COVID-19 Pandemic Effect on Mortality of Hemodialysis Patients

Background: COVID-19 was associated with significant excess mortality among dialysis patients. We aimed to assess mortality risk of patients with confirmed COVID-19 infection (COVID-19+) versus other hemodialysis (HD) patients, and its relation to COVID-19 pandemic in general population (GP). Method(s): We included 63,216 HD patients treated in 2019-2020 at NephroCare centers of 23 countries from European Clinical Database (EuCliD). Mortality risk per calendar month in 2020 was estimated separately for COVID-19+ and other HD patients using Cox regression models, with COVID-19 status as a time-varying covariate and patients per month in 2019 as reference. The correlation between monthly mortality risk and numbers of COVID-19 cases and deaths in GP were evaluated. Result(s): Monthly treated patients were 42,000-43,000 (Fig1). In line with two waves of pandemic in GP, two fluctuations of mortality risk were observed for both COVID-19+ and other HD patients (Fig1). Mortality risk of COVID-19+ patients persisted at much higher levels across 2020, with adjusted hazard ratios (HR)>6.5, whereas mortality risk of other HD patients elevated slightly (HRs<1.5) and mainly during the pandemic peak period (Fig1). Correlation of mortality risk with pandemic in GP were higher for other HD patients (spearman correlation coefficients [rho] of HRs with the numbers of COVID-19 cases/deaths in GP, 0.77/0.44) than for COVID-19+ HD patients (rho, -0.10/0.42). Conclusion(s): COVID-19 pandemic had direct and indirect impact on mortality of HD patients. Potential reasons of increased mortality in patients without confirmed COVID-19 diagnosis could be undertesting or healthcare system capacity constraints. Quantifying the magnitude of pandemics on patients with/without confirmed disease may benefit dialysis clinics to manage patients during critical events. (Figure Presented).

EFFECTIVENESS OF COVID-19 VACCINES IN A LARGE EUROPEAN HAEMODIALYSIS COHORT

BACKGROUND AND AIMS: To date, no large-scale study has evaluated the effectiveness of COVID-19 vaccines in hemodialysis patients. We sought to evaluate the effectiveness of vaccines against SARS-CoV-2 infections and death in haemodialysis patients registered in the Fresenius Medical Care (FMC) Nephrocare network. METHOD: In this historical, 1:1 matched cohort study, we analysed electronic health records (EHR) of individuals receiving in-center haemodialysis therapy in FMC European dialysis clinics from 1 December 2020, to 31 May 2021 (study period). For each vaccinated patient, an unvaccinated patient was selected among patients registered in the same country and attending a dialysis session within +/-3 days from the vaccination date. Matching without replacement was based on demographics, clinical characteristics, past COVID-19 infections and a risk score representing the local (dialysis centre) background risk of infection at each vaccination date. The infection risk score was calculated from an artificial Intelligence model predicting the risk of COVID-19 outbreak in each clinic over a 2-week prediction horizon. The infection risk score was based on trends in regional COVID-19 epidemic metrics, FMC COVID-19 reporting system and clinical practice patterns. The index date was the date of the first vaccination for the vaccinated and the matching treatment date for the unvaccinated controls. To overcome violation of the proportional hazard assumption, we estimated the effectiveness of the COVID-19 vaccines in preventing infection and mortality rates as 1-hazard ratio estimated from a time-dependent extended Cox regression stratified by country and vaccine type. RESULTS: We included 44 458 patients, 22 229 vaccinated and matched 22 229 unvaccinated. Distribution of covariates was balanced across study arms after matching (Figure 1A). In the effectiveness analysis on mRNA vaccines, we observed 850 SARS-CoV-2 infections and 201 COVID19-related deaths among the 28 110 patients (14 055 vaccinated and 14 055 unvaccinated) during a mean follow up time of 44 ± 40 days. In the effectiveness analysis of viral-vector vaccines, we observed 297 SARS-CoV-2 infections and 64 COVID19-related deaths among 12 888 patients (6444 vaccinated and 6444 unvaccinated) during a mean a follow-up time of 48 ± 32 days (Figure 1B). We observed 18.5/100 patient-year and 8.5/100 patient-year fewer infections and 5.4/100 patient-year and 5.2/100 patient-year fewer COVID-19-related deaths among patients vaccinated with mRNA and viral-vector vaccines respectively, as compared to matched unvaccinated controls. The effectiveness of COVID-19 vaccines concerning both symptomatic infections and COVID-related death along the follow up period is shown in Figure 2. CONCLUSION: In this matched, historical cohort study, we observed a strong reduction in both SARS-CoV-2 symptomatic infection and COVID-19-related death among dialysis patients receiving an mRNA vaccine. Despite seemingly less protective against symptomatic infections, we observed similar reduction in COVID-19 mortality rate among patients receiving a viral-carrier vaccine. (Figure Presented).

