Review Article

Readmission Risk Prediction Model for Patients with Chronic Heart Failure: A Systematic Review and Meta-Analysis


Background: The present systematic review and meta-analysis aimed to systematically evaluate a risk prediction model for the readmission of patients with CHF.

Methods: The search was carried out in databases including PubMed, Embase, EBSCO, Web of Science, Cochrane Library and also domestic databases including Chinese Biomedical Literature Database, Chinese Academic Journal Full Text Database, Wanfang Database, and Vipu Chinese Journal Service Platform. All the original studies published by July 2021. Two researchers identified previous studies involving readmission risk prediction models that met our selection criteria. The quality of the included studies was evaluated based on the CHARMS checklist, and the prediction models were systematically evaluated.

Results: Of the overall 4787 studies retrieved, nine studies—two prospective, seven retrospective—met our selection criteria. The area under the receiver operating characteristic curve exceeded 0.63 (0.63-0.80) for all the studies. The most common predictors in the model were B-type natriuretic peptide (BNP) or N-terminal pro-brain BNP (Odds Ratio 4.35; 95% confidence interval (CI) 2.53–7.49; P<0.001), renal insufficiency (Odds Ratio 1.60; 95%CI 1.24–2.08; P<0.001), comorbidities, and a history of hospitalization.

Conclusion: The use of non-parametric statistical methods and assessment of large samples of electronic data improve the predictive abilities of the risk assessment models. It is necessary to calibrate and verify such models and promote the combined use of parametric and non-parametric methods to establish precise predictive models for clinical use.

