Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia
Clinical Medicine Research
Volume 6, Issue 6, November 2017, Pages: 201-208
Received: Oct. 18, 2017;
Accepted: Nov. 16, 2017;
Published: Dec. 15, 2017
Views 2240 Downloads 112
Mekiya Hussein, Department of Statistics, Haramaya University, Haramaya, Ethiopia
Geremew Muleta, Department of Statistics, Jimma University, Jimma, Ethiopia
Dinberu Seyoum, Department of Statistics, Jimma University, Jimma, Ethiopia
Demeke Kifle, Department of Statistics, Jimma University, Jimma, Ethiopia
Dechasa Bedada, Department of Statistics, Jimma University, Jimma, Ethiopia
Background: Chronic kidney disease (CKD) with diagonesised end-stage renal disease (ESRD) is common public health problems worldwide. This study was aimed to investigate socio-economics and clinical characteristics determinants among end-stage renal disease (ESRD) population. Method: This study is a retrospective cohort design which was conducted during May 2012 to April 2016 and included 500 ESRD patients at Adama Hospital Medical College. Retrospectives data were gathered by reviewing patients’ medical and surgical wards history. The Cox PH regression and parametric survival (Weibull, Log-logistic and log normal) models were molded and compared for examining survival analysis of ESRD patient using R statistical package software. Results: The study participants are 500 ESRD patients, 72.40% were alive at the end of this study, while 27.40% were died. The survival time of ESRD Majority of patients (66.20%) were female. Log-normal model had fitted the ESRD data set best relatively among possible candidate models. The age at the time of admission to ESRD (HR=0.94, p-value < 0.05), female (HR=0.54, p-value <0.05) and family history (HR=0.45, p-value<0.05) had significantly shorter survival time of ESRD patients to mortality. Conclusion: parametric survival model with baseline hazard lognormal distribution was found appropriate to our dataset. This study identified that having ESRD with complications increases the probability of death. The family history of experiencing ESRD is a driver for being ESRD patient. Female patients had greater risk of death than males in this study. Age specific follow-up is necessary to reduce the mortality related to ESRD.
Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia, Clinical Medicine Research.
Vol. 6, No. 6,
2017, pp. 201-208.
Temesgen F., Mehidi K, Tilahun Y. (2014). Prevalence of chronic kidney disease and associated risk factors among diabetic patients in southern Ethiopia, American Journal of Health Research, 2 (4): 216-221 (http://www.sciencepublishinggroup.com/j/ajhr).
James MT, Quan H, Tonelli M, et al. (2009 Jul); CKD and risk of hospitalization and death with pneumonia. American Journal of Kidney Diseases. 54 (1): 24-32. PMID19447535.
Atkins RC, Zimmet P. World Kidney Day 2010 Feb: Diabetic Kidney Disease—Act Now or Pay Later. Am J Kidney Dis. 55 (2): 205–8.
Zilisteanu, D. S.(2013) Late nephrology referral and impact on morbidity and mortality of patients with chronic renal disease, Ph. D. Thesis, Carol Davila University of Medicine and Pharmacy, Bucharest.
National Kidney Foundation. (2002 Feb); K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 39 (2 Suppl 1): S1-266. PMID 11904577.
USRDS. United States Renal Data System 2008 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; www.usrds.org/adr_2008.htm. Accessed on Jan. 2010.
Gilbertson DT, Liu J, Xue JL, et al. (2015); projecting the number of patients with end stage renal disease in the United States to the year. Journal of the American Society of Nephrology; 16 (12): 3736-41. PMID 16267160.
USRDS. United States Renal Data System 2010 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.
Zewdu W. (1994) Acute renal failure in Addis Ababa, Ethiopia: a prospective study of 136 patients. Ethiop Med J; 32 (2): 79-87.
Wali RK. (2010) Aspirin and the prevention of cardiovascular disease in chronic kidney disease: time to move forward? J Am Coll Cardiol; 56: 966–8.
Schieppati A, Remuzzi G. (2003) the future of renoprotection: frustration and promises. Kidney Int; 64: 1947–1955
Zhang L, Wang F, Wang L, Wang W, Liu B, Liu J, Chen M, He Q, Liao Y, Yu X, Chen N, Zhang JE, Hu Z, Liu F, Hong D, Ma L, Liu H, Zhou X, Chen J, Pan L, Chen W, Wang W, Li X, Wang H; (2012) Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet, 379: 815-822.
Randeree IG, Czarnocki A, Moodley J, Seedat YK, Naiker IP. (1995) Acute renal failure in pregnancy in South Africa. Ren Fail; 17 (2): 147-153.
Cheng, C. L., Kao, Y. H., Lin, S. J., Lee, C. H. & Lai, M. L. (2011) Validation of the National Health Insurance Research Database with ischemic stroke cases in Taiwan. Pharmacoepidemiology and drug safety 20, 236–242.
Naicker S. End-stage renal disease in sub-Saharan Africa. Ethn Dis 2009; 19 (S1): 13-15.
Kaplan, E. L., and P. Meier. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53: 457–481.
Oakes D. (1977), the asymptotic information in censored survival data. Biometrika. 64, 441-8.
Lawless, J. F., (1982). Statistical Methods and Model for Lifetime Data; Wiley, New York.
Kleinbaum D, Klein M. Survival analysis: A self- learning text, New York, Springer-Verlag. 2005.
Kalbfleisch, J. D., and Prentice, R. L. Marginal likelihoods based on Cox. s regression and life model. Biometrika 60 (1973), 267.278.
US Renal Data System. USRDS 2002 Annual Data Report. Bethesda: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.
McGilchrist, C. A. and Aisbett, C. W. (1991), Regression with frailty in survival analysis. Biometrics 47, 461-466.
Fatiu A, Rashad S. (2008) CKD Prevention in Sub-Saharan Africa: A Call for Governmental, Nongovernmental, and Community Support. Am J. Kidney Dis. 51: 515–523. doi: 10.1053/j.ajkd.2007.12.006? [PubMed].
Tamiru Sh., Esayas K., Belete H., Amare D. and Tewodros A., (2013) Survival patterns of patients on maintenance Hemodialysis for end stage renal disease in Ethiopia: summary of 91 cases, BMC Nephrology. 14: 127, PMCID: PMC3693969.
IBRAHIM, J. G., CHEN, M.-H., and SINHA, D. Bayesian Survival Analysis. Springer-Verlag, New York, 2001.
Go AS, Chertow GM, Fan D, et al. (2004 Sep 23) Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. New England Journal of Medicine; 351 (13): 1296-305. PMID 15385656.
Laird, N., and Olivier, D. (2012) Covariance analysis of censored survival data using log-linear analysis.
Ohta M, Babazono T, Uchigata Y, Iwamoto Y. (2010) Comparison of the prevalence of chronic kidney disease in Japanese patients with Type 1 and Type 2 diabetes. Diabet Med. 27 (9): 1017–23.
Retnakaran R, Cull CA, Thorne KI, Adler AI, Holman RR. (2006) Risk Factors for Renal Dysfunction in Type 2 Diabetes U. K. Prospective Diabetes Study 74. Diabetes. 55 (6): 1832–9.
Klein, J. P., and Moeschberger, M. L. (1997) Survival Analysis: Techniques for Censored and Truncated Data. Springer, New York.
Limpert, E., W. A. Stahel and M. Abbt, 2001. 24. Nardi, A. and M. Schemper, 2003. Log-normal distributions across the sciences: keys and clues. Biosciences, 51 (5): 341-52.
Therneau T, Grambsch P. (2000) Modeling survival data: Extending the Cox Model, New York, Springer-Verlag.