Risk factors for mortality and antibody responses in chronic hemodialysis patients with coronavirus disease 2019: a single-center experience in China

Risk factors for mortality and antibody responses in chronic hemodialysis patients with coronavirus disease 2019: a single-center experience in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Risk factors for mortality and antibody responses in chronic hemodialysis patients with coronavirus disease 2019: a single-center experience in China Yuhuan Song, Guangyan Cai, Yuefei Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4428998/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : This single-center retrospective research aimed to depict the outcomes of coronavirus disease 2019 (COVID-19) in a cohort of patients from China. Methods : We traced the outcomes of 216 MHD patients admitted to the Aerospace Center Hospital of China during a COVID-19 wave . Clinical information was assembled and compared between survivors and non-survivors. Serum immunoglobulin M (IgM) and IgG antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were detected in MHD survivors 90–120 days post-infection. Clinical information was analyzed to find outfactors influencing mortality and antibody responses. Results : Among 216 patients, 207 (95.8%) were evaluated as COVID-19, and 25 (12.1%) passed away within 90 days. Fifty-five (26.6%) patients were needed of hospital admission. Non-survivors had lower levels of hemoglobin and serum albumin, higher levels of alkaline phosphatase, higher white blood cell counts and lower percentage of lymphocytes than survivors. Furthermore, the Clinical Frailty Scale (CFS) scores were higher in non-survivors significantly (p<0.05). Multivariate analysis showed that diabetes (HR 3.98, 95% CI 1.25–12.66, p=0.019), level of frailty according to the CFS (HR 2.10, 95% CI 1.36-3.23, p=0.001) and central venous catheter (CVC) use (hazard ratio [HR] 3.48, 95% confidence interval [CI] 1.18-10.29, p=0.024), had significant impacts on mortality. IgG was positive in 59.5%, and IgM was positive in 3.3% of patients (>1 sample/cutoff) between 90–120 days post-infection. Lower blood C-reactive protein (CRP) levels before the onset of SARS-CoV-2 infection were associated with significantly higher IgG antibody levels at 90-120 days after COVID-19 infection. Discussion : This study identified high mortality rates of SARS-CoV-2 infection among hemodialysis patients. Risk factors associated with mortality include diabetes, frailty, and CVC access. Low level of blood CRP before SARS-CoV-2 infection may predict high antibody responses during convalescence. Biological sciences/Immunology Health sciences/Diseases hemodialysis mortality COVID-19 antibody responses chronic kidney disease Figures Figure 1 Figure 2 Figure 3 1. Introduction Since December 7, 2022, adjustments in the prevention and control policies have increased the number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in mainland China (1). The emergence of this pandemic has disproportionately affected people with chronic diseases, such as chronic kidney disease (CKD), particularly CKD requiring maintenance hemodialysis (MHD) (2). Despite protective measures, patients undergoing dialysis are at high risk of contracting coronavirus disease 2019 (COVID-19). These patients have inferior immune responses compared with those of the healthy population. Dialysis is generally administered in large universal and crowded spaces (3). The first reported case of COVID-19 occurred at the dialysis center of the Aerospace Center Hospital on December 6, 2022. Currently, the factors associated with COVID-19 mortality and naturally acquired immunity in patients undergoing hemodialysis are largely unexplored. Variations in the antibody responses to natural infections remain unclear and unpredictable. Therefore, the aim of this study was to determine the factors affecting mortality and levels of specific serum immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against SARS-CoV-2 in dialysis patients with COVID-19 in China. 