Outcome and Determinants of Outcome of COVID-19 Infection amongst Hemodialysis Patients: Findings from a National Dialysis Network Program in India

Abstract

INTRODUCTION The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), the causative agent of the COVID-19 pandemic, has infected nearly 86 million people and caused more than 180,000 deaths worldwide. Patients with pre-existing conditions including those with CKD are at increased risk of adverse outcomes due to this infection. In the US, the risk of COVID-19 infection was 3.5 times greater among Medicare ESRD beneficiaries compared to all fee-for-service beneficiaries. Dialysis patients, already at high risk for a variety of complications, in particular cardiovascular disease, are also at an increased risk of adverse outcomes secondary to COVID-19 because of age, comorbidities like diabetes, hypertension and the need for multiple hospital contacts for dialysis. Most reports of COVID-19 in dialysis patients are from the USA and Europe, and the impact of the pandemic on dialysis patients in the developing world is lacking. 1, 2, 3, 4 Initial reports from India highlighted the large-scale hardships faced by the patient on dialysis inability to access treatment during the prolonged period of nationwide lockdown leading to missed treatments and dropouts. 5 , 6 In this article, we report the outcomes of patients diagnosed with COVID-19 in-center in a large hemodialysis network in India. RESULTS Out of a total 14,573 patients who received dialysis in the network centers during the entire study period, 1279 subjects were found to be positive for SARS-CoV 2. Table 1 describes the demographic characteristics and comorbidities for all subjects. The mean age was 53.63 years, and male patients were predominant (72.2%). Patients had been on dialysis for 590±725 days before diagnosis. The commonest comorbidities were hypertension in 39.85%, diabetes in 20.31%, and heart disease in 6.57%. Table 1 Description of COVID-19 positive hemodialysis subjects Total (N=1279) MalesN=923 (72.17%) FemalesN=356 (27.83%) Age (years)<3030-55>55 53.63±13.3067 (5.50)597(49.50)543 (45.00) 54.14±13.3045 (5.14)427 (48.74)404 (46.12) 52.27±13.2222 (6.60)170 (51.40)139 (42.00) Duration of hospital stay (days)Range 11.95±7.001-39 11.60±6.621-39 12.88±7.861-39 Dialysis vintage (Days)Range 590 ±7251-4032 569±7161-4032 648±7471-3554 Reason for COVID testing Symptom based 805 (62.94) 583 (63.16) 222 (62.36) Exposed in unit 86 (6.72) 67 (7.25) 19 (5.33) Contact in neighbourhood/home 20 (1.56) 15 (1.63) 5 (1.40) Travel history 4 (0.31) 4 (0.43) - Unknown 364 (28.46) 254 (27.52) 110 (30.90) Outcome Discharged 969 (75.76) 693 (75.08) 276 (77.53) Expired 293 (22.91) 219 (23.73) 74 (20.79) Treating at home 17 (1.33) 11 (1.19) 6 (1.69) Referred from another facility No 902 (70.52) 650 (70.42) 252 (70.79) Yes 377 (29.48) 273 (29.58) 104 (29.21) Payment type Out of pocket 210 (22.15) 150 (21.25) 60 (24.79) Public Insurance 677 (71.41) 514 (72.80) 163 (67.36) Private insurance 61 (6.43) 42 (5.95) 19 (7.85) Regular exercise 148 (12.63) 114 (13.43) 34 (10.53) Tobacco use* 128 (10.95) 118 (13.93) 10 (3.11) Hypertension 467 (39.85) 335 (39.46) 132 (40.87) Diabetes 238 (20.31) 176 (20.73) 62 (19.20) Dyslipidaemia 18 (1.54) 16 (1.88) 2 (0.62) Heart disease 77 (6.57) 58 (6.83) 19 (5.88) Data presented as Mean± Standard deviation and number (percentage) *Includes smoking as well as smokeless (chewable) tobacco The main indications for testing for SARS-CoV2 were the presence of symptoms in 805 (63%), and contact with SARS-CoV 2 positive patients in hospital in 86 (6.72%). A total of 377 (29.48%) patients were referred from another dialysis facility after receiving a COVID-19 diagnosis because the referring unit did not have the facility to dialyse these patients. A majority of the patients (1262, 98.67%) were hospitalized after being diagnosed with COVID-19. The duration of hospital stay was 11.95±7 days. The distribution of variables in subjects who survived and who expired are shown in table 2 . Of the COVID positive population, 293 (22.91%) expired. During the