Table of Contents
ISRN Surgery
Volume 2011 (2011), Article ID 714935, 6 pages
http://dx.doi.org/10.5402/2011/714935
Research Article

Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention

1Division of Biostatistics, Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park, Mazer 220, Bronx, New York, NY 10461, USA
2Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY, USA

Received 28 April 2011; Accepted 16 June 2011

Academic Editor: M. Caputo

Copyright © 2011 Abdissa Negassa and E. Scott Monrad. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. T. O'Connor, D. J. Malenka, H. Quinton et al., “Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994–1996,” Journal of the American College of Cardiology, vol. 34, no. 3, pp. 681–691, 1999. View at Publisher · View at Google Scholar
  2. D. R. Holmes, P. B. Berger, K. N. Garratt et al., “Application of the New York state PTCA mortality model in patients undergoing stent implantation,” Circulation, vol. 102, no. 5, pp. 517–522, 2000. View at Google Scholar · View at Scopus
  3. S. E. Kimmel, J. A. Berlin, B. L. Strom, and W. K. Laskey, “Development and validation of a simplified predictive index for major complications in contemporary percutaneous transluminal coronary angioplasty practice,” Journal of the American College of Cardiology, vol. 26, no. 4, pp. 931–938, 1995. View at Publisher · View at Google Scholar
  4. P. C. Block, E. C. Peterson, R. Krone et al., “Identification of variables needed to risk adjust outcomes of coronary interventions: evidence-based guidelines for efficient data collection,” Journal of the American College of Cardiology, vol. 32, no. 1, pp. 275–282, 1998. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Moscucci, G. T. O'Connor, S. G. Ellis et al., “Validation of risk adjustment models for in-hospital percutaneous transluminal coronary angioplasty mortality on an independent data set,” Journal of the American College of Cardiology, vol. 34, no. 3, pp. 692–697, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. E. L. Hannan, M. Racz, T. J. Ryan et al., “Coronary angioplasty volume-outcome relationships for hospitals and cardiologists,” Journal of the American Medical Association, vol. 277, no. 11, pp. 892–898, 1997. View at Google Scholar · View at Scopus
  7. E. L. Hannan, D. T. Arani, L. W. Johnson, H. G. Kemp, and G. Lukacik, “Percutaneous transluminal coronary angioplasty in New York State: risk factors and outcomes,” Journal of the American Medical Association, vol. 268, no. 21, pp. 3092–3097, 1992. View at Publisher · View at Google Scholar · View at Scopus
  8. R. E. Shaw, H. V. Anderson, R. G. Brindis et al., “Development of a risk adjustment mortality model using the American College of Cardiology-National Cardiovascular Data Registry (ACC-NCDR) experience: 1998–2000,” Journal of the American College of Cardiology, vol. 39, no. 7, pp. 1104–1112, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. S. G. Ellis, W. Weintraub, D. Holmes, R. E. Shaw, P. C. Block, and S. B. King, “Relation of operator volume and experience to procedural outcome of percutaneous coronary revascularization at hospitals with high interventional volumes,” Circulation, vol. 95, no. 11, pp. 2479–2484, 1997. View at Google Scholar · View at Scopus
  10. M. Moscucci, K. E. Rogers, D. Share et al., “Simple bedside additive tool for prediction of in-hospital mortality after percutaneous coronary interventions,” Circulation, vol. 104, no. 3, pp. 263–268, 2001. View at Google Scholar · View at Scopus
  11. D. R. Holmes, F. Selzer, J. M. Johnston 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, vol. 107, no. 14, pp. 1871–1876, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Singh, C. S. Rihal, F. Selzer, K. E. Kip, K. Detre, and D. R. Holmes, “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,” Journal of the American College of Cardiology, vol. 42, no. 10, pp. 1722–1728, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Singh, R. J. Lennon, D. R. Holmes, M. R. Bell, and C. S. Rihal, “Correlates of procedural complications and a simple integer risk score for percutaneous coronary intervention,” Journal of the American College of Cardiology, vol. 40, no. 3, pp. 387–393, 2002. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Wu, E. L. Hannan, G. Walford et al., “A risk score to predict in-hospital mortality for percutaneous coronary interventions,” Journal of the American College of Cardiology, vol. 47, no. 3, pp. 654–660, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Negassa, E. S. Monrad, J. Y. Bang, and V. S. Srinivas, “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,” The American Heart Journal, vol. 154, no. 2, pp. 322–329, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Negassa, S. Monrad, and V. S. Srinivas, “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),” The American Journal of Cardiology, vol. 103, no. 7, pp. 937–942, 2009. View at Publisher · View at Google Scholar
