Original Article

Evaluation of Death among the Patients Undergoing Permanent Pacemaker Implantation: A Competing Risks Analysis

Abstract

Background: Permanent artificial pacemaker is one of the important therapies for treatment of cardiac conduction system problems. The present study aimed to determine the association between some predictive variables and all-cause and cause-specific mortality in the patients who had undergone pacemaker implantation.

Methods: This study was conducted on 1207 patients who had undergone permanent pacemaker implantation in the hospitals affiliated with Shiraz University of Medical Sciences, Iran, from Mar 2002 to Mar 2012. The variables that existed in the patients’ medical records included sex, diabetes mellitus, obesity, cerebrovascular accident, cardiomegaly, smoking, hypertension, ischemic heart disease, congenital heart disease, sick sinus syndrome, and atrial fibrillation. Competing risks model was used to assess the association between the predictive variables and cause-specific (i.e., cardiac and vascular) mortality.

Results: The patients’ mean age was 66.32±17.92 yr (70.62±14.45 yr in the patients with single-chamber pacemakers vs. 61.91±17.69 yr in those with two-chamber pacemakers) (P<0.001). Sick sinus syndrome and age increased the risk of all-cause mortality, while two-chamber pacemaker decreased this risk. Obesity increased the risk of cardiac death, and diabetes mellitus and heart valve disease increased the risk of vascular death.

Conclusion: The variables predicting mortality in all-cause model were completely different from those in cause-specific model. Moreover, death in such patients may occur due to reasons other than pacemaker. Therefore, future studies, particularly prospective ones, are recommended to use competing risks models.

 

Ali-Akbari F, Khalifehzadeh A, Parvin N (2009). The effect of short time telephone follow-up on physical conditions and quality of life in patients after pacemaker implantation. Journal of Shahrekord University of Medical Sciences, 11:23-28.

Brunner M, Olschewski M, Geibel A, Bode C, Zehender M (2004). Long-term survival after pacemaker implantation - Prognostic importance of gender and baseline patient characteristics. Eur Heart J, 25:88-95.

Udo EO, van Hemel NM, Zuithoff NP, Doevendans PA, Moons KG (2013). Prognosis of the bradycardia pacemaker recipient assessed at first implantation: a nationwide cohort study. Heart, 99:1573-8.

Kim WH, Joung B, Shim J, Park JS, Hwang ES, Pak HN, Kim S, Lee M (2010). Long-term outcome of single-chamber atrial pacing compared with dual-chamber pacing in patients with sinus-node dysfunction and intact atrioventricular node conduction. Yonsei Med J, 51:832-7.

Marchandise S, Scavee C, le Polain de Waroux JB, de Meester C, Vanoverschelde JL, Debbas N (2012). Long-term follow-up of DDD and VDD pacing: a prospective non-randomized single-centre comparison of patients with symptomatic atrioventricular block. Europace, 14:496-501.

Ovsyshcher E, Hayes DL, Furman S (1998). Dual-Chamber Pacing Is Superior to Ventricular Pacing Fact or Controversy? Circulation, 97:2368-2370.

Vassolo M, Lamas G (1999). Dual-chamber vs ventricular pacing in the elderly: quality of life and clinical outcomes. Eur Heart J , 20:1607-1608.

Rajaeefard A, Ghorbani M, Babaee Baigi MA, Tabatabae H (2015). Ten-year Survival and Its Associated Factors in the Patients Undergoing Pacemaker Implantation in Hospitals Affiliated to Shiraz University of Medical Sciences During 2002 - 2012. Iran Red Crescent Med J, 17:e20744.

Koller MT, Schaer B, Wolbers M, Sticherling C, Bucher HC, Osswald S (2008). Death Without Prior Appropriate Implantable Cardioverter-Defibrillator Therapy A Competing Risk Study. Circulation, 117:1918-1926.

Wolbers M, Koller MT, Witteman JC, Steyerberg EW (2009). Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology, 20:555-561.

Putter H, Fiocco M, Geskus R (2007). Tutorial in biostatistics: competing risks and multi‐state models. Stat Med, 26:2389-2430.

Kalbfleisch JD, Prentice RL (2011). The statistical analysis of failure time data. ed. John Wiley & Sons.

Tsiatis A (1975). A nonidentifiability aspect of the problem of competing risks. Proc Natl Acad Sci U S A, 72:20-22.

Grunkemeier GL, Anderson RP, Miller DC, Starr A (1997). Time-related analysis of nonfatal heart valve complications: cumulative incidence (actual) versus Kaplan-Meier (actuarial). Circulation, 96:II-70-4; discussion II-74-5.

Grunkemeier GL, Jin R, Eijkemans MJ, Takkenberg JJ (2007). Actual and actuarial probabilities of competing risks: apples and lemons. Ann Thorac Surg, 83:1586-1592.

Ghaem Maralani H, Tai BC, Wong TY, Tai ES, Li J, Wang JJ, Mitchell P (2014). The prognostic role of body mass index on mortality amongst the middle-aged and elderly: a competing risk analysis. Diabetes Res Clin Pract, 103:42-50.

Barili F, Cheema FH, Barzaghi N, Grossi C (2012). The analysis of intensive care unit length of stay in a competing risk setting. Eur J Cardiothorac Surg, 41:232.

Deslandes E, Chevret S (2010). Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data. BMC Med Res Methodol, 10:69.

Klein JP, Andersen PK (2005). Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function. Biometrics, 61:223-229.

Kleinbaum DG, Klein M (1996). Survival Analysis: A Self-learning Text. ed. Springer.

Pintilie M (2006). Competing risks: a practical perspective. ed. John Wiley & Sons.

Abadi A, Dehghani-Arani M, Yavari P, Alavi-Majd H, Bajik K (2013). Application of the competing risk models for the analysis of risk factors in patients with breast cancer. KAUMS Journal (FEYZ), 16:546-552.

Barili F, Barzaghi N, Cheema FH, Capo A, Jiang J, Ardemagni E, Argenziano M, Grossi C (2013). An original model to predict Intensive Care Unit length-of stay after cardiac surgery in a competing risk framework. Int J Cardiol, 168:219-225.

Tai B-C, Grundy R, Machin D (2011). On the importance of accounting for competing risks in pediatric brain cancer: II. Regression modeling and sample size. Int J Radiat Oncol Biol Phys, 79:1139-1146.

Shen WK, Hammill SC, Hayes DL, Packer DL, Bailey KR, Ballard DJ, Gersh BJ (1994). Long-Term Survival After Pacemaker Implantation For Heart-Block In Patients Greater-Than-Or-Equal-To-65 Years. Am J Cardiol, 74:560-564.

Ozcan C, Jahangir A, Friedman PA, Patel PJ, Munger TM, Rea RF, Lloyd MA, Packer DL, Hodge DO, Gersh BJ, Hammill SC, Shen WK (2001). Long-term survival after ablation of the atrioventricular node and implantation of a permanent pacemaker in patients with atrial fibrillation. N Engl J Med, 344:1043-1051.

Lamas GA, Orav J, Stambler BS, Ellenbogen KA, Sgarbossa EB, Huang SKS, Marinchak RA, Estes NAM, Mitchell GF, Lieberman EH, Mangione CM, Goldman L, Pacemaker Selection Elderly I (1998). Quality of life and clinical outcomes in elderly patients treated with ventricular pacing as compared with dual-chamber pacing. N Engl J Med, 338:1097-1104.

Lamas GA, Lee KL, Sweeney MO, Silverman R, Leon A, Yee R, Marinchak RA, Flaker G, Schron E, Orav EJ (2002). Ventricular pacing or dual-chamber pacing for sinus-node dysfunction. N Engl J Med, 346:1854-1862.

Sowers JR1 (2003). Obesity as a cardiovascular risk factor. Am J Med, 115 Suppl 8A:37S-41S.

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IssueVol 46 No 6 (2017) QRcode
SectionOriginal Article(s)
Keywords
Pacemaker Competing for risk Sick sinus syndrome

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How to Cite
1.
GHAEM H, GHORBANI M, ZARE DORNIANI S. Evaluation of Death among the Patients Undergoing Permanent Pacemaker Implantation: A Competing Risks Analysis. Iran J Public Health. 2017;46(6):820-826.