1. Adams KF, Jr., Fonarow GC, Emerman CL, et al (2005). Characteristics and outcomes of patients hospitalized for heart failure in the United States: rationale, design, and preliminary observations from the first 100,000 cases in the Acute Decompensated Heart Failure National Registry (ADHERE). Am Heart J, 149(2):209-16.
2. Liu X LP, Guo Lh (2017). Research status of influencing factors and intervention measures for readmission of patients with heart failure. Chinese Nursing Management. Chinese Nursing Management, 17(6): 859-863.
3. Writing Group M, Mozaffarian D, Benjamin EJ, et al (2016). Heart Disease and Stroke Statistics-2016 Update: A Report from the American Heart Association. Circulation, 133(4):e38-360.
4. Harkness K, Spaling MA, Currie K, et al (2015). A systematic review of patient heart failure self-care strategies. J Cardiovasc Nurs, 30(2):121-35.
5. Lu MX, Zhang YY, Jiang JF, et al (2016). Weight Management Belief is the Leading Influential Factor of Weight Monitoring Compliance in Congestive Heart Failure Patients. Acta Cardiol Sin, 32(6):708-15.
6. Moons KGM, Kengne AP, Grobbee DE, et al (2012). Risk prediction models: II. External validation, model updating, and impact assessment. Heart, 98(9):691-8.
7. Mizukawa M, Moriyama M, Yamamoto H, et al (2019). Nurse-Led Collaborative Management Using Telemonitoring Improves Quality of Life and Prevention of Rehospitalization in Patients with Heart Failure A Pilot Study. Int Heart J, 60(6):1293-302.
8. Cox ZL, Lai P, Lewis CM, et al (2018). Customizing national models for a medical center's population to rapidly identify patients at high risk of 30-day all-cause hospital readmission following a heart failure hospitalization. Heart Lung, 47(4):290-6.
9. Leong KTG, Wong LY, Aung KCY, et al (2017). Risk Stratification Model for 30-Day Heart Failure Readmission in a Multiethnic South East Asian Community. Am J Cardiol, 119(9):1428-32.
10. Mahajan SM, Ghani R(2019). Using Ensemble Machine Learning Methods for Predicting Risk of Readmission for Heart Failure. Stud Health Technol Inform, 264:243-7.
11. Tan BY, Gu JY, Wei HY, et al (2019). Electronic medical record-based model to predict the risk of 90-day readmission for patients with heart failure. BMC Med Inform Decis Mak, 19(1):193.
12. Saito M, Negishi K, Marwick TH(2016). Meta-Analysis of Risks for Short-Term Readmission in Patients With Heart Failure. Am J Cardiol, 117(4):626-32.
13. Betihavas V, Davidson PM, Newton PJ, et al (2012). What are the factors in risk prediction models for rehospitalisation for adults with chronic heart failure? Aust Crit Care, 25(1):31-40.
14. Ouwerkerk W, Voors AA, Zwinderman AH (2014). Factors Influencing the Predictive Power of Models for Predicting Mortality and/or Heart Failure Hospitalization in Patients With Heart Failure. JACC Heart Fail, 2(5):429-36.
15. Benjamin EJ, Blaha MJ, Chiuve SE, et al (2017). Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation, 135(10):e146-e603.
16. Moons KG, de Groot JA, Bouwmeester W, et al (2014). Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 11(10):e1001744.
17. Alvarez-Garcia J, Ferrero-Gregori A, Puig T, et al (2015). A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE. Eur J Heart Fail, 17(8):818-27.
18. Amarasingham R, Moore BJ, Tabak YP, et al (2010). An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data. Med Care, 48(11):981-8.
19. Betihavas V, Frost SA, Newton PJ, et al (2015). An Absolute Risk Prediction Model to Determine Unplanned Cardiovascular Readmissions for Adults with Chronic Heart Failure. Heart Lung Circ, 24(11):1068-73.
20. Hummel SL, Ghalib HH, Ratz D, et al (2013). Risk stratification for death and all-cause hospitalization in heart failure clinic outpatients. Am Heart J, 166(5): 895-903.e1.
21. Shameer K, Johnson KW, Yahi A, et al (2017). Predictive Modeling of Hospital Readmission Rates Using Electronic Medical Record-Wide Machine Learning: A Case-Study Using Mount Sinai Heart Failure Cohort. Pac Symp Biocomput, 22: 276-87.
22. Li Yl ZB, Liu Hm.(2014). Meta-analysis of the effect of the duration of low molecular weight heparin subcutaneous injection on adverse reactions. Chinese Journal of Practical Nursing, 30(18): 59-62.
23. Mortazavi BJ, Downing NS, Bucholz EM, et al (2016). Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circ Cardiovasc Qual Outcomes, 9(6):629-640.
24. Tsutamoto T, Wada A, Maeda K, et al (1997). Attenuation of compensation of endogenous cardiac natriuretic peptide system in chronic heart failure - Prognostic role of plasma brain natriuretic peptide concentration in patients with chronic symptomatic left ventricular dysfunction. Circulation, 96(2):509-16.
25. McCallum W, Tighiouart H, Kiernan MS, et al (2020). Relation of Kidney Function Decline and NT-proBNP With Risk of Mortality and Readmission in Acute Decompensated Heart Failure. Am J Med, 133(1): 115-122.e2.
26. Sudharshan S, Novak E, Hock K, et al (2017). Use of Biomarkers to Predict Readmission for Congestive Heart Failure. Am J Cardiol, 119(3):445-51.
27. Tedeschi A, Agostoni P, Pezzuto B, et al (2020). Role of comorbidities in heart failure prognosis Part 2: Chronic kidney disease, elevated serum uric acid. Eur J Prev Cardiol, 27(2_suppl):35-45.
28. Tang WHW, Kitai T(2016). Intrarenal Venous Flow A Window Into the Congestive Kidney Failure Phenotype of Heart Failure? JACC Heart Fail, 4(8):683-6.
29. Ruiz-Laiglesia FJ, Sanchez-Marteles M, Perez-Calvo JI, et al (2014). Comorbidity in heart failure. Results of the Spanish RICA Registry. QJM, 107(12):989-94.
30. Thomas MC(2018). Perspective Review: Type 2 Diabetes and Readmission for Heart Failure. Clin Med Insights Cardiol, 12:1179546818779588.
31. Krumholz HM, Chen YT, Wang Y, et al (2000). Predictors of readmission among elderly survivors of admission with heart failure. Am Heart J, 139(1 Pt 1):72-7.
32. Heart Failure Group of Chinese Society of Cardiology of Chinese Medical Association; Chinese Heart Failure Association of Chinese Medical Doctor Association; Editorial Board of Chinese Journal of Cardiology (2018). [Chinese guidelines for the diagnosis and treatment of heart failure 2018]. Zhonghua Xin Xue Guan Bing Za Zhi, 46(10):760-789.
33. Expert Committee of Rational Drug Use of National Health and Family Planning Commission CPA (2019). Guidelines for rational drug use in heart failure (2nd edition).
34. Zhao X WW, Zhao Xj (2020). Summary of the best evidence for the management of patients with chronic heart failure. Chinese Journal of Nursing, 2020, 55(3): 456-461.
35. Ruppar TM, Cooper PS, Mehr DR, et al (2016). Medication Adherence Interventions Improve Heart Failure Mortality and Readmission Rates: Systematic Review and Meta-Analysis of Controlled Trials. J Am Heart Assoc, 5(6):e002606.
36. Calvillo-King L, Arnold D, Eubank KJ, et al (2013). Impact of Social Factors on Risk of Readmission or Mortality in Pneumonia and Heart Failure: Systematic Review. J Gen Intern Med, 28(2):269-82.
37. Lim NK, Lee SE, Lee HY, et al (2019). Risk prediction for 30-day heart failure-specific readmission or death after discharge: Data from the Korean Acute Heart Failure (KorAHF) registry. J Cardiol, 73(2):108-13.
38. Rice H, Say R, Betihavas V (2018). The effect of nurse-led education on hospitalisation, readmission, quality of life and cost in adults with heart failure. A systematic review. Patient Educ Couns, 101(3):363-74.
IssueVol 51 No 7 (2022) QRcode
SectionReview Article(s)
Chronic heart failure Readmission Prediction model Systematic review

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How to Cite
Liu J, Liu P, Lei M-R, Zhang H-W, You A-L, Luan X-R. Readmission Risk Prediction Model for Patients with Chronic Heart Failure: A Systematic Review and Meta-Analysis. Iran J Public Health. 2022;51(7):1481-1493.