  17. M. Singh, C. S. Rihal, R. J. Lennon, K. N. Garratt, and D. R. Holmes, “A critical appraisal of current models of risk stratification for percutaneous coronary interventions,” The American Heart Journal, vol. 149, no. 5, pp. 753–760, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. N. Seshadri, P. L. Whitlow, N. Acharya, P. Houghtaling, E. H. Blackstone, and S. G. Ellis, “Emergency coronary artery bypass surgery in the contemporary percutaneous coronary intervention era,” Circulation, vol. 106, no. 18, pp. 2346–2350, 2002. View at Publisher · View at Google Scholar · View at Scopus
  19. C. E. Chambers, G. J. Dehmer, D. A. Cox et al., “Defining the length of stay following percutaneous coronary intervention: an expert consensus document from the society for cardiovascular angiography and interventions endorsed by the American college of cardiology foundation,” Catheterization and Cardiovascular Interventions, vol. 73, no. 7, pp. 847–858, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth International Group, Belmont, Calif, USA, 1984.
  21. New York State Department of Health, “Percutaneous Coronary Intervention (PCI) in New York State, 2002–2004,” 2006, http://www.health.state.ny.us. View at Google Scholar
  22. S. S. Kumar, A. Negassa, E. S. Monrad, and V. S. Srinivas, “The Mayo clinic risk adjustment model predicts in-hospital mortality in a contemporary primary angioplasty population,” Journal of Invasive Cardiology, vol. 17, pp. 522–526, 2005. View at Google Scholar
  23. L. M. Sullivan, J. M. Massaro, and R. B. D'Agostino, “Presentation of multivariate data for clinical use: the Framingham study risk score functions,” Statistics in Medicine, vol. 23, no. 10, pp. 1631–1660, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Negassa, A. Ciampi, M. Abrahamowicz, S. Shapiro, and J.-F. Boivin, “Tree-structured prognostic classification for censored survival data: validation of computationally inexpensive model selection criteria,” Journal of Statistical Computation and Simulation, vol. 67, no. 4, pp. 289–318, 2000. View at Google Scholar · View at Scopus
  25. A. Negassa, A. Ciampi, M. Abrahamowicz, S. Shapiro, and J.-F. Boivin, “Tree-structured subgroup analysis for censored survival data: validation of computationally inexpensive model selection criteria,” Statistics and Computing, vol. 15, no. 3, pp. 231–239, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Agresti, Categorical Data Analysis, Wiley, New York, NY, USA, 1990.
  27. Q. Vuong, “Likelihood ratio tests for model selection and non-nested hypotheses,” Econometrica, vol. 57, pp. 307–333, 1989. View at Publisher · View at Google Scholar
  28. S. L. Zeger, K. Y. Liang, and P. S. Albert, “Models for longitudinal data: a generalized estimating equation approach,” Biometrics, vol. 44, no. 4, pp. 1049–1060, 1988. View at Google Scholar · View at Scopus
  29. E. D. Peterson, D. Dai, E. R. DeLong et al., “Contemporary mortality risk prediction for percutaneous coronary intervention: results from 588,398 procedures in the National Cardiovascular Data Registry,” Journal of the American College of Cardiology, vol. 55, no. 18, pp. 1923–1932, 2010. View at Google Scholar
  30. S. Addala, C. L. Grines, S. R. Dixon et al., “Predicting mortality in patients with ST-elevation myocardial infarction treated with primary percutaneous coronary intervention (PAMI risk score),” The American Journal of Cardiology, vol. 93, no. 5, pp. 629–632, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Halkin, M. Singh, E. Nikolsky et al., “Prediction of mortality after primary percutaneous coronary intervention for acute myocardial infarction: the CADILLAC risk score,” Journal of the American College of Cardiology, vol. 45, no. 9, pp. 1397–1405, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. T. Killip and J. T. Kimball, “Treatment of myocardial infarction in a coronary care unit: a two year experience with 250 patients,” The American Journal of Cardiology, vol. 20, no. 4, pp. 457–464, 1967. View at Google Scholar · View at Scopus