Prediction of Survival after Myocardial Infarction Using Killip Class

Kourosh Sayehmiri, Diana Sarokhani, Hassan Jahanihashemi, Ali Sayehmiri, Mohamad Taher Sarokhani, Farajollah Hemati, Enayatolah Bakhshi, Morteza Motedayen

Biostatistics Department, Qazvin University of Medical Sciences, Qazvin, Iran.
Cardio and Vascular Department, Vali e Asr Hospital, Zanjan University of Medical Sciences, Zanjan, Iran.
Department of computer engineering, Science and Research branch, Islamic Azad University, Kermanshah, Iran.
Department of Statistics and Computer, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.
Economic Departments, Ilam University, Ilam, Iran.
Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran.
Psychosocial Injuries Research Center, Ilam University of Medical Sciences, Ilam, Iran.
Students Research Committee, Ilam University of Medical Sciences, Ilam, Iran.

DOI: 10.4236/ijcm.2012.37102                

Abstract

Background: Short and long term predictions of mortality and survival after a myocardial infarction (MI) are important in order to assist physicians in their decision about optimal treatment. We considered the utility of Killip class and other risk factors in the prediction of cardiac death after a MI. Methods: One hundred and eighty two patients with myocardial infarctions were studied over a one year period. Variables include historical factors, physical examination and noninvasive factors measured during hospitalization. All patients were selected in the Imam Khomeini hospital in Ilam City in Iran. Discriminant function and Logistic regression were used to analyze data. The percent of correct classification was compute using the Jack knife method. Results: The one month, 6 months, and one year mortality rate after MI was 25.8, 29.7, and 32.8 percent, respectively. The rate of mortality for women was 1.78 times higher than of the men (RR = 1.78, P-value = 0.02).The mean age was 62.45 year. Our results show that the mortality at 1 month and 6 months after MI had a significant relation with Killip class (P-value < 0.01). Discriminant function analysis shows that with knowledge of Killip class mortality and patient survival could be predicted with 88.1 percent accuracy up to 6 months. By adding age, cholesterol, and sex it could be increased to 91 percent. The results of logistic regression analysis revealed that there was a significant relation between mortality after MI and variables such a age, sex, cholesterol and the maximum blood urea nitrogen (BUN) level. Conclusion: Death and patient survival of up to one year after MI is predictable using an initial Killip class and other patient characteristics.

Keywords

Prediction; Myocardial Infarction; Logistic Regression; Discriminant Function; Killip Class

