title: Major complications after angioplasty in patients with chronic renal failure: a comparison of predictive models.
abstract: Novel modeling approaches were investigated to predict major complications in patients with chronic renal failure (CRF) or end-stage renal disease (ESRD) undergoing percutaneous transluminal coronary angioplasty (PTCA). The following hypotheses were explored: (1) Pre-angioplasty patient risk factors, demographic characteristics and procedural information may be used to predict major complications after PTCA; and (2) Rough sets and artificial neural nets (ANN) may be used to build models that are better than standard logistic regression models. Several variables were found to be predictive of major complications for patients with CRF or ESRD undergoing PTCA. The presence of shock at presentation portends poor outcome but congestive heart failure and prior history of myocardial infarction increases the risk tenfold and 25-fold, respectively. The discriminatory ability of the ANN model was better than both Rough Sets and Logistic Regression for the test set.
authors.affiliation: Decision Systems Group and Division of Health Sciences and Technology, Brigham and Women's Hospital, Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA.
authors.forename: R C
authors.initials: RC
authors.lastname: Lacson
authors.forename: L
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D000704:Analysis of Variance; D015906:Angioplasty, Balloon, Coronary / Q000009:adverse effects*; D003327:Coronary Disease / Q000145:classification / Q000150:complications / Q000628:therapy*; D006333:Heart Failure / Q000150:complications; D006801:Humans; D007676:Kidney Failure, Chronic / Q000150:complications*; D016015:Logistic Models; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000150:complications; D016571:Neural Networks, Computer; D011379:Prognosis; D012307:Risk Factors