ASSOCIATION BETWEEN IMMUNE SYSTEM FUNCTION AS DETERMINED BY HEPATITIS B INFECTION/VACCINATION STATUS AND RISK OF COVID-19 INFECTION

BACKGROUND AND AIMS: Since the beginning of the COVID-19 pandemic in early 2020, >290 million people were infected by SARS-CoV-2 and >5.4 million have died from or with COVID-19 (https://coronavirus.jhu.edu/). Patients with chronic health conditions such as end-stage kidney disease (ESKD) experience particularly high morbidity and mortality because of COVID-19. ESKD patients on hemodialysis are widely vaccinated for hepatitis B (HBV) and seroconversion is routinely measured. This practice presents a rare opportunity to study immune function on a wide scale. It can be reasonably assumed that patients who are able to produce a vaccinal or post-HBV antibodies titers have a better immune function than those who are unable to mount such a serological response. We aim to jointly analyze results of SARS-CoV-2 RT-PCR and hepatitis B serology to determine if presence of vaccinal or post-HBV antibodies is associated with likelihood of developing COVID-19 infection. METHOD: Patients who were tested for COVID-19 at Fresenius Medical Care North America dialysis clinics from May 2020 to September 2020 were included in this analysis. HBV infection/vaccination status, demographic parameters and clinical parameters were obtained from the medical record. Nasopharyngeal swab specimen was tested via RT-PCR to detect presence of SARS-CoV-2. Patients were categorized as having good immune function or poor immune function based on vaccinal and post-HBV sero-status. Patients who were vaccinated against HBV but did not seroconvert were considered to have poor immune function. On the other hand, patients who mounted vaccinal or post-HBV antibodies were considered to have good immune function. Univariate and multivariate logistic regression were utilized to study the association between immune function and other demographic, anthropometric and clinical parameters on the likelihood of not being diagnosed with COVID-19. Four models were constructed: Model 1: unadjusted;Model 2: adjusted for age. Model 3: adjusted for age, gender, race, ethnicity, body mass index (BMI). Model 4: adjusted for parameters in model 3 and dialysis vintage (in years), diabetes and congestive heart failure (CHF). RESULTS: 11 870 patients were included in this analysis. 54% patients were male, 33% were Black, 24% of the patients were Hispanic, 69% had diabetes and 22% had CHF. Patients were 61.2 ± 14.4 years old with dialysis vintage of 3.9 ± 3.9 years, BMI of 29.6 ± 9.7 kg/m2 and eKt/V 1.5 ± 0.3. Of these patients, 21% had poor immune function and 79% had good immune function. Results of the logistic regression models are shown in Table 1. In the unadjusted model, poor immune function was associated with an increased likelihood of being diagnosed with COVID-19. In models, 2, 3 and 4 age, vintage and presence of diabetes were all significantly associated with a higher likelihood of being diagnosed with COVID-19. However, poor immune function was not a significant predictor of COVID-19 diagnosis in the adjusted models. CONCLUSION: Patients who have vaccinal or post-HBV antibodies did not have a lower likelihood of COVID-19 compared with patients who were unable to mount an adequate vaccinal or post-HBV antibody response. Response to HBV vaccination or infection may not be adequate to characterize a patient as having good immune response. Other factors that are routinely measured in hemodialysis patients, which may allow us to make inferences about a patient's immune function should be explored.