2. Materials and methods All procedures involving human participants followed the Declaration of Helsinki and its later amendments. Ethics approval for this study was obtained from the Research and Ethics Committee of Aerospace Center Hospital. All the included patients signed informed consent for the release of clinical data for publication. A total of 216 patients who had been on hemodialysis (4 h, 3 times weekly) for more than 3 months at the Aerospace Center Hospital of China as of December 2022 were included. Screening of patients prior to entering the dialysis center was conducted in an approved laboratory using reverse transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 infection. Surveillance revealed that the first new case of COVID-19 occurred on December 6, 2022. Patients with positive RT-PCR results were diagnosed with COVID-19 and underwent dialysis in segregation. The inclusion criterion included patients who had been undergoing hemodialysis for >3 months and were positive for SARS-CoV-2. The exclusion criteria were transfer to other dialysis centers, completion of one or more doses of COVID-19 vaccine(s) before SARS-CoV-2 infection, and kidney transplantation or incomplete laboratory results. The following outcomes were investigated: (1) SARS-CoV-2 infection between December 6, 2022 and April 6, 2023 and (2) 90-day mortality among infected patients. For patients with SARS-CoV-2, the following variables were also investigated: (3) hospitalization (yes, no) and (4) immune response to immunoglobulin G (IgG) and IgM 90-120 days after infection. During the study period, antiviral drugs were not administered to patients who had symptoms but did not require hospitalization. Rather, these patients were administered symptomatic supportive treatments such as antipyretic and oral anti-inflammatory drugs. We collected the clinical data of the hemodialysis patients on December 1, 2022, approximately one week before the first confirmed COVID-19 infection in the dialysis center, rather than collecting the data after confirmed SARS-CoV-2 infection. Blood samples were routinely obtained before the second hemodialysis session. Background data, such as age, sex, dialysis duration, and smoking status, were recorded. In addition, data on comorbidities, such as diabetes mellitus, hypertension, cardiovascular disease, and malignancy, were collected. The Clinical Frailty Scale (CFS) was used to determine the patient level of frailty prior to COVID-19 infection According to this scale, a score of 1 indicates that the patient is “very fit” in that they are “robust, active, energetic, and motivated.” A score of 9 categorizes a patient who is “terminally ill” (4, 5). Finally, all relevant laboratory test results were recorded. To investigate the potential influence of nutritional status on patient outcome, we estimated the trends in dry weight (DW) and laboratory parameters in the trajectories of MHD patients with RT-PCR-certified COVID-19 who survived for approximately 30 days after the diagnosis of SARS-CoV-2. DWs were adjusted according to the body composition monitor (BCM) output data following the DW adjustment strategy. Patients in the survival group underwent BCM measurements at the beginning and approximately 30 days after diagnosis of SARS-CoV-2 infection. The body composition and hydration state were estimated using a portable whole-body bioimpedance spectroscopy device (Fresenius Medical Care, Germany). RT-PCR was performed using a reverse transcription quantitative PCR method according to the manufacturer’s protocol (Bojie Co. Ltd, Shanghai, China). The levels of specific serum IgM and IgG antibodies against SARS-CoV-2 (Autobio Co. Ltd., Zhengzhou, China) were measured in surviving MHD patients 90-120 days after infection. The IgG and IgM indices were considered positive if they were >1.0 sample/cutoff (s/co). Statistical Analysis SPSS (version 22.0; SPSS Inc., Chicago, IL, USA) was used for statistical analyses. All data are presented as mean ± standard deviation (SD); non-normally distributed variables are presented as median and interquartile range (IQR). Categorical variables are expressed as n (%). Continuous variable differences in outcomes were analyzed using one-way analysis of variance (ANOVA), whereas categorical variables were analyzed using the chi-square test. To determine the relative risk of the possible predictor variables, odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using logistic regression analysis. Clinical indices that were significant in the univariate analysis (p<0.05) were identified as potential predictor indices and analyzed using multivariate analysis. A forward stepwise (conditional) model was constructed using a multivariate analysis that incorporated binary logistic regression. 