Two Survival Tree Models for Myocardial Infarction Patients

In the search of a better prognostic survival model for post-acute myocardial infarction patients, the scientists at the Technical University of Munich's "Klinikum rechts der Isar" and the German Heart Center in Munich have developed some new parameters using 24-hour ECG (Schmidt et al 1999). A series of investigations were done using these parameters on different data sets and the Cox-PH model (Schmidt et al 1999, Ulm et al 2000). This paper is a response to the discussion paper by Ulm et al (2000), which suggests a Cox model for calculating the risk stratification of the MPIP data set patients including the predictors ejection fraction and heart rate turbulence. The current paper suggests the use of the classification and regression trees technique for survival data in order to deduct a survival stratification model for the NIRVPIP data set. Two models are compared: one contains the variables suggested by Ulm et al (2000) the other model has two additional variables, namely presence of couplets and number of extra systolic beats in the longest salvo of the patient's 24-hour ECG. The second model is shown to be an improvement on the first one

A Statistical Model for Risk Stratification on the Basis of Left Ventricular Ejection Fraction and Heart-Rate Turbulence

The MPIP data set was used to obtain a model for mortality risk stratification of acute myocardial infarction patients. The predictors heart rate turbulence (HRT) and left-ventricular ejection fraction (LVEF) were employed. HRT was a categorical variable of three levels; LVEF was continuous and its influence on the relative risk was explained by the natural logarithm function (found using fractional polynomials). Cox - PH model with HRT and lnLVEF was constructed and used for risk stratification. The model can be used to divide the patients into two or more groups according to mortality risk. It also describes the relationship between risk and predictors by a (continuous) function, which allows the calculation of individual mortality risk