MODIFIABLE AND NON-MODIFIABLE FACTORS ASSOCIATED WITH INCREASED COVID-19 RELATED MORTALITY AMONG DIALYSIS PATIENTS

BACKGROUND AND AIMS: Patients with end-stage kidney disease (ESKD) face higher risk for severe outcomes from COVID-19 infection. Moreover, it is not well known to which extent potentially modifiable risk factors contribute to mortality risk. In this study, we investigated the incidence and risk factors for 30-day case-fatality of COVID-19 in haemodialysis patients treated in the European Fresenius Medical Care (FMC) Nephrocare network. METHOD: In this historical cohort study, we included unvaccinated adult dialysis patients with a first documented SARS-CoV-2 infection between 1 February 2020 and 31 March 2021 (study period) registered in the European Clinical Database (EuCliD ® ). The first SARS-CoV-2 suspicion date for all documented infections was considered the index date for the analysis. Patients were followed for up to 30 days. Follow-up time was defined from the index date until the date of death, end of follow-up period or lost to follow-up, whichever occurred first. We ascertained patients' characteristics in the 6-month period prior to index date. We used logistic regression and XGBoost to assess risk factors for 30-day mortality. RESULTS: We included 9211 patients meeting the inclusion criteria for the study (Table 1). Age was 65.4 ± 13.7 years, dialysis vintage was 4.2 ± 3.7 years. In the follow up period, 1912 patients died within 30 days (20.8%, 95% confidence interval: 19.9%- 21.6%). Correlates of COVID-19 related mortality are summarized in Table 2. Several potentially modifiable factors were associated with increased risk of death: patients on HD compared with online haemodiafiltration had shorter survival after presentation with COVID-19 as well as those who did not achieve the therapeutic targets for serum albumin, erythropoietin resistance index, protein catabolic rate, haemodynamic status, C-reactive protein, single-pool Kt/V, hydration status and serum sodium in the months before infection. The discrimination accuracy of prediction models developed with XGBoost was similar to that observed for main-effect logistic regression (AUC 0.69 and 0.71, respectively) suggesting that no major cross-interaction and non-linear effect could improve prediction accuracy. CONCLUSION: We observed high 30-day COVID-19 related mortality among unvaccinated dialysis patients. Older patients, men and those with greater.

Analysis of the COVID-19 pandemic in home and in-center dialysis populations

Background: While dialysis patients have a high risk of complications from COVID-19, in-center hemodialysis (ICHD) patients show lower SARS-CoV-2 reproduction rates when compared to the general population (Cherif, JASN 2021), possibly related to lifestyle and interventions to prevent SARS-CoV-2 spread. Here we expand the research to study the prevalence of COVID-19 in both home (PD/HHD) and ICHD patients. Methods: We analyzed COVID-19 cases in PD/HHD and ICHD patients from the U.S. Fresenius Kidney Care (FKC) network, from March 1 to November 27, 2020. Patients were tested for SARS-CoV-2 (confirmed by RT-PCR) when showing signs compatible with COVID-19 or exposed to an infected person. We perform statistical analysis for inter-/intra-modalities, with continuous and categorical variables being expressed as the mean (standard deviation) and absolute (relative, %), respectively. Results: We studied 263,223 patients (age 62.8±14.5 years, 57.7% males) receiving dialysis in the FKC network (87.3% ICHD;12.7% PD/HHD). In the FKC network, 21,175 (8.05%) were infected with SARS-CoV-2. COVID-19 infection was more prevalent among ICHD (8.56%) vs. PD/HHD (4.49%) patients. Black had a significantly higher risk than other races for both ICHD (9.10%, p < 0.0001) and PD/HHD (5.13%, p = 0.0038), without a difference between modalities (p = 0.1827). While white ICHD patients did not have a different risk compared to others (8.52%, p = 0.4105), they had a smaller risk when dialyzed at home PD/HHD (3.82%, p < 0.0001), and the difference between ICHD and PD/HHD was significant (p < 0.0001). Similar results are shown for other patients (Tab.1). Conclusions: COVID-19 infection was more common among ICHD patients. To what extent this is related to lifestyle?, travel to dialysis facilities or other aspects warrants further analyses.

Trajectories of clinical and laboratory characteristics before COVID-19 diagnosis in hemodialysis patients

