Abstract
BackgroundSeveral models have been designed to predict survival of patients with heart failure. These, while available and widely used for both stratifying and deciding upon different treatment options on the individual level, have several limitations. Specifically, some clinical variables that may influence prognosis may have an influence that change over time. Statistical models that include such characteristic may help in evaluating prognosis. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting.MethodologySurvival data from an inception cohort of five hundred patients diagnosed with heart failure functional class III and IV between 2002 and 2004 and followed-up to 2006 were analyzed by using the proportional hazards Cox model and variations of the Cox’s model and also of the Aalen’s additive model.Principal FindingsOne-hundred and eighty eight (188) patients died during follow-up. For patients under study, age, serum sodium, hemoglobin, serum creatinine, and left ventricular ejection fraction were significantly associated with mortality. Evidence of time-varying effect was suggested for the last three. Both high hemoglobin and high LV ejection fraction were associated with a reduced risk of dying with a stronger initial effect. High creatinine, associated with an increased risk of dying, also presented an initial stronger effect. The impact of age and sodium were constant over time.ConclusionsThe current study points to the importance of evaluating covariates with time-varying effects in heart failure models. The analysis performed suggests that variations of Cox and Aalen models constitute a valuable tool for identifying these variables. The implementation of covariates with time-varying effects into heart failure prognostication models may reduce bias and increase the specificity of such models.
Highlights
Patients with heart failure usually experience a progressive clinical deterioration over time
Decline trends in HF hospitalization rates have been shown in Europe [2] and in the USA [3], current advances in the treatment of both myocardial infarction and heart failure itself bring the forecast of even higher heart failure numbers
Patients were included as part of a secondary-cohort of HF individuals attended at a cardiology tertiary care center in Sao Paulo, Brazil (Heart Institute of the Sao Paulo University Medical School)
Summary
Patients with heart failure usually experience a progressive clinical deterioration over time. Factors that influence the unfavorable outcome are less predictable over time as they may be dependent on several, and distinct, factors such as pump failure, autonomic nervous system influence, cardiac arrhythmias, metabolic derangements (such as renal failure, hyperkalemia, hypokalemia), and complications that many times may be subclinical or undiagnosed, such as pulmonary embolism. This myriad of potential complications that may ensue in spite of current therapy are less predictable over time. The aim of the present study was to analyze and quantify the impact of modeling heart failure survival allowing for covariates with time-varying effects known to be independent predictors of overall mortality in this clinical setting
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
