American Journal of Cardiology
Volume 103, Issue 7 , Pages 937-942 , 1 April 2009

A Simple Prognostic Classification Model for Postprocedural Complications After Percutaneous Coronary Intervention for Acute Myocardial Infarction (from the New York State Percutaneous Coronary Intervention Database)

  • Abdissa Negassa, PhD

      Affiliations

    • Division of Biostatistics, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
    • Corresponding Author InformationCorresponding author: Tel: 718-430-3575; fax: 718-430-8649
    • ,
  • E. Scott Monrad, MD

      Affiliations

    • Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
    • ,
  • Vankeepuram S. Srinivas, MB, BS

      Affiliations

    • Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York

Received 8 September 2008 ,Revised 21 November 2008 ,Accepted 21 November 2008.

References 

  1. O'Connor GT, Malenka DJ, Quinton H, Robb JF, Kellett MA, Shubrooks S, et al Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994–1996. J Am Coll Cardiol. 1999;34:681–691
  2. Holmes DR, Berger PB, Garratt KN, Mathew V, Bell MR, Barsness GW, et al. Application of the New York State PTCA mortality model in patients undergoing stent implantation. Circulation. 2000;102:517–522
  3. Kimmel SE, Berlin JA, Strom BL, Laskey WK Registry Committee of the Society for Cardiac Angiography and Interventions. Development and validation of a simplified predictive index for major complications in contemporary percutaneous transluminal coronary angioplasty practice. J Am Coll Cardiol. 1995;26:931–938
  4. Block PC, Peterson EC, Krone R, Kesler K, Hannan E, O'Connor GT, et al. Identification of variables needed to risk adjusted outcomes of coronary interventions: evidence-based guidelines for efficient data collection. J Am Coll Cardiol. 1998;32:275–282
  5. Moscucci M, O'Connor GT, Ellis SG, Malenka DJ, Sievers J, Bates ER, et al. Validation of risk adjustment models for in-hospital percutaneous transluminal coronary angioplasty mortality on an independent data set. J Am Coll Cardiol. 1999;34:692–697
  6. Hannan EL, Racz M, Ryan TJ, McCallister BD, Johnson LW, Arani DT, et al. Coronary angioplasty volume-outcome relationships for hospitals and Cardiologists. JAMA. 1997;277:892–898
  7. Hannan EL, Arani DT, Johnson LW, Kemp HG, Lukacik G. Percutaneous transluminal coronary angioplasty in New York State: risk factors and outcomes. JAMA. 1992;268:3092–3097
  8. Shaw RE, Anderson HV, Brindis RG, Krone RJ, Klein LW, McKay CR, 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. 2002;39:1104–1112
  9. Ellis SG, Weintraub W, Holmes D, Shaw RE, Block PC, King SB. Relation of operator volume and experience to procedural outcome of percutaneous coronary revascularization at hospitals with high interventional volumes. Circulation. 1997;95:2479–2484
  10. Moscucci M, Rogers KE, Share D, O'Donnell M, Eward MA, Meengs WL, et al. Simple bedside additive tool for prediction of in-hospital mortality after percutaneous coronary interventions. Circulation. 2001;104:263–268
  11. Holmes DR, Selzer F, Johnston JM, Kelsey SF, Holubkov R, Cohen HA, et al. Modeling and risk prediction in the current era of intervention cardiology: a report from the National Heart, Lung, and Blood Institute Dynamic Registry. Circulation. 2003;107:1871–1876
  12. Singh M, Rihal CS, Selzer F, Kip KE, Detre K, Holmes DR. 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. J Am Coll Cardiol. 2003;42:1722–1728
  13. Singh M, Lennon RL, Holmes DR, Bell MR, Rihal CS. Correlates of procedural complications and a simple integer risk score for percutaneous coronary intervention. J Am Coll Cardiol. 2002;40:387–393
  14. Wu C, Hannan EL, Walford G, Ambrose JA, Holmes DR, King SB, et al. A risk score to predict in-hospital mortality for percutaneous coronary interventions. J Am Coll Cardiol. 2006;47:654–660
  15. Singh M, Rihal CS, Lennon RJ, Garratt KN, Holmes DR. A critical appraisal of current models of risk stratification for percutaneous coronary interventions. Am Heart J. 2005;149:753–760
  16. Seshadri N, Whitlow PL, Acharya N, Houghtaling P, Blackstone EH, Ellis SG. Emergency coronary artery bypass surgery in the contemporary precutaneous coronary intervention era. Circulation. 2002;106:2346–2350
  17. Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Belmont, CA: Wadsworth International Group; 1984;358
  18. Negassa A, Monrad ES, Bang JY, Srinivas VS. Tree-structured risk stratification of in-hospital mortality after percutaneous coronary intervention for acute myocardial infarction: a report from the New York State percutaneous coronary intervention database. Am Heart J. 2007;154:322–329
  19. New York State Department of Health. Percutaneous Coronary Intervention (PCI) in New York State, 2002–2004. http://www.health.state.ny.usAccessed on February 18, 2008
  20. Kumar SS, Negassa A, Monrad ES, Srinivas VS. The Mayo Clinic risk adjustment model predicts in-hospital mortality in a contemporary primary angioplasty population. J Invasive Cardiol. 2005;17:522–526
  21. Sullivan LM, Massaro JM, D'Agostino RB. Presentation of multivariate data for clinical use: the Farmingham study risk score functions. Stat Med. 2004;23:1631–1660
  22. Negassa A, Ciampi A, Abrahamowicz M, Shapiro S, Boivin J-F. Tree-structured prognostic classification for censored survival data: validation of computationally inexpensive model selection criteria. J Statistical Computation Simulation. 2000;67:289–318
  23. Negassa A, Ciampi A, Abrahamowicz M, Shapiro S, Boivin J-F. Tree-structured subgroup analysis for censored survival data: validation of computationally inexpensive model selection criteria. Statistics and Computing. 2005;15:231–239
  24. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36
  25. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;42:1049–1060
  26. Addala S, Grines CL, Dixon SR, Stone GW, Boura JA, Ochoa AB, et al. Predicting mortality in patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention (PAMI risk score). Am J Cardiol. 2004;93:629–632
  27. Halkin A, Singh M, Nikolsky E, Grines CL, Tcheng JE, Garcia E, et al Prediction of mortality after primary percutaneous coronary intervention for acute myocardial infarction: the CADILLAC risk score. J Am Coll Cardiol. 2005;45:1397–1405
  28. Killip T, Kimball JT. Treatment of myocardial infarction in coronary care unit: a two-year experience with 250 patients. Am J Cardiol. 1967;20:457–464
  29. Hochman JS, Sleeper LA, Godfrey E, McKinlay SM, Sanborn T, Col J, et al. Should we emergently revascularize occluded coronaries for cardiologic shock: an international randomized trial of emergency PTCA/CABG: trial design. Am Heart J. 1999;137:313–321
  30. Hochman JS, Sleeper LA, White HD, Dzavik V, Wong SC, Menon V, et al One-year survival following early revascularization for cardiogenic shock. JAMA. 2001;285:190–192

 This work was supported by Grant HL080580-01 A2 (AN) from the National Lung, Heart and Blood Institute, National Institutes of Health, Bethesda, Maryland.

PII: S0002-9149(08)02180-2

doi: 10.1016/j.amjcard.2008.11.055

American Journal of Cardiology
Volume 103, Issue 7 , Pages 937-942 , 1 April 2009

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