Logit and Fuzzy Models in Data Analysis: Estimation of Risk in Cardiac Patients



Year of publication 2010
Type Article in Periodical
Magazine / Source Physiological Research
MU Faculty or unit

Faculty of Medicine

Web http://www.biomed.cas.cz/physiolres/pdf/59%20Suppl%201/59_S89.pdf
Field Cardiovascular diseases incl. cardiosurgery
Keywords Risk prediction; Myocardial infarction; Implantable cardioverter-defibrillator; Fuzzy logic; Area under receiver operating characteristic; Logistic regression
Description The aim of this study was a comparison of risk stratification for death in patients after myocardial infarction (MI) and of risk stratification for malignant arrhythmias in patients with implantable cardioverter-defibrillator (ICD). The individual risk factors and more complex approaches were used, which take into account that a borderline between a risky and non-risky value of each predictor is not clear-cut (fuzzification of a critical value) and that individual risk factors have different weight (area under receiver operating curve - AUC or Sommers' D - Dxy). The risk factors were baroreflex sensitivity, ejection fraction and the number of ventricular premature complexes/hour on Holter monitoring. Those factors were evaluated separately and they were involved into logit model and fuzzy models (Fuzzy, Fuzzy-AUC, and Fuzzy-Dxy). Two groups of patients were examined: a) 308 patients 7-21 days after MI (23 patients died within period of 24 month); b) 53 patients with left ventricular dysfunction examined before implantation of ICD (7 patients with malignant arrhythmia and electric discharge within 11 month after implantation). Our results obtained in MI patients demonstrated that the application of logit and fuzzy models was superior over the risk stratification based on algorithm where the decision making is dependent on one parameter. In patients with implanted defibrillator only logit method yielded statistically significant result, but its reliability was doubtful because all other tests were statistically insignificant. We recommend evaluating the data not only by tests based on logit model but also by tests based on fuzzy models.
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