A critical appraisal of current models of risk stratification for percutaneous coronary interventions.

[1]  Samin K. Sharma,et al.  Anemia is an independent predictor of mortality after percutaneous coronary intervention. , 2004, Journal of the American College of Cardiology.

[2]  D. Holmes,et al.  Comparison of Mayo Clinic risk score and American College of Cardiology/American Heart Association lesion classification in the prediction of adverse cardiovascular outcome following percutaneous coronary interventions. , 2004, Journal of the American College of Cardiology.

[3]  W. O’Neill,et al.  Simplified scoring system for predicting mortality after percutaneous coronary intervention. , 2003, Journal of the American College of Cardiology.

[4]  K. Detre,et al.  Validation of Mayo Clinic risk adjustment model for in-hospital complications after percutaneous coronary interventions, using the National Heart, Lung, and Blood Institute dynamic registry. , 2003, Journal of the American College of Cardiology.

[5]  Deepak L. Bhatt,et al.  Preprocedural white blood cell count and death after percutaneous coronary intervention. , 2003, American heart journal.

[6]  L. Klein,et al.  Evaluation of the American College of Cardiology/American Heart Association and the Society for Coronary Angiography and Interventions lesion classification system in the current "stent era" of coronary interventions (from the ACC-National Cardiovascular Data Registry). , 2003, The American journal of cardiology.

[7]  S. Kelsey,et al.  Modeling and Risk Prediction in the Current Era of Interventional Cardiology: A Report From the National Heart, Lung, and Blood Institute Dynamic Registry , 2003, Circulation.

[8]  J. Golmard,et al.  Age and lack of beta-blocker therapy are associated with increased long-term mortality after primary coronary angioplasty for acute myocardial infarction. , 2003, International journal of cardiology.

[9]  S. Ellis,et al.  Emergency Coronary Artery Bypass Surgery in the Contemporary Percutaneous Coronary Intervention Era , 2002, Circulation.

[10]  M. Bell,et al.  Correlates of procedural complicationsand a simple integer risk scorefor percutaneous coronary intervention , 2002 .

[11]  C. Di Mario,et al.  Results and Long-Term Predictors of Adverse Clinical Events After Elective Percutaneous Interventions on Unprotected Left Main Coronary Artery , 2002, Circulation.

[12]  D. Thiemann.  Primary angioplasty for elderly patients with myocardial infarction: theory, practice and possibilities. , 2002, Journal of the American College of Cardiology.

[13]  Kirk N. Garratt,et al.  Incidence and Prognostic Importance of Acute Renal Failure After Percutaneous Coronary Intervention , 2002, Circulation.

[14]  M. Bell,et al.  The impact of renal insufficiency on clinical outcomes in patients undergoing percutaneous coronary interventions. , 2002, Journal of the American College of Cardiology.

[15]  William S Weintraub,et al.  Development of a risk adjustment mortality model using the American College of Cardiology-National Cardiovascular Data Registry (ACC-NCDR) experience: 1998-2000. , 2002, Journal of the American College of Cardiology.

[16]  Kirit Patel,et al.  Simple Bedside Additive Tool for Prediction of In-Hospital Mortality After Percutaneous Coronary Interventions , 2001, Circulation.

[17]  L Ohno-Machado,et al.  Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention. , 2001, The American journal of cardiology.

[18]  M. Bell,et al.  Application of the New York State PTCA mortality model in patients undergoing stent implantation. , 2000, Circulation.

[19]  M. Bell,et al.  Effect of age on the outcome of angioplasty for acute myocardial infarction among patients treated at the Mayo Clinic. , 2000, The American journal of medicine.

[20]  David P Miller,et al.  Relation between lesion characteristics and risk with percutaneous intervention in the stent and glycoprotein IIb/IIIa era: An analysis of results from 10,907 lesions and proposal for new classification scheme. , 1999, Circulation.

[21]  T. Ryan,et al.  The relationship between operator volume and outcomes after percutaneous coronary interventions in high volume hospitals in 1994-1996: the northern New England experience. Northern New England Cardiovascular Disease Study Group. , 1999, Journal of the American College of Cardiology.

[22]  P. Pasqualetti,et al.  Preprocedural serum levels of C-reactive protein predict early complications and late restenosis after coronary angioplasty. , 1999, Journal of the American College of Cardiology.

[23]  A. Kastrati,et al.  Prognostic value of the modified american college of Cardiology/American heart association stenosis morphology classification for long-term angiographic and clinical outcome after coronary stent placement. , 1999, Circulation.

[24]  T. Ryan,et al.  Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994-1996. Northern New England Cardiovascular Disease Study Group. , 1999, Journal of the American College of Cardiology.

[25]  H. White,et al.  Early revascularization in acute myocardial infarction complicated by cardiogenic shock. SHOCK Investigators. Should We Emergently Revascularize Occluded Coronaries for Cardiogenic Shock. , 1999, The New England journal of medicine.

[26]  H. Schaff,et al.  Increased incidence of periprocedural complications among patients with peripheral vascular disease undergoing myocardial revascularization in the bypass angioplasty revascularization investigation. , 1999, Circulation.

[27]  L. Klein,et al.  Value of the American College of Cardiology/American Heart Association stenosis morphology classification for coronary interventions in the late 1990s. , 1998, The American journal of cardiology.

[28]  E. Hannan,et al.  Identification of variables needed to risk adjust outcomes of coronary interventions: evidence-based guidelines for efficient data collection. , 1998, Journal of the American College of Cardiology.

[29]  S. Ellis,et al.  Relation of operator volume and experience to procedural outcome of percutaneous coronary revascularization at hospitals with high interventional volumes. , 1997, Circulation.

[30]  E L Hannan,et al.  Coronary angioplasty volume-outcome relationships for hospitals and cardiologists. , 1997, JAMA.

[31]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[32]  E J Topol,et al.  Coronary morphologic and clinical determinants of procedural outcome with angioplasty for multivessel coronary disease. Implications for patient selection. Multivessel Angioplasty Prognosis Study Group. , 1990, Circulation.

[33]  T. Ryan.  Guidelines for percutaneous transluminal coronary angioplasty. A report of the American College of Cardiology/American Heart Association Task Force on Assessment of Diagnostic and Therapeutic Cardiovascular Procedures (Subcommittee on Percutaneous Transluminal Coronary Angioplasty). , 1988, Journal of the American College of Cardiology.

[34]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[35]  W. Siegenthaler,et al.  Nonoperative dilatation of coronary-artery stenosis: percutaneous transluminal coronary angioplasty. , 1979, The New England journal of medicine.

[36]  R. R. Hocking.  The analysis and selection of variables in linear regression , 1976 .

[37]  Strother H. Walker,et al.  Estimation of the probability of an event as a function of several independent variables. , 1967, Biometrika.

[38]  M Schumacher,et al.  A bootstrap resampling procedure for model building: application to the Cox regression model. , 1992, Statistics in medicine.

[39]  D. Hosmer,et al.  Goodness of fit tests for the multiple logistic regression model , 1980 .