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	C. J. L. Murray and A. D. Lopez, “Alternative Projection of Mortality and Disability by Cause 1990-2020: Global Burden of Disease Study,” Lancet, Vol. 349, 1997, pp. 1498-1504.
[2]	R. M. Califf, K. S. Pieper, K. L. Lee, F. Van De Werf, R. J. Simes, P. W. Armstrong and E. J. Topol, “Prediction of 1-Year Survival after Thrombolysis for Acute Myocardial Infarction in the Global Utilization of Streptokinase and TPA for Occluded Coronary Arteries Trial,” Circulation, Vol. 101, No. 19, 2000, pp. 2231-2238. doi:10.1161/01.CIR.101.19.2231
[3]	D. P. Zipes, “Braunwald’s: Heart Diseases: A Text Book of Cardiovascular Medicine,” Vol. 7, W.B. Saunders, Philadelphia, 2005.
[4]	K. Sayehmiri, “Prediction Death and Survival 5 Days after Myocardial Infarction by Using Multivariate Statistics,” Tehran, 1997.
[5]	P. Korhonen, T. Husa, T. Konttila, I. Tierala, M. Makijarvi, H. Vaananen and L. Toivonen, “Complex T-Wave Morphology in Body Surface Potential Mapping in Prediction of Arrhythmic Events in Patients with Acute Myocardial Infarction and Cardiac Dysfunction,” Europace, Vol. 11, No. 4, 2009, pp. 514-520. doi:10.1093/europace/eup051
[6]	E. B. Madsen, et al., “Prediction of Late Mortality after Myocardial Infarction from Variables Measured at Different Time during Hospitalization,” American Journal of Cardiology, Vol. 53, No. 1, 1984, pp. 47-54.
[7]	S. J. Brener and S. G. Ellis, “Predictors of Death and Survival at 30 Days after Primary Angioplasty,” American Heart Journal, Vol. 139, No. 3, 2000, p. 5.
[8]	T. H. Makikallio, P. Barthel, R. Schneider, A. Bauer, J. M. Tapanainen, M. P. Tulppo, G. Schmidt and H. V. Huikuri, “Prediction of Sudden Cardiac Death after Acute Myocardial Infarction: Role of Holter Monitoring in the Modern Treatment Era,” European Heart Journal, Vol. 26, No. 8, 2005, pp. 762-769. doi:10.1093/eurheartj/ehi188
[9]	H. K. Park, S. J. Yoon, H. S. Ahn, L. S. Ahn, H. J. Seo, S. I. Lee and K. S. Lee, “Comparison of Risk-Adjustment Models Using Administrative or Clinical Data for Outcome Prediction in Patients after Myocardial Infarction or Coronary Bypass Surgery in Korea,” International Journal of Clinical Practice, Vol. 61, No. 7, 2007, pp. 1086-1090. doi:10.1111/j.1742-1241.2007.01345.x
[10]	R. M. Califf, et al., “Prediction of 1-Year Survival after Thrombolysis for Acute Myocardial Infarction in the Global Utilization of Streptokinase and TPA for Occluded coronary Arteries Trial,” Circulation, Vol. 101, 2000, pp. 2231-2238.
[11]	P. D Verdoum, et al., “Short Term Survival after Acute Myocardial Infarction Predicted by Homodynamic Parameters,” Circulation, Vol. 52, 1975, pp. 413-419.
[12]	H. Henning, et al., “Prognosis after Acute Myocardial Infarction a Multivariate Analysis of Mortality and Survival,” Circulation, Vol. 59, No. 6, 1997, pp. 1124-1136.
[13]	Y. Sahasakul, S. Chaithiraphan, P. Panchavinnin, P. Jootar, V. Thongtang, N. Srivanasont, N. Charoenchob and C. Kangkagate, “Multivariate Analysis in the Prediction of Death in Hospital after Acute Myocardial Infarction,” British Heart Journal, Vol. 64, No. 3, 1990, pp. 182-185. doi:10.1136/hrt.64.3.182
[14]	L. Hsu, M. P. Senaratne, S. De-Silva, R. E. Rossall and T. Kappagoda, “Prediction of Coronary Events Following Myocardial Infarction Using a Discriminant Function Analysis,” Journal of Chronic Diseases, Vol. 39, No. 7, 1986, pp. 543-552. doi:10.1016/0021-9681(86)90199-2
[15]	G. Dwivedi, R. Janardhanan, S. A. Hayat, T. K. Lim and R. Senior, “Improved Prediction of Outcome by Contrast Echocardiography Determined Left Ventricular Remodeling Parameters Compared to Unenhanced Echocardiography in Patients Following Acute Myocardial Infarction,” European Journal of Echocardiography, Vol. 10, No. 8, 2009, pp. 933-940. doi:10.1093/ejechocard/jep099
[16]	S. Bangalore, S. S. Yao and F. A. Chaudhry, “Prediction of Myocardial Infarction versus Cardiac Death by Stress Echocardiography,” Journal of the American Society of Echocardiography, Vol. 22, No. 3, 2009, pp. 261-267. doi:10.1016/j.echo.2008.12.022
[17]	N. Rahman, K. A. Kazmi and M. Yousaf, “Non-Invasive Prediction of ST Elevation Myocardial Infarction Complications by Left Ventricular Tei Index,” Journal of the Pakistan Medical Association, Vol. 59, No. 2, 2009, pp. 75-78.
[18]	K. A. Fox, O. H. Dabbous, R. J. Goldberg, K. S. Pieper, K. A. Eagle, F. Van de Werf, A. Avezum, S. G. Goodman, M. D. Flather, F. A. Anderson Jr., et al., “Prediction of Risk of Death and Myocardial Infarction in the Six Months after Presentation with Acute Coronary Syndrome: Prospective Multinational Observational Study (GRACE),” British Medical Journal, Vol. 333, No. 7578, 2006, pp. 1091. doi:10.1136/bmj.38985.646481.55
[19]	T. Lundman, “Short and Long-Term Prognosis of Patients with Acute Myocardial Infarction (AMI) and Prediction of Sudden Death,” Forensic Science, Vol. 8, No. 1, 1976, pp. 77-87. doi:10.1016/0300-9432(76)90050-9
[20]	M. J. Young, L. F. McMahon Jr. and J. K. Stross, “Prediction Rules for Patients with Suspected Myocardial Infarction. Applying Guidelines in Community Hospitals,” Archives of International Medicine, Vol. 147, No. 7, 1987, pp. 1219-1222. doi:10.1001/archinte.147.7.1219
[21]	G. M. De Ferrari and P. J. Schwartz, “Sudden Death after Myocardial Infarction. Prediction Based on the Baroreceptor Reflex,” Arch Mal Coeur Vaiss, Vol. 83, No. 10, 1990, pp. 1521-1527.
[22]	K. Piestrzeniewicz, K. Luczak and J. H. Goch, “Value of Blood Adipose Tissue Hormones Concentration—Adi-ponectin, Resistin and Leptin in the Prediction of Major Adverse Cardiac Events (MACE) in 1-Year Follow-Up after Primary Percutaneous Coronary Intervention in ST-Segment Elevation Acute Myocardial Infarction,” Neuroendocrinol Letters, Vol. 29, No. 4, 2008, pp. 581-588.