same time, there were 2560 deaths amongst the 13,294 COVID-19 negative population in the network, giving a mortality rate of 19.26%. In comparison, this death rate during the same period in the previous year (2019) was 15%. Table 2 Variables among Covid-19 positive patients who died and survived DiedN = 293 (22.91) SurvivedN = 986 (77.09) Sex FemaleMale 74 (25.26) 282 (28.60) 219 (74.74) 704 (71.40) Referred from another facility NoYes 239 (81.57) 663 (67.24) 54 (18.43) 323 (32.76) Smoker/tobacco user 24 (8.70) 104 (11.65) Exercise 36 (12.90) 112 (12.54) Diabetes 75 (26.88) 163 (18.25) Dyslipidemia 6 (2.15) 12 (1.34) Heart disease 27 (9.68) 50 (5.60) Hypertension 140 (50.18) 327 (36.62) Age (years) 56.51±12.74 52.73±13.35 Dialysis duration (days)Range 786±8261-3481 531±6811-4032 Length of hospitalization (days)Range 8.33±7.101-35 13.1±6.521-39 Data presented as number (percentage) and mean± standard deviation Compared to those who survived the illness, the COVID-19 positive patients who died were older (age 56.51±12.74 vs 52.73±13.35 years, p <0.001), and had longer dialysis vintage (786±826 vs 531±681 days, p<0.001). Mortality in subjects over 55 years was >3 fold higher as compared to subjects <30 years (p=0.014). Diabetes (OR 1.65, 95% confidence interval [CI] 1.20-2.25, p =0.002), hypertension (OR 1.74, 95% CI 1.33-2.29 p<0.001,), heart disease (OR 1.81, 95% CI 1.11-2.95 p=0.018), older age (OR 1.02, 95% CI: 1.01-1.03, p<0.001) and dialysis vintage (OR 1.20, 95% CI: 1.13-1.29, p<0.001) were significantly associated with mortality (Table 3 ). Those who were referred from other dialysis facility had a lower mortality (OR 0.46, 95% CI: 0.34-0.64, p<0.001). After adjusting for other factors, only older age (OR=1.02, 95% CI 1.01-1.03, p <0.001) retained significant association with mortality (Table 3). Table 3 Logistic regression analysis showing association of death with clinical variables Unadjusted Adjusted Variables OR (95% CI) P value OR (95% CI) P value Male sex 1.19 (0.88, 1.60) 0.262 1.22 (0.88,1.69) 0.238 Tobacco use 0.72 (0.45, 1.15) 0.172 0.73 (0.42,1.26) 0.263 Exercise 1.03 (0.69, 1.54) 0.874 0.85 (0.55,1.30) 0.443 Diabetes 1.65 (1.20, 2.25) 0.002 1.00 (0.68, 1.45) 0.979 Dyslipidemia 1.61 (0.60, 4.34) 0.343 0.82 (0.27, 2.52) 0.729 Heart disease 1.81 (1.11, 2.95) 0.018 1.37 (0.79, 2.36) 0.261 Hypertension 1.74 (1.33, 2.29) <0.001 1.28 (0.89, 1.83) 0.179 Age (years) 1.02 (1.01, 1.03) <0.001 1.02 (1.01, 1.03) <0.001 Dialysis duration* (days) 1.20 (1.13, 1.29) <0.001 1.07 (0.97, 1.18) 0.199 Referred from another facility 0.46 (0.34,0.64) <0.001 0.72 (0.45,1.15) 0.172 * Duration of dialysis covariate is log transformed for analysis purpose DISCUSSION This is the first systematic report of the impact of COVID-19 on the outcomes of patients on in-centre hemodialysis from the developing world. In the absence of a national dialysis registry, this analysis from a large cohort comes closest to a nationwide representation of the health effects of this pandemic in India. We found that the prevalence of infection was 20-fold greater in this population compared to that reported in the general population in India during this period (8.7% and 0.44% respectively). This is greater than that described in the REIN registry data from France (3.3% v 0.2%). 1 In addition to the increase in risk due to repeated contact with the health care system, the higher prevalence could also be contributed by opportunities for more frequent screening and testing. The male predominance likely reflects the male dominance in the general dialysis population. An overwhelming proportion (99%) of the COVID-positive patients in this cohort were admitted to hospitals in compliance with local policies. The COVID-19 surge in India followed those in China, Western Europe and North America. This allowed the Indian centers to rapidly adopt the best practices implemented in dialysis centers in those parts of the world. There were some unique challenges, however, related to the Indian healthcare system, such as the closure of units in certain hospitals that were converted into COVID hospitals and other centers turning COVID positive patients away because of lack of resources that produced additional hardships for dialysis patients in India. 