Literature DB >> 24464911

Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51,043 patients from the Swedish heart failure registry.

Ulrik Sartipy1, Ulf Dahlström, Magnus Edner, Lars H Lund.   

Abstract

AIMS: The aim of this study was to evaluate the performance of a recently developed risk score for mortality in heart failure by external validation in a national heart failure registry. METHODS AND
RESULTS: From 13 routinely available patient characteristics, the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) constructed a risk score for prediction of mortality in heart failure. We included 51 043 patients from the national Swedish Heart Failure Registry and calculated the MAGGIC risk score for each patient. The outcome measure was 3-year mortality. The predicted probability of death obtained from the calculated risk score was compared with the observed 3-year mortality, and model discrimination and calibration were assessed by formal tests and graphical means. The overall 3-year mortality in the study population was 39.4% and the MAGGIC project heart failure risk score predicted mortality was 36.4% (observed to expected ratio: 1.08). Discrimination was excellent overall (C index = 0.741). The difference between the model-predicted and the observed 3-year mortality in the six risk groups varied between 5% and -12%. Calibration plots demonstrated slight overprediction for the lowest risk patients, and underprediction in high risk patients.
CONCLUSION: The MAGGIC project heart failure risk score demonstrated an excellent ability to categorize patients in separate risk strata. Although the predicted 3-year mortality risk was higher in low risk groups and lower in high risk groups compared with the observed 3-year mortality in the Swedish Heart Failure Registry, the MAGGIC project heart failure risk score performed well in a large nationwide contemporary external validation cohort.
© 2013 The Authors. European Journal of Heart Failure © 2013 European Society of Cardiology.

Entities:  

Keywords:  External validation; Heart failure; Prognostic risk models; Survival

Mesh:

Year:  2013        PMID: 24464911     DOI: 10.1111/ejhf.32

Source DB:  PubMed          Journal:  Eur J Heart Fail        ISSN: 1388-9842            Impact factor:   15.534


  36 in total

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10.  Machine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention.

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