The increasing incidence and prevalence of chronic kidney disease (CKD) is a serious health challenge globally. The need to examine the longevity of patients with CKD can never be over emphasized. We investigated the survival experience and factors that may contribute to the longevity of chronic kidney disease patient on haemodialysis. Methods: Data for the study were extracted from the record of CKD patient on haemodialysis. Kaplan-Meier survival analysis was done to assess both short-and

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... term survival. The impact of six covariates on survival chances were separately investigated using Log-rank test and collectively examined using both Cox and Weibull models. Akaike Information Criterion was then employed for determination of a better model between variables. Pvalue of <0.05 was considered as statistically significant. Results: The overall median survival time was 182 days. Only 66.3% of all the patients survived their 90 th days after starting dialysis and approximately 25% survived to 366 days. The hazard ratios for those patients with family history of chronic kidney disease was 0.45; 95% CI 0.23-0.90 and for those with urinary symptoms was 0.59; 95% CI 0.35-0.99. Model generated imply h i (t)= -5.1499 exp{-0.7850Family His. Of CKD i -0.5353Urinary symptom i }. The age of the patient was also found to be statistically significant when separately investigated. Archives of Nephrology and Urology 2 Conclusion: Family history of chronic kidney disease and urinary symptoms were found to influence the survival of patients on haemodialysis. Early identification of at-risk family and prompt treatment of urinary symptoms is advocated. Introduction The kidney is an important organ of the body which maintaining internal balance among other functions. When the kidney failed, the function is impaired with consequent retention of waste products of metabolism. Globally, the prevalence of renal disease is at a pandemic rate and has become a public concern. Report has shown that the prevalence of CKD is currently more than 10% in the general population and reaches 14% or more in high-risk subpopulations [1, 2]. There are currently over 1.4 million patients receiving renal replacement therapy (RRT) worldwide [3]. Haemodialysis, a form of RRT is beneficial to individuals with kidney failure. This treatment modality helps to remove waste and excess water from the blood. However, haemodialysis cannot completely reverse the function of the kidney. Mortality is substantially elevated in patients with chronic kidney disease (CKD) on dialysis [4, 5]. A Swedish population-based study compared the mortality in CKD stages 4 and 5 patients on renal replacement therapy. The authors found that during 6553 person-years, 766 patients with CKD stages 4 and 5 died (deaths/100 person-years 12, 95% CI 11 to 13) compared with 924 deaths during 3680 person-years in haemodialysis (25, 95% CI 23 to 27). In the same study, Neovius et al reported that against matched general population controls, the mortality HR was 3.6 (95% CI 3.2 to 4.0) for CKD and 12.6 (95% CI 10.8 to 14.6) for haemodialysis [6]. Survival of patient who cannot afford adequate treatment such as regular dialysis or renal transplant is unimaginable poor due to several complications that is associated with the disease [4, 5] . The financial burden of end stage renal disease (ESRD) is prohibitive. In our country, CKD patients are battling with poverty, inadequate health facilities, lack of subsidy for medical treatment among other challenges with a gloomy outlook. Survival analysis looks at how long it takes for an event to happen. The event outcome may either be positive or negative such as recovery or failure respectively. Analysis of survival data needs special consideration compared to other data because of the censored nature of the data as it does not follow a normal distribution. The survival analysis employed in this study is designed to handle time-to-event data, considering censored cases. These are cases where the event of interest has not occurred yet at the end of the study or before lost to follow-up or it has occurred due to some other causes. The degree of censoring can affect the reliability of the results. We investigated the survival experience and factors that may contribute to longevity of chronic kidney disease patient on haemodialysis in our dialysis centre. We determined the 4-week, 12-week, 26-week and 52-week survival probabilities and the best model while considering the effect of factors such as age, sex, blood pressure, family history of CKD, diabetes and urinary symptoms on the survival of the patient. Literature is scanty on the study of survival chance of dialysis patients in Nigeria. This study therefore, provided a scholarly impetus to improve the study of survival chance of dialysis patients in our practice. This study was a five-year (2010-2014) retrospective review of patients with CKD who received treatment at the haemodialysis unit of Ekiti State University Teaching hospital, Ado Ekiti (EKSUTH), Nigeria. The analyzed data were extracted from the patient's clinical and haemodialysis record. Patients with incomplete data were excluded from the study. The following information was extracted: date of first dialysis session, age of the patient, sex of the patient, blood pressure (BP) of the patient at commencement of dialysis, urinary symptoms, history of diabetes mellitus, family history of CKD, last date of dialysis session, status of patient after last dialysis and patient time. Ethical approval was obtained from the ethical committee of the institution. Definition of terms Date of first dialysis session: This is the date the patient commenced dialysis. This is the treatment starting point for the patient. Age of the patient: This is the patient age at first dialysis. Age was categorized into two groups <60 and >/=60 using 60 years as cut off point. This was used in Cox proportional and Weibull analysis to determine whether age is a predictor of survival time. Blood pressure (BP) of the patient at the commencement of dialysis: Blood pressure classification according to Eight Joint National Committee [7]. Last date of dialysis session: This is the date which the last dialysis was done for the patient. This is the treatment endpoint for the patient. Status of the patient after last the dialysis: This is the treatment outcome of the patient after the last dialysis session. The status may be a failure (death) or censor. Patient time: This is the time a patient spends in the study from the time of first to the last dialysis. Data management and data analysis Data analysis was performed with STATA version 12. Bivariate analysis was conducted using nonparametric technique; Kaplan-Meier Product Limit [8] Method was employed in estimation of the survival functions and hazard rates of the survival data. Also, both Log-Rank Test and Wilcoxon (Breslow) test was used for the comparison of survival functions. Cox's proportional hazard regression (Multivariate analysis) was initially used to model the relationship between the survival time and explanatory variables such as age, sex, hypertension, diabetes, family history and urinary symptoms. Archives of Nephrology and Urology 4 Cox proportional and Weibull, Akaike Information Criterion (AIC) was employed. Model that has AIC estimate with the smallest value was considered to be more precise. Charts and tables were used to present the results. Result Sociodemographic distribution of the patients One hundred and seven patients were the participants. Male constitute a higher proportion, 74 (69.2%). The mean age of the patients was 51 ± 15.4 years with a range of 21-89 years. The largest number of patients falls between age group 50-59 years (22.3%). Most of the patients (43.9%) were secondary school holders and petty trader (43.9%) constituted the highest proportion of their occupations (Table 1). The highest number of patient (46.7%) were treated in year 2013. Out of 107 patients, 40 (37.4%) were censored as alive up till or after the study time or were lost to follow up, 67 (62.6%) patients were reported to have died as a result of CKD. Variables Count Percentage (%) Age Group (Years) 20-29 8 7.8 Urinary symptoms No urinary symptoms (ref.) 0.63 [0.37-1.07] 0.59 [0.35-0.99] Akaike Information Criterion 496.36 267.62 Table 5: Comparison of the result form the Cox and the Weibull model. Model fitting Family history of CKD and urinary symptoms are the only covariates compared to others that influence survival chance in this study. The hazard ratios of the two covariates are supportive. Therefore, better model using computations in table 6 imply; h i (t) = -5.1499 exp {-0.7850Family His. Of CKD i -0.5353Urinary symptoms i } Covariate HR β Urinary status 0.5855 -0.5352891 Family his. Of CKD 0.4561 -0.7850432 Constant 0.0058 -5.14989736