Validating the Clinical Outcomes Assessment Program risk model for percutaneous coronary intervention

American Heart Journal

Volumn 151, Issue 6, 2006, Pages 1276-1280

  Wu, YingXing ^a   Jin, Ruyun ^a   Grunkemeier, Gary L ^a  

^a EARLE A CHILES RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; CALIBRATION; CARDIAC PATIENT; CONFIDENCE INTERVAL; DATA ANALYSIS; FEMALE; HEALTH PROGRAM; HUMAN; MAJOR CLINICAL STUDY; MALE; METHODOLOGY; MORTALITY; OUTCOME ASSESSMENT; PERCUTANEOUS CORONARY INTERVENTION; PRIORITY JOURNAL; RISK ASSESSMENT; VALIDATION PROCESS;

ADULT; AGED; AGED, 80 AND OVER; ANGIOPLASTY, TRANSLUMINAL, PERCUTANEOUS CORONARY; FEMALE; HOSPITAL MORTALITY; HUMANS; MALE; MIDDLE AGED; MODELS, STATISTICAL; RISK ASSESSMENT;

EID: 33744967466     PISSN: 00028703     EISSN: 10976744     Source Type: Journal    
DOI: 10.1016/j.ahj.2005.07.009     Document Type: Article

References (20)

1. Kizer J.R., Berlin J.A., Laskey W.K., et al. Limitations of current risk-adjustment models in the era of coronary stenting. Am Heart J 145 (2003) 683-692
2. Ellis S.G., Weintraub W., Holmes D., et al. Relation of operator volume and experience to procedural outcome of percutaneous coronary revascularization at hospitals with high interventional volumes. Circulation 95 (1997) 2479-2484
3. O'Connor G.T., Malenka D.J., Quinton H., et al. Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994-1996. Northern New England Cardiovascular Disease Study Group. J Am Coll Cardiol 34 (1999) 681-691
4. Hannan E.L., Racz M., Ryan T.J., et al. Coronary angioplasty volume-outcome relationships for hospitals and cardiologists. JAMA 277 (1997) 892-898
5. Maynard C., Goss J.R., Malenka D.J., et al. Adjusting for patient differences in predicting hospital mortality for percutaneous coronary interventions in the Clinical Outcomes Assessment Program. Am Heart J 145 (2003) 658-664
6. Grunkemeier G.L., and Jin R. Receiver operating characteristic curve analysis of clinical risk models. Ann Thorac Surg 72 (2001) 323-326
7. Hosmer D.W., and Lemeshow S. A goodness-of-fit test for the multiple logistic regression model. Commun Stat A10 (1980) 1043-1069
8. Le Cessie S., and van Houwelingen H.C. A goodness-of-fit test for binary data based on smoothing residuals. Biometrics 47 (1991) 1267-1282
9. Hosmer D.W., Hosmer T., Le Cessie S., et al. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med 16 (1997) 965-980
10. Steyerberg E.W., Borsboom G.J., van Houwelingen H.C., et al. Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med 23 (2004) 2567-2586
11. Cox D.R. Two further applications of a model for binary regression. Biometrika 45 (1958) 562-565
12. Miller M.E., Hui S.L., and Tierney W.M. Validation techniques for logistic regression models. Stat Med 10 (1991) 1213-1226
13. Hannan E.L., Arani D.T., Johnson L.W., et al. Percutaneous transluminal coronary angioplasty in New York State. Risk factors and outcomes. JAMA 268 (1992) 3092-3097
14. Shaw R.E., Anderson H.V., Brindis R.G., et al. Development of a risk adjustment mortality model using the American College of Cardiology-National Cardiovascular Data Registry (ACC-NCDR) experience: 1998-2000. J Am Coll Cardiol 39 (2002) 1104-1112
15. Shaw R.E., Anderson H.V., Brindis R.G., et al. Updated risk adjustment mortality model using the complete 1.1 dataset from the American College of Cardiology National Cardiovascular Data Registry (ACC-NCDR). J Invasive Cardiol 15 (2003) 578-580
16. Moscucci M., Kline-Rogers E., Share D., et al. Simple bedside additive tool for prediction of in-hospital mortality after percutaneous coronary interventions. Circulation 104 (2001) 263-268
17. Holmes D.R., Selzer F., Johnston J.M., 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. Circulation 107 (2003) 1871-1876 [electronic publication 2003 Mar 31]
18. DeLong E.R., Peterson E.D., DeLong D.M., et al. Comparing risk-adjustment methods for provider profiling. Stat Med 16 (1997) 2645-2664
19. Harrell Jr. F.E., Lee K.L., Califf R.M., et al. Regression modelling strategies for improved prognostic prediction. Stat Med 3 (1984) 143-152
20. Ulrich M.R., Brock D.M., and Ziskind A.A. Analysis of trends in coronary artery bypass grafting and percutaneous coronary intervention rates in Washington state from 1987 to 2001. Am J Cardiol 92 (2003) 836-839