Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Aug;17(8):818-27.
doi: 10.1002/ejhf.287. Epub 2015 May 23.

A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE

Jesús Álvarez-García  1 , Andreu Ferrero-Gregori  2 , Teresa Puig  2 , Rafael Vázquez  3 , Juan Delgado  4 , Domingo Pascual-Figal  5 , Luis Alonso-Pulpón  6 , José R González-Juanatey  7 , Miguel Rivera  8 , Fernando Worner  9 , Alfredo Bardají  10 , Juan Cinca  1 ; investigators of the Spanish Heart Failure Network (REDINSCOR)
Collaborators, Affiliations

A simple validated method for predicting the risk of hospitalization for worsening of heart failure in ambulatory patients: the Redin-SCORE

Jesús Álvarez-García et al. Eur J Heart Fail. 2015 Aug.

Abstract

Aims: Prevention of hospital readmissions is one of the main objectives in the management of patients with heart failure (HF). Most of the models predicting readmissions are based on data extracted from hospitalized patients rather than from outpatients. Our objective was to develop a validated score predicting 1-month and 1-year risk of readmission for worsening of HF in ambulatory patients.

Methods and results: A cohort of 2507 ambulatory patients with chronic HF was prospectively followed for a median of 3.3 years. Clinical, echocardiographic, ECG, and biochemical variables were used in a competing risk regression analysis to construct a risk score for readmissions due to worsening of HF. Thereafter, the score was externally validated using a different cohort of 992 patients with chronic HF (MUSIC registry). Predictors of 1-month readmission were the presence of elevated natriuretic peptides, left ventricular (LV) HF signs, and estimated glomerular filtration rate (eGFR) <60 mL/min/m(2) . Predictors of 1-year readmission were elevated natriuretic peptides, anaemia, left atrial size >26 mm/m(2) , heart rate >70 b.p.m., LV HF signs, and eGFR <60 mL/min/m(2) . The C-statistics for the models were 0.72 and 0.66, respectively. The cumulative incidence function distinguished low-risk (<1% event rate) and high-risk groups (>5% event rate) for 1-month HF readmission. Likewise, low-risk (7.8%), intermediate-risk (15.6%) and high-risk groups (26.1%) were identified for 1-year HF readmission risk. The C-statistics remained consistent after the external validation (<5% loss of discrimination).

Conclusion: The Redin-SCORE predicts early and late readmission for worsening of HF using proven prognostic variables that are routinely collected in outpatient management of chronic HF.

Keywords: Competing risk; Death; Heart failure; Readmission; Score.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Cumulative incidence curves of hospitalizations risk for worsening of heart failure in the REDINSCOR cohort after 1‐month (upper panel) and 1‐year (lower panel) follow‐up.
Figure 2
Figure 2
Calibration plot of the 1‐month (upper panel) and 1‐year (lower panel) hospitalization Fine–Gray regression models for worsening of heart failure.
Figure 3
Figure 3
Calibration plot of the 1‐year hospitalization model for worsening of heart failure (external validation).
See this image and copyright information in PMC

Comment in

  • The Redin SCORE: useful, but not for all: reply.
    Álvarez-García J, Ferrero-Gregori A, Puig T, Cinca J; investigators of the Spanish Heart Failure Network (REDINSCOR). Álvarez-García J, et al. Eur J Heart Fail. 2016 Jan;18(1):117. doi: 10.1002/ejhf.433. Epub 2015 Oct 28. Eur J Heart Fail. 2016. PMID: 26511381 No abstract available.
  • The Redin SCORE: useful, but not for all.
    Rizzi MA, Alquezar A, Marcuello JM, Ontiveros HH. Rizzi MA, et al. Eur J Heart Fail. 2016 Jan;18(1):116. doi: 10.1002/ejhf.428. Epub 2015 Nov 25. Eur J Heart Fail. 2016. PMID: 26607047 No abstract available.

References

    1. Antoñanzas Villar F, Antón Botella F, Juárez Castelló CA, Echevarría Echarri L. [Costs of chronic heart failure in spain]. An Med Intern 1997;14:9–14. - PubMed
    1. Desai AS, Stevenson LW. Rehospitalization for heart failure: predict or prevent? Circulation 2012;126:501–506. - PubMed
    1. Bradley EH, Curry L, Horwitz LI, Sipsma H, Wang Y, Walsh MN, Goldmann D, White N, Piña IL, Krumholz HM. Hospital strategies associated with 30‐day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes 2013;6:444–450. - PMC - PubMed
    1. Ouwerkerk W, Voors AA, Zwinderman AH. Factors influencing the predictive power of models for predicting mortality and/or heart‐failure hospitalization in patients with heart failure. JACC Heart Fail 2014;2:429–436. - PubMed
    1. Rahimi K, Bennett D, Conrad N, Williams TM, Basu J, Dwight J, Woodward M, Patel A, McMurray J, MacMahon S. Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail 2014;2:440–446. - PubMed

Publication types