Background: The frequency of evaluations in hemodialysis (HD) care affords opportunities to assess profiles that may characterize onset of the 2019 coronavirus disease (COVID-19). We aimed to characterize the trajectories of clinical/laboratory assessments before COVID-19 diagnosis in HD patients. Methods: We assessed data from HD patients with known COVID-19 dialyzed at Fresenius Kidney Care in the United States between 02 Mar and 09 Apr 2020. We computed mean daily values for 40 variables 90 days before a positive rRT-PCR test (COVID-19+). Nonparametric smoothing splines were used to fit data of individual trajectories and estimate the mean change over time. Results: There were 1294 HD patients with COVID-19 (mean age 64±14 years, 60% male, 47% white race, 69% had diabetes, and 24% had coronary artery disease). Mean pre- HD body temperature (primarily oral) increased by about 1° Fahrenheit (F) over 10 days before COVID-19+ test and approached 99° F at diagnosis (Fig 1A). Mean interdialytic weight gain decreased by about 0.75 kg (Fig 1B) over 14 days before COVID-19+ test;concurrent decreases of about 20 minutes were seen in HD treatment time. Mean neutrophil-to-lymphocyte ratio had mild increases (Fig 1C), while mean platelet counts decreased by about 40×109/L over 14 days before COVID-19+ test (Fig 1D). Trajectories of many variables (vitals, heparin, hematology, nutrition, bone, anemia) were observed to change before COVID-19+ test, yet alternations were generally minor. Conclusions: The trajectories of several clinical/laboratory parameters appeared to change before COVID-19 diagnosis in HD patients. Many changes were small and may not be independently useful in identifying onset of COVID-19. Mean pre-HD body temperature before SARS-CoV-2 infection was 97.4° F and should be considered in screening. Findings may have utility in prediction model development. Further comparisons to patients without COVID-19 are needed.

Trends in fever and respiratory illness in hemodialysis patients during the COVID-19 pandemic

Background: Hemodialysis (HD) patients are vulnerable to the 2019 coronavirus disease (COVID-19) due to older age and common coexistence of comorbidities. Fever and respiratory illness (RI) are common symptoms of COVID-19. In order to create a disease surveillance tool and anticipate areas of COVID-19 outbreak, we aimed to assess the trends in fever and RI symptoms in HD patients treated at a national dialysis provider network in the United States during the pandemic. Methods: We used data from HD patients actively treated between Jan 1 2018 ad May 16 2020 at a national dialysis provider network of large integrated health care company. If the body temperature of the patient either before or after the treatment was greater than 100 degrees Fahrenheit, then the patient was identified as exhibiting the symptom of fever. If the patient complained of shortness of breath, wheezing, runny nose, bloody cough, dry cough or purulent cough, then in this analysis the patient was identified as exhibiting the symptom of RI. Results: The total patients count ranged from 196,774 to 209,475 per week while the total count of HD treatments ranged from 413,477 to 454,215. For the year 2020, a clear increase in trend for number of patients was observed after week 11 (03/08-03/14/2020) for RI symptoms (Figure 1A) and week 12 (3/15-3/21/2020) for fever symptom (Figure 1B). Both increasing trends spike at the week 15 (04/05-04/11/2020) and decline thereafter. Conclusions: HD patients appear to exhibit a different trend in RI and fever symptoms during the year 2020 compared to concurrent periods in 2018 and 2019. which coincides with COVID-19 outbreak. Routine surveillance of dialysis patients may allow for early identification of COVID-19 outbreaks.

Enhanced sentinel surveillance system for COVID-19 outbreak prediction in a European dialysis clinics network

Background: Accurate predictions of epidemic dynamics may enable timely organizational interventions in high risk regions. We exploited the interconnection of the EMEA Fresenius Medical Care (FMC) dialysis clinic network to establish a sentinel surveillance system where the occurrence of new cases in a clinic propagates distanceweighted risk estimates to proximal dialysis units. The surveillance system is embedded in an artificial intelligence model which predicts COVID-19 outbreak occurrence in HD clinics from trends in clinical practice patterns and regional COVID-19 epidemic metrics. The system stratifies clinics by their risk of new local outbreak. Methods: The risk prediction model is computed considering a cohort of 640 clinics belonging to the FMC network. We trained a model to predict outbreak in each clinic in a 2-week prediction horizon (i.e. two or more COVID-19 cases). In addition to sentinel distance-weighted risk estimates, the model included 73 variables (i.e. regional-level epidemic data from open source datasets and clinical practice data from the EuCliD® database). We generated the training set on data available on 04/01/2020 and tested prediction accuracy at 4/15/2020 and 4/20/2020. Results: In the training set there were 58 (9.1%) clinics with two or more patients with COVID-19 infection in the two-week prediction window. In the validation samples there were 27 (4.2%) and 12 (1.9%) clinics with two or more patients with COVID-19 infection during the two-week prediction window. The performance of the model was suitable in both testing windows (AUC=0.86 and 0.80 respectively). The model is used to construct risk maps highlighting geographical clusters of clinics at risk (figure). Conclusions: A sentinel surveillance system together with the wealth of information collected in EuCliD® and state of the art modeling strategies allows prompt risk assessment and timely response to COVID-19 epidemic challenges throughout networked European clinics.