Competing Risk Analyses of Patients with End-Stage Renal Disease
Keywords:
competing risk, end stage renal disease, kidney diseaseAbstract
Introduction: Chronic kidney disease (CKD) is an important health problem that gradually leads to end-stage renal disease (ESRD). In ESRD patients, death due to other diseases or some events, such as renal transplantation (known as competing risks), would change the probability of observing the event of interest. The aim of this study was to estimate the survival of ESRD patients using competing risk analyses.
Methods: In this retrospective longitudinal study, 307 ESRD patients who were older than 20 were recruited from the dialysis and kidney transplant Centers in Kerman City, Iran, from2007 to 2011. To assess the impacts of the investigated factors on the outcome, a cause-specific hazard model and competing risk models were fitted. Also, the cumulative incidence (CI) approach and sensitivity analysis were implemented. All of the analyses were performed using Stata software, V.12.
Results: The results of competing risk models showed that age and type of dialysis were associated with death (hazard ratio (HR)=1.03, p<0.001 and HR=1.65, p=0.011, respectively). In cause specific hazard model each year increase in age was associated with a 2% increase in the risk of death. Also, the types of dialysis were associated significantly with death (HR=1.93), and the effect of the type of dialysis was estimated as HR=1.51 (p=0.04) when we assumed that all patients who had experienced transplantation survived for the longest survival time. For those for whom receiving the transplantation was considered as death, the HR for the type of dialysis as well as the corresponding p-values were 1.82 and 0.001, respectively.
Conclusion: Ignoring the competing risks of death due to ESRD, such as renal transplantation, in estimating the survival of these patients might lead to overestimation of the results.
References
Mahdavi-Mazdeh M, Saeed HashemiNazri S, Hajghasemi E, Nozari B, Zinat Nadia H, Mahdavi
A.Screening for decreased renal function in taxi drivers in Tehran, Iran. Ren Fail. 2010; 32 (1): 62-8. doi:
3109/08860220903491190. PMID: 20113268.
Kopple, J.D., National kidney foundation K/DOQI clinical practice guidelines for nutrition in chronic renal
failure.Am J Kidney Dis. 2001; 37(1): S66-70. PMID: 11158865.
Aghighi M1, Mahdavi-Mazdeh M, Zamyadi M, HeidaryRouchi A, Rajolani H, Nourozi S.Changing
epidemiology of end-stage renal disease in last 10 years in Iran. Iran J Kidney Dis. 2009; 3 (4): 192-6.
PMID: 19841521.
Beladi-Mousavi, Alemzadeh-Ansari MJ, Alemzadeh-Ansari MH, Beladi-Mousavi M. Long-term survival
of patients with end-stage renal disease on maintenance hemodialysis: a multicenter study in Iran. Iran J
Kidney Dis. 2012; 6 (6): 452-456. PMID: 23146984.
Nafar M, Mousavi SM, Mahdavi-Mazdeh M, Pour-Reza-Gholi F, Firoozan A, Einollahi B, et al.Burden of
chronic kidney disease in Iran: a screening program is of essential need. Iran J Kidney Dis. 2008; 2 (4):
-92. PMID: 19377235.
Roudbari M1, Foruzandeh F, Roudbari S.Survival analysis of dialysis patients and its associated factors in
Zahedan, Iran.Saudi Med J. 2010; 31 (1): 91-3. PMID: 20062909.
Longo D. PoliticsExperiencing Politics: A Legislator's Stories of Government and Health Care. JAMA.
; 286 (8): 971. doi:10.1001/jama.286.8.971.
Latouchea A, Allignol A, Beyersmann J, Labopin M, Fine JP.A competing risks analysis should report
results on all cause-specific hazards and cumulative incidence functions.J ClinEpidemiol. 2013; 66 (6):
-53. doi: 10.1016/j.jclinepi.2012.09.017. PMID: 23415868.
Latouche, A, Boisson V, Chevret S, Porcher R.Misspecified regression model for the subdistribution
hazard of a competing risk.Stat Med. 2007; 26 (5): 965-74. PMID: 16755533.
Southern DA,Faris PD, Brant R, Galbraith PD, Norris CM, Knudtson ML, et al.Kaplan-Meier methods
yielded misleading results in competing risk scenarios. J Clin Epidemiol. 2006; 59 (10): 1110-4. PMID:
Lim HJ,Zhang X, Dyck R, Osgood N.Methods of competing risks analysis of end-stage renal disease and
mortality among people with diabetes.BMC Med Res Methodol. 2010; 10: 97. doi: 10.1186/1471-2288-10- 9. PMID: 20964855, PMCID: PMC2988010.
Lau B, Cole SR, Gange SJ. Competingrisk regression models for epidemiologic data. Am J Epidemiol.
; 170 (2): 244-56. doi: 10.1093/aje/kwp107. PMID: 19494242, PMCID: PMC2732996.
Satagopan, JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD. A note on competing risks
in survival data analysis. Br J Cancer. 2004; 91 (7): 1229-35. PMID: 15305188, PMCID: PMC2410013.
Fine JP, Gray RJ.A proportional hazards model for the subdistribution of a competing risk. ASA. 1999; 94
(446): 496-509. doi:10.1080/01621459.1999.10474144.
Kleinbaum DG, Klein M. Survival analysis: a self-learning text. Second Edition. 2005: Springer. 415-418.
Haghighi AN, Broumand B, D'Amico M, Locatelli F, Ritz E.The epidemiology of end‐stage renal disease
in Iran in an international perspective. Nephrol Dial Transplant. 2002; 17 (1): 28-32. PMID: 11773458.
Vonesh EF, Moran J. Mortality in End-Stage Renal Disease A Reassessment of Differences between
Patients Treated with Hemodialysis and Peritoneal Dialysis. J Am SocNephrol. 1999; 10 (2): 354-65.
PMID: 10215336.
Yeates K, Zhu N, Vonesh E, Trpeski L, Blake P, Fenton S. Hemodialysis and peritoneal dialysis are
associated with similar outcomes for end-stage renal disease treatment in Canada. Nephrol Dial Transplant.
; 27 (9): 3568-75. doi: 10.1093/ndt/gfr674. PMID: 22391139.
Published
Issue
Section
License
Copyright (c) 2020 knowledge kingdom publishing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.