3. Results A flow diagram of the patients who were infected with COVID-19 is shown in Figure 1. A total of 216 patients were on dialysis for more than 3 months at the Aerospace Center Hospital of China during the study period. COVID-19 was diagnosed in 207 patients, representing a positivity rate of 95.8%. Nine patients who were COVID-19 negative have survived to date, including one patient who required hospitalization for myocardial ischemia. Of the 207 COVID-19 positive patients, 55 (26.6%) patients with SARS-CoV-2 presented with clinical worsening and required hospitalization. The three most common causes of hospitalization were respiratory infections, cardiac diseases, and gastrointestinal disorders. These complications were all attributed to COVID-19. Of the 55 hospitalized patients, 25 COVID-19 positive patients died within 90 days. The cause of death was COVID-19 infection. Twenty-three out of the 25 patients died within 28 days of follow-up. None of the 25 patients who died had received the SARS-CoV-2 vaccination. Among the 182 surviving patients, 39 patients without complete laboratory results, six patients who were transferred to other dialysis centers, and three patients who underwent kidney transplantation were excluded. Patients with complete data (n=134) were included in the survival group. Table 1 shows a comparison of the clinical data between survivors and non-survivors before the onset of SARS-CoV-2 infections. The mean hemoglobin was 105.48±21.7 g/L in non-survivors, which was significantly lower than that in the survivors (110.80±15.92 g/L) (p=0.003). Albumin level in non-survivors was also significantly lower than that in survivors (35.04±4.82 g/L vs. 38.01±3.21 g/L, respectively; p=0.007). The serum alkaline phosphatase level in non-survivors (83.72±32.84 mmol/L) was significantly higher than that in survivors (75.08±23.04 mmol/L; p=0.008). White blood cell count in non-survivors was 7.09±3.66×10 9 /L, which is significantly higher than that in survivors (6.24±1.61×10 9 /L; p=0.011). The percentage of lymphocytes in non-survivors was 13.77±4.73%, which was significantly lower than that in survivors (17.00±6.68%; p=0.023). The CFS was 5.44±1.04 arbitrary units (AU) in non-survivors and 3.71±1.00 AU in survivors (p=0.000). No significant differences were found in age, dialysis duration, phosphorus level, intact parathyroid hormone level, serum lactate dehydrogenase level, Kt/V (K is the dialyzer clearance of urea, t is the dialysis time, and V is the volume of urea distribution), or other laboratory parameters (p>0.05). In addition, we compared the effects of age, sex, and comorbidities on mortality incidence in our analysis. Mortality was higher among patients with diabetes mellitus (p=0.009) and those receiving dialysis administered through a CVC than among patients with arteriovenous fistulas (p=0.001). No differences were observed between the survivors and non-survivors in terms of age, sex, smoking status, and comorbidity in incidences of hypertension, ischemic heart disease, or malignancy (p>0.05). Univariate logistic regression analysis showed that hemoglobin levels and white blood cell counts had no significant effects on mortality (p>0.05) (Table 2). CVC access, diabetes mellitus, frailty as determined using the CFS,low percentage of lymphocyte, and low serum albumin levels were independent risk factors for mortality (p<0.05). Using multivariate logistic analysis and adjusting the results of independent variables significant in the univariate analysis, three different models were created and applied to the multivariate logistic regression forward elimination analysis (Table 3). In model 1, diabetes mellitus and frailty were significant predictors of mortality. In model 2, after removing lower albumin levels, diabetes mellitus, CVC access, and CFS were significant predictors of mortality. In model 3, upon eliminating the lymphocyte percentage from the list, CVC use (HR 3.48, 95% CI 1.18-10.29, p=0.024), diabetes (HR 3.98, 95% CI 1.25-12.66, p=0.019), and CFS (frailty) (HR 2.10, 95% CI 1.36-3.23, p=0.001) predicted higher mortality. A comparison of the DW and blood test data of the survivors before COVID-19 and 30 days after infection is shown in Table 4. Hemoglobin, albumin, and serum phosphorus levels were lower, whereas intact parathyroid hormone (iPTH) levels were higher in survivors at 30 days post-infection (p<0.05). DW and serum calcium levels remained unchanged before and after COVID-19. IgG and IgM antibodies against SARS-CoV-2 were tested in 121 surviving patients from 90-120 days after COVID-19 infection. The remaining 13 patients refused to undergo antibody testing for SARS-CoV-2. The s/co index for IgG ranged from 0.03 to 417.16 (mean 15.91) and that for IgM ranged from 0.02 to 52.01 (mean 0.57). The distribution of IgG antibody levels in the 121 surviving patients is shown in Figure 2. This analysis included 40.4% of patients who had an IgG antibody titer of 100 s/co. The semi-quantitative results of SARS-CoV-2 antibodies in the MHD patients revealed that 4/121 (3.3%) patients were positive for IgM, and 72/121 (59.5%) patients were positive for IgG. According to the data collected to determine the factors affecting the antibody response in the patients from 90–120 days after infection, there were no statistically significant differences in antibody levels according to sex, body mass index (BMI), smoking status, diabetes mellitus, and hospital stay (p>0.05). Blood CRP levels in the IgG (+) group were significantly lower than those in the IgG (-) group (p<0.05) (Table 5). Low blood CRP levels before the onset of SARS-CoV-2 infection were similarly associated with high IgG levels at 90-120 days post-infection, as shown in Figure 3. 4. Discussion Worldwide, the COVID-19 pandemic has disrupted every facet of life, including health service delivery. This is particularly true for people living with chronic diseases who require consistent monitoring of care needs and medication by health professionals to maintain and enhance their health ( 6 ) . Patients undergoing dialysis are categorized as having a high risk of SARS-CoV-2 infection/transmission and are more vulnerable to unfavorable clinical outcomes. Previous studies have shown that the Omicron COVID-19 threat to patients undergoing MHD is lower than that of previous variants (7); however, the present study revealed a higher mortality rate among MHD patients after COVID-19 infection than that of the general population (12.1% and 1.45% to 8%, respectively) (8-10). Yavuz et al. reported that approximately 49.5% of patients on hemodialysis and peritoneal dialysis with SARS-CoV-2 infection required hospitalization (11). The current study showed that approximately 26.6% of COVID-positive MHD patients required hospitalization, demonstrating the need for sufficient resource planning in in-hospital dialysis facilities. In addition, our study showed a mortality rate of 12.1% among unvaccinated patients. Hemodialysis patients have a high rate of hesitancy regarding COVID-19 primary vaccination (12). A previous study revealed lower mortality rates in fully and partially vaccinated individuals (5.3% and 8.3%, respectively) (13). These findings support the use of COVID-19 vaccinations to improve SARS-CoV-2 outcomes of patients undergoing chronic dialysis (14). SARS-CoV-2 booster dose hesitancy among MHD patients is a major concern and emphasizes the need to develop effective strategies to increase vaccine compliance (12). According to our results, the risk factors associated with poor outcome in COVID-19 infected dialysis patients included diabetes mellitus, CVC access for dialysis, and frailty. CVC vascular access was an independent risk factor; this result is consistent with that of previous research [15]. CVC use has been reported to result in excess mortality in hemodialysis patients (16), thus, verifying identical findings in cases of COVID-19 is reasonable. Potential microorganisms that adhere to the CVC surface may increase the likelihood of bloodstream contamination in patients undergoing MHD (17). Arteriovenous fistulas (AVFs) are associated with higher survival rates in patients with MHD and COVID-19. In particular, during pandemic situations, clinicians who manage patients with stage 5 CKD should establish appropriate vascular access using an AVF (18). Our results showed that diabetes mellitus was an independent risk factor for mortality in patients with COVID-19 undergoing MHD. The estimated prevalence of diabetes in China increased from 10.9% in 2013 to 12.4% in 2018 (19). Our data are consistent with this previous result, as 76% of non-survivors were diagnosed with diabetes mellitus. Uncontrolled blood sugar levels are associated with poor COVID-19 outcomes (20). Frailty is common among patients undergoing dialysis and may contribute to impaired immunogenicity (21). This study demonstrated that higher clinical frailty scores were associated with higher mortality in dialysis patients with COVID-19. This indicates that an easy-to-use clinical frailty score such as the CFS should be used to guide treatment decisions in dialysis patients with COVID-19 (22). Therefore, strategies aimed at preventing or attenuating frailty in the dialysis population are warranted (21). In addition, we found that among patients undergoing MHD with COVID-19, the decline in albumin and hemoglobin levels was pronounced in survivors. This implies a profound exacerbation of the catabolic effects of COVID-19 due to factors such as eating disorders, oxygen deficiency, immobilization, and increased levels of proinflammatory cytokines (23). Therefore, clinicians should consider allowing intradialytic meals and complementary oral nutrition protocols to be continued during COVID-19 outbreaks. CKD requiring dialysis can decrease the immune response against COVID-19 (24). Tian et al. reported antibody responses to SARS-CoV-2 in 484 non-dialysis patients with mild or moderate symptoms ranging from 154 to 193 days, with 92% of the recovered patients presenting a positive IgG response and 63% presenting a positive IgM response (25). The current study revealed a shortage of IgG seroconversion in 40.5% of the hemodialysis patients. The increased risk of severe COVID-19 in patients undergoing MHD may, to some extent, be due to a decreased antibody response (24). As multiple COVID-19 vaccines have been authorized for use, we expect that dialysis patients who receive vaccinations will develop protective immunity. CRP is a component of the innate immune system and is elevated in response to various inflammatory conditions (26). Our analysis revealed a statistical association between CRP and SARS-CoV-2-specific IgG concentrations 90–120 days after infection, suggesting that elevated levels of CRP before acute COVID-19 infection may decrease the concentration of IgG antibodies post-infection. A persistent, unresolved infection may decrease the concentration of IgG antibodies after COVID-19 infection. Despite the significance of our findings, some limitations of this study must be considered. The main limitation is that this was a single-center retrospective study with a relatively small sample size. Second, approximately 95% of patients in the dialysis center had not received a COVID-19 vaccine; therefore, vaccine-related statistical analysis was not conducted. Third, the outbreak of this particular COVID-19 strain was so rapid that we were unable to obtain clinical data on oxygen saturation (SatO2), respiratory rate, and other vital signs, particularly for the outpatient dialysis patients. Therefore, we were unable to determine the World Health Organization classification of COVID-19 severity. Moreover, we did not acquire data on the SARS-CoV-2 antibody responses in non-survivors. Thus, the impact of such responses on mortality remains uninvestigated. Finally, antiviral drugs were not available in the dialysis center during the epidemic period. Thus, the effects of antiviral drugs such as remdesivir were not analyzed in this study. Given the unpredictable clearance of the drug, the clinical safety of remdesivir therapy in COVID-19 patients with renal insufficiency should be evaluated. With further multi-center studies with larger sample sizes, the identification of prognostic risk factors associated with poor outcomes in patients undergoing hemodialysis and the characterization of the SARS-CoV-2 antibody response may improve future therapeutic and preventive methods, including vaccination strategies. Conclusion Patients undergoing MHD are susceptible to COVID-19 and have high mortality rates. Diabetes mellitus, frailty, and CVC vascular access are poor prognostic factors of COVID-19 in patients undergoing hemodialysis. Furthermore, patients undergoing MHD have a decreased ability to produce specific antibodies against COVID-19. The persistence of an unresolved infection may decrease the concentration of IgG antibodies after COVID-19 infection. Understanding the risk factors for mortality and antibody responses to COVID-19 is critical for interventions aimed at decreasing mortality in dialysis patients. This understanding and subsequent strategies to decrease mortality could improve preparedness for future pandemics. Abbreviations MHD, maintenance hemodialysis; COVID-19, coronavirus disease 2019; CFS, Clinical Frailty Scale; CVC, central venous catheter; s/co, sample/cutoff; DW, dry weight; BCM, body composition monitor; AU, arbitrary unit Declarations Ethics approval and consent to participate All procedures involving human participants followed the Declaration of Helsinki and its later amendments. Ethics approval for this study was obtained from the Research and Ethics Committee of Aerospace Center Hospital. All the included patients signed informed consent for the release of clinical data for publication. Consent for publication Not applicable Availability of data and materials The data generated during this study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was financially supported by the Scientific Research Fund of the Aerospace Center Hospital (YN202209), Natural Science Foundation of China (NSFC) (82170686), and a grant from GYC (22KJLJ001). Author contributions Yu-Huan Song conceived and designed the study, participated in the literature search, and drafted the manuscript. Guang-Yan Cai and Yue-Fei Xiao designed the study and performed the literature search.Yu-Huan Song revised the manuscript for intellectual content. Authors' information Department of Nephrology, Aerospace Center Hospital (Peking University Aerospace School of Clinical Medicine), Beijing, China Yu-Huan Song and Yue-Fei Xiao Department of Nephrology, State Key Laboratory of Kidney Diseases, Chinese PLA General Hospital, Beijing, China Guang-Yan Cai Acknowledgements Not applicable. References Sun Y, Wang M, Lin W, Dong W, Xu J. Evolutionary analysis of Omicron variant Q19 BF.7 and BA.5.2 pandemic in China. J Biosaf Biosecur (2023) 5:14–20. doi: 10.1016/j.jobb.2023.01.002. Chung EYM, Palmer SC, Natale P, Krishnan A, Cooper TE, Saglimbene VM, et al. Incidence and outcomes of COVID-19 in people with CKD: A systematic review andmeta-analysis. Am J Kidney Dis (2021) 78:804–15. doi: 10.1053/j.ajkd.2021.07.003 Moore LR, Al-Jaddou N, Wodeyar H, Sharma A, Schulz M, Rao A, et al. SARSCoV-2 in dialysis patients and the impact of vaccination. 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Int J Eat Disord (2023) 56:5–25. doi: 10.1002/eat.23704 Beppu H, Fukuda T, Kawanishi T, Yasui F, Toda M, Kimura H, et al. Hemodialysis patients with coronavirus disease 2019: reduced antibody response. Clin Exp Nephrol (2022) 26:170–7. doi: 10.1007/s10157-021-02130-8 Tian X, Liu L, Jiang W, Zhang H, Liu W, Li J. Potent and Persistent Antibody Response in COVID-19 Recovered Patients. Front Immunol. 2021 May 28;12:659041. doi: 10.3389/fimmu.2021.659041. Hachim SK, Ali AS, Arif KB. Effect of IL-6 and CRP titer with antibody level on severity of COVID-19 infection. Hum Antibodies. (2023) 31:45–9. doi: 10.3233/HAB-23000 Tables Table 1 Demographic and laboratory characteristics of hemodialysis patients with COVID-19 Variable Survivors n = 134 Non-Survivors n = 25 p Demographics Male(%) Age, years Dialysis vintage, months Frailty index,arbitrary units Systolic BP, mmHg Diastolic BP, mmHg Dialysis catheter interdialytic weight gain Body mass index, kg/m 2 Comorbidities,n(%) Diabetic mellitus Hypertension Ischemic heart disease Stroke Smoking Drinking Malignancy Laboratory values Serum creatinine, umol/L Serum uric acid, umol/L Serum calcium, mmol/L Serum phosphorus, mmol/L Alkaline phosphatase, U/L Serum lactate dehydrogenase, U/L Kt/V IPTH, pg/ml Hemoglobin, g/L Albumin, g/L Triglyceride, mmol/L Cholesterol, mmol/L White blood cell count, ×10 9 /L The percentage of lymphocyte(%) C-reactive protein 77(57.5%) 62.33±12.27 36.0(24.0-98.0) 3.71±1.00 142.93±19.91 73.58±13.57 21(15.7%) 2.57±0.75 23.87±3.23 64(47.8%) 109(81.3%) 70(52.2%) 32(23.9%) 52(38.8%) 23(17.2%) 15(11.2%) 821.14±246.96 380.8 (341.9-416.5) 2.29±0.20 1.88±0.55 75.08±23.04 202.81±49.71 1.21±0.32 225.81±139.82 110.80±15.92 38.01±3.21 2.67±6.99 3.78±1.11 6.24±1.61 17.00±6.68 7.80±14.03 16(64.0%) 71.56±10.65 30.0(15.5±76.0) 5.44±1.04 147.92±25.17 74.12±10.24 12(48.0%) 2.01±0.86 23.26±4.44 19(76.0%) 22(88.0%) 15(60.0%) 9(36.0%) 8(32.0%) 6(24.0%) 2(8.0%) 673.70±298.07 362.5(308.9-456.25) 2.21±0.18 1.87±0.51 83.72±32.84 210.85±48.19 1.22±0.21 236.93±135.89 105.48±21.7 35.04±4.82 1.87±1.17 3.70±1.13 7.09±3.66 13.77±4.73 10.79±13.84 0.543 0.363 0.180 0.000 0.314 0.067 0.001 0.615 0.050 0.009 0.422 0.475 0.519 0.772 0.416 0.635 0.190 0.567 0.547 0.677 0.008 0.367 0.577 0.564 0.003 0.007 0.528 0.542 0.011 0.023 0.383 BP, blood pressure; iPTH, Intact parathyroid hormone. Kt/V (where K is the dialyzer clearance of urea; t is the dialysis time; and V is the volume of distribution of urea). Table 2 Univariate logistic regression for death in hemodialysis patients with COVID-19. Variable OR(95% CI) p value Use of Central venous catheter Frailty,arbitrary units Diabetes mellitus Hemoglobin, g/L Albumin, g/L Alkaline phosphatase, U/L White blood cell count, ×10 9 /L The percentage of lymphocyte(%) 5.829(2.341-14.517) 2.370(1.600-3.510) 3.464(1.302-9.214) 0.984(0.963-1.007) 0.819(0.730-0.919) 1.013(0.997-1.028) 1.176(0.973-1.420) 0.902(0.827-0.985) 0.000 0.000 0.013 0.165 0.001 0.118 0.093 0.021 Table 3 Multivariate logistic regression for death in hemodialysis patients with COVID-19. Variable Model 1 Model 2 Model 3 OR(95% CI) p OR(95% CI) p OR(95% CI) p Use of Central venous catheter Frailty,arbitrary units Diabetes mellitus The percentage of lymphocyte(%) Albumin, g/L 2.929(0.894-9.597) 1.945(1.230-3.077) 4.684(1.385-15.838) 0.951(0.869-1.041) 0.907(0.792-1.040) 0.076 0.004 0.013 0.279 0.162 3.746(1.210-11.599) 1.974(1.261-3.090) 4.286(1.308-14.041) 0.934(0.853-1.022) 0.022 0.003 0.016 0.137 3.483(1.179-10.291 2.096(1.358-3.233) 3.984(1.254-12.657) 0.024 0.001 0.019 Model 1, adjusted for Use of Central venous catheter, Frailty,Diabetes mellitus, The percentage of lymphocyte and Albumin; Model 2, adjusted for Use of Central venous catheter, Frailty,Diabetes mellitus, The percentage of lymphocyte; Model 3, adjusted for Use of Central venous catheter, Frailty,Diabetes mellitus