The prevalence of Chronic Kidney Disease (CKD) and its evolution to End-Stage Renal Disease (ESRD) have been increasing steadily at global and national levels [1-3]. It was projected that in 2040 it would be the fifth leading cause of death worldwide [4, 5]. Hemodialysis (HD) is the major treatment modality for renal replacement therapy worldwide. Although maintenance dialysis methods have successfully improved the survival of patients with terminal uremia, mortality remains high [6]. Approximately 9-13% of patients on hemodialysis in India die within one year [7, 8]. The adjusted rates of all-cause mortality are 6.3 to 8.2 times greater for dialysis patients than for the general population [9].

Identifying the prognostic factors that are associated with the mortality of patients with HD certainly helps to impact on the survival of HD cohort [10]. Most of the studies related to the survival of patients with HD have come from the developed world [11]. Hence it is vital for the developing, underdeveloped and the poorer group of patients in the third world to have their own epidemiological studies pertaining to those geographical areas with their own unique disease patterns, etiologies, demographics and differing standards of care. However, there is a dearth of studies in the rural, impoverished patient sector. So we looked at the survival of our HD cohort with the following study objectives.

During the study, there were 264 patients registered for HD. Out of them, 35 had incomplete information on the study variables and hence were excluded from the study. Finally, 229 eligible HD patients were included, out of which 112 (48.9%) expired (outcome of interest) during the study period and 117 were alive. The majority of the participants (176(76.91% were males. Most of them had hypertension 148(64.4%) and/or diabetes mellitus as a comorbidity and 20(8.7%) had other forms of kidney pathology in form of pyelonephritis (n=6), chronic glomerulonephritis (n=8) and Autosomal Dominant Polycystic Kidney Disease (n=6). Left Ventricular Hypertrophy (LVH) was found in 139 (60.7%) participants. The distribution of comorbidities and LVH were statistically significant (p<0.001) between patients who died and survived (Table 1).

The mean (SD) age of all the patients included was 51.1 (12.5) years. The mean serum phosphorous, QTC interval, blood urea, URR, and KTv values were significantly higher and mean haemoglobin and serum albumin values were lower among patients who expired (Table 2). Of the 112 expired patients, the major cause of death was due to cardiac pathology 29 (25.7%) followed by sepsis 21 (18.6%). The other causes were cerebrovascular accident 8 (7%), gasto-intestinal bleed 6 (5.4%), brain tumor 3 (2.6%), respiratory failure 4 (3.6%) and hemoptysis 3 (2.6%). Sudden death was recorded in 38(33.9%) patients. The number of patients in this cohort who had a dialysis catheter were only 26.

The Kaplan – Meir survival analysis showed that the estimated median survival was 819 days (95% CI: 750 – 1043) for the HD patients followed for five years. Unadjusted hazard ratios for death were estimated by Cox regression analysis using a proportional model. It showed that the presence of a comorbid condition and LVH, higher serum phosphorus, lower serum hemoglobin, prolonged QTC interval, higher blood urea, higher URR and KTv values, and lower serum albumin were statistically associated with the death of the HD patients. Adjusted Cox proportional model, including those variables that were statistically significant in the unadjusted model showed the presence of LVH [HR: 78.1 (95% CI: 10.8 – 565)], single unit increase in serum phosphorus [HR:1.26 (95% CI: 1.1 – 1.45)], single unit decrease in haemoglobin value [HR: 0.78 (95% CI: 0.69 – 0.87)], and single unit decrease in serum albumin [HR:0.52 (95% CI: 0.37 – 0.72)] were significantly associated with death during HD (Table 3). The survival curve separated by the status of left ventricular hypertrophy among patients undergoing chronic haemodialysis after adjusting for covariates is shown in Fig. (1).

Fig. (1). The survival curve separated by status of left ventricular hypertrophy among patients undergoing chronic haemodialysis after adjusting for the covariates.

In the present study, 229 patients on HD for various chronic kidney diseases were followed retrospectively for five years and half of them died during the study period. Cardiac pathology accounted for one fourth of those deaths and one third were sudden deaths during HD. The Kaplan – Meir survival analysis showed that their median survival was 819 days. The median survival time is actually the median time on dialysis until death. Three-fourths of them were male and around 60% of them had hypertension and or diabetes mellitus as a comorbidity. Adjusted Cox proportional model showed the presence of LVH, a decrease in haemoglobin and serum albumin and an increase in serum phosphorus were significantly associated with death during HD.

Median survival was 819 days in our study, whereas a study in Ghana showed it as 90 days and in Ethiopia it was 263 days [12, 13]. There could be various factors associated with the five year survival of HD patients. Those factors are further discussed below. It was found that gender was not significantly associated with survival in our study, that was similar to a study done in the UK [14]. However, few previous studies showed that the mortality was higher in males [15-17]. Hypertension and diabetes mellitus were associated with poor prognosis. However, after adjusting for confounders, they became statistically insignificant in the current study. Studies in other countries also found that diabetic patients do more badly on HD [18]. On the contrary, an Indian study reported no significant survival difference between non-diabetics and diabetics.

Cardiac diseases account for major cause of mortality in our study and that was similar to the finding of a previous study [19]. Patients with LVH had a significantly higher probability of death and the same finding was reported in many other studies as well [20, 21]. LVH has been described as one of the major factors which lead to sudden cardiac deaths in the general population and more so in the dialysis population, especially in the presence of cardiac disease [22]. In hemodialysis patients, ACE inhibitors play a key role in the regression of LVH, independent of blood pressure changes. Hence screening for LVH and treating them with ACE inhibitors may actually help to improve survival amongst HD patients.

Serum Hb%, albumin and phosphate levels were an independent predictor of mortality in the current study. In alignment with our study, previous studies also revealed that higher serum phosphorus values were a bad prognostic marker [23-25]. In order to improve survival among HD patients, close attention needs to be paid to these relevant factors also, when they come for follow-up. In an earlier French study, where they looked at aortic pulse wave velocity and aortic stiffness and survival in hemodialysis patients, it was demonstrated that ACE inhibitors play a key role in the regression of LVH and had a favorable effect on the survival of patients in hemodialysis independent of blood pressure changes [26]. This is an important observation supporting the role of ACE Inhibitors in hemodialysis patients to cause regression of LVH in light of these results, as shown in our study.

Infections are an important cause of mortality on dialysis in most dialysis units and our study also showed that infection was the second commonest cause of mortality and cardiovascular causes are the commonest, unlike US studies which showed that infection was the commonest cause of death [27]. This is important as our study shows the significance of serum albumin levels, as lower serum albumin levels are directly linked to immunity and mortality.

A survival analysis conducted in Indonesia showed a significant difference in survival on dialysis based on the presence of insurance status compared to non-insured groups (p-value<0.05 [28]. This factor became redundant in our study as almost all patients were covered by insurance given by the government or other private insurance providers. This goes to show that it is not merely a patient factor that is impacting the survival of these patients but also the characteristics of the physician’s treatment modalities and those of the institutions concerned.

A Canadian study has demonstrated that there is a lower risk of mortality in hemodialysis care in private, not-for-profit centers when compared with care in private for-profit centers. When using a random-effects model, the pooled estimate showed that there was a higher risk of dying associated with private, for-profit dialysis center (relative risk, 1.08; 95% confidence interval, 1.04-1.13; P<.001) [29]. Our rural hemodialysis centre offers a purely non-profit model with free dialysis and free food being offered to our patients using government schemes.

A study from Macedonia indicated that death actually is the most severe result of inadequate dialysis and may in fact be used as an indicator of dialysis adequacy [30]. Hence, measures to improve the treatment factors may help in improving survival. Yet another study from Philippines discussed developing a probability risk factor of death in ESRD patients generated by Cox regression and Weibull distribution at a certain age to help us to improve the quality of medications and methodology to help lessen the risk of death in ESRD patients [31].

As there were sudden deaths captured in the data gathered, we need to address the causative factors for the same. More focus also needs to be placed on acquired Long QT syndrome / arrhythmogenicity and channelopathies in future studies [ 33-38].

There has been a concept that the physician–case load ratio impacts directly on survival, as shown in urban dialysis centers, implying that the nephrologist with the lowest case load had lower mortality rates, offered higher dialysis doses, had been giving longer dialysis sessions and received more kidney transplants [39]. This needs to be looked into further.

Quantization of comorbidities and approximation to survival in hemodialysis studies [40] especially using modern computational models using Artificial Intelligence, are presently lacking in the field of nephrology and need to be studied in depth urgently.

About the issue of the set of patients who discontinued the treatment at their center, not much information is not available for their outcomes or even if they entered into another dialysis programme. It seems that about 10% of deaths on chronic hemodialysis programmes are preceded by stopping dialysis [41].

The NECOSAD study group-Netherlands co-operative study has developed a prognostic model tool that helps in picking up those dialysis patients who would be more likely to have poor health risk and combines survival, quality of life and morbidity [42].

The median year of survival of HD patients was 2.3 years and LVH, lesser haemoglobin levels, lower serum albumin and higher phosphorus were the independent, harmful prognostic factors identified. Screening for LVH, anemia, hypoalbuminemia and hyperphosphatemia amongst HD patients and managing them to prevent these bad prognostic factors has the potential to improve their survival. Future research on the predictors of sudden death, quantification of comorbidities and the utility of acquired long QT syndrome to improve survival are required.

PRK was responsible for developing the concept, planning, collecting the data, maintaining database, writing the paper, proofreading, revising and review of literature. VV was responsible for writing of the paper,analysis of the data,revising the paper and proofreading. AD was responsible for planning, data analysis, revising the paper and proof reading. PD was responsible for the planning, data analysis, revising the paper and proofreading of the paper.