title: Role of biological and non biological factors in congestive heart failure mortality: PREDICE-SCORE: a clinical prediction rule.
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.
  
  
  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.
authors.affiliation: Research Institute, Clinical Research Unit Hospital 12 de Octobre, Madrid, Spain. acamara@h12o.es
authors.forename: Agustín Gómez
authors.initials: AG
authors.lastname: de la Cámara
authors.forename: Juan Manuel
authors.initials: JM
authors.lastname: Guerravales
authors.forename: Purificación Magán
authors.initials: PM
authors.lastname: Tapia
authors.forename: Eva Andrés
authors.initials: EA
authors.lastname: Esteban
authors.forename: Silvia Vázquez Fernández
authors.initials: SV
authors.lastname: del Pozo
authors.forename: Enrique Calderón
authors.initials: EC
authors.lastname: Sandubete
authors.forename: Francisco J Medrano
authors.initials: FJ
authors.lastname: Ortega
authors.forename: Asunción Navarro
authors.initials: AN
authors.lastname: Puerto
authors.forename: Ignacio
authors.initials: I
authors.lastname: Marín-León
mesh_terms: D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D015331:Cohort Studies; D003404:Creatinine / Q000097:blood; D005260:Female; D005500:Follow-Up Studies; D006333:Heart Failure / Q000097:blood / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology*; D006785:Hospitals, University; D006801:Humans; D008019:Life Style; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012964:Sodium / Q000097:blood; D013030:Spain / Q000453:epidemiology; D015996:Survival Rate; D013599:Systole; D018487:Ventricular Dysfunction, Left / Q000097:blood / Q000175:diagnosis / Q000401:mortality* / Q000503:physiopathology*
chemical_list: D015415:Biomarkers; D012964:Sodium; D003404:Creatinine