5 , 6 About 30% of COVID-19 positive patients had been referred from other dialysis centers. About a quarter of all COVID-positive dialysis patients died. This mortality rate is comparable to that reported by other studies on hemodialysis patients from high-income countries, despite the Indian dialysis population being younger and having a lower prevalence of comorbidities. The mortality was indeed much greater than the COVID-19 case fatality rate amongst the general population of India (1.45%). However, the mortality rate among non-COVID patients during the study period in the network was 19.26%, which suggests that the excess mortality in the COVID-positive population was just about 3.7%. The mortality in the NephroPlus dialysis cohort during the corresponding period in 2019 was 15%, suggesting that the impact of COVID-19 during the study period was not limited to those who were infected with the virus. Our finding confirms the high mortality reported amongst dialysis patients in general during the pandemic, 5 , 7 attributable due to other factors related to the pandemic or the lockdown like difficulty in transport, closure of dialysis facilities, reduced dialysis frequency, decreased inpatient and outpatient care, and financial difficulties especially. As expected, elderly males, those with diabetes, hypertension, pre-existing heart disease, and those with longer dialysis vintage were at increased risk of mortality. The mortality risk factors are similar to those reported in other studies amongst the dialysis population and general population studies from India. The strength of our study includes nationwide coverage with a large population base who were screened using a uniform protocol, and the completeness of outcome data. We show that despite relatively limited resources, it was possible to implement COVID protocols in dialysis units. This is important because in-center dialysis is the overwhelming dialysis modality during COVID-19 in India, with very low penetration of home dialysis and an almost complete stoppage of transplantation. 8 There are some limitations, however. Though the network had a uniform temperature and symptom screening protocol prior to unit entry, the implementation might have differed based on local practice adherence. The absence of universal screening might have led to missing out of asymptomatic individuals and an overestimation of case fatality rates. The protocols for screening during the study period were constrained by government directives, local preferences, access to testing and self-reporting of symptoms. A small study of COVID dialysis from Mumbai had shown that more than 50% of patients were asymptomatic or had mild disease. 7 Finally, we did not have data on the severity of COVID-19 infection and treatment protocols in the individuals. In view of the ongoing surges, the threat by the pandemic will remain significant for this vulnerable population. This can be minimized by liberal testing protocols and persisting with steps intended to minimize disease transmission. COVID vaccination program started in India on January 16, 2021. Appropriately, the healthcare workers and the elderly are being prioritized. Given the high risk of death, especially amongst the younger population on dialysis, experts have called for prioritization of dialysis patients for vaccination before other high-risk groups such as the obese and smokers and those with heart disease and obesity. 9 It is worth pointing out that a significant proportion of tobacco users use chewing tobacco, which does not cause lung injury to the same extent as smoking. To conclude, our study confirms that the in-center dialysis population has a high risk of acquiring COVID-19 infection and has poor outcomes once infected. Our study reinforces the need to implement strict measures targeting personal protection as well as the need to find evidence-based approaches to prevent the development of COVID-19 in this high-risk population.