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Role of biological and non biological factors in congestive heart failure mortality: PREDICE-SCORE: A clinical prediction rule
open access
Abstract
Background: Congestive heart failure (HF) is a chronic, frequent and disabling condition but with a modifiable course and a large potential for improving. The aim of this project was to develop a clinical prediction model of biological and non biological factors in patients with first diagnosis of HF that facilitates the risk-stratification and decision-making process at the point of care.
Methods and Results: Historical cohort analysis of 600 patients attended at three tertiary hospitals and diagnosed of a first episode of HF according Framingham criteria. There were followed 1 year. We analyzed sociodemographic, clinical and laboratory data with potential prognostic value. The modelling process concluded into a logistic regression multivariable analysis and a predictive rule: PREDICE SCORE. Age, dependency for daily basic activities, creatinine clearance, sodium levels at admission and systolic dysfunction diagnosis (HF with left ventricular ejection fraction < 40%) were the selected variables. The model showed a c-statistic of 0.763. PREDICE Score, has range of 22 points to stratifications of 1-year mortality.
Conclusions: The follow-up of 600 patients hospitalized by a first episode of congestive HF, allowed us to obtain a predictive 1 year mortality model from the combination of demographic data, routine biochemistry and easy handling social and functional variables at the point of care. The variables included were non-invasive, undemanding to collect, and widely available. It allows for risk stratification and therapeutical targeting and may help in the clinical decisions process in a sustainable way.
Abstract
Background: Congestive heart failure (HF) is a chronic, frequent and disabling condition but with a modifiable course and a large potential for improving. The aim of this project was to develop a clinical prediction model of biological and non biological factors in patients with first diagnosis of HF that facilitates the risk-stratification and decision-making process at the point of care.
Methods and Results: Historical cohort analysis of 600 patients attended at three tertiary hospitals and diagnosed of a first episode of HF according Framingham criteria. There were followed 1 year. We analyzed sociodemographic, clinical and laboratory data with potential prognostic value. The modelling process concluded into a logistic regression multivariable analysis and a predictive rule: PREDICE SCORE. Age, dependency for daily basic activities, creatinine clearance, sodium levels at admission and systolic dysfunction diagnosis (HF with left ventricular ejection fraction < 40%) were the selected variables. The model showed a c-statistic of 0.763. PREDICE Score, has range of 22 points to stratifications of 1-year mortality.
Conclusions: The follow-up of 600 patients hospitalized by a first episode of congestive HF, allowed us to obtain a predictive 1 year mortality model from the combination of demographic data, routine biochemistry and easy handling social and functional variables at the point of care. The variables included were non-invasive, undemanding to collect, and widely available. It allows for risk stratification and therapeutical targeting and may help in the clinical decisions process in a sustainable way.
Keywords
heart failure; systolic dysfunction; prognosis; predictive rules
About this articleTitle
Role of biological and non biological factors in congestive heart failure mortality: PREDICE-SCORE: A clinical prediction rule
Journal
Issue
Pages
578-585
Published online
2012-12-06
Page views
1304
Article views/downloads
2374
DOI
10.5603/CJ.2012.0108
Bibliographic record
Cardiol J 2012;19(6):578-585.
Keywords
heart failure
systolic dysfunction
prognosis
predictive rules
Authors
Agustín Gómez de la Cámara
Juan Manuel GuerraVales
Purificación Magán Tapia
Eva Andrés Esteban
Silvia Vázquez Fernández del Pozo
Enrique Calderón Sandubete
Francisco J. Medrano Ortega
Asunción Navarro Puerto
Ignacio Marín-León; on the behalf of PR Group
