B-type natriuretic peptide/brain natriuretic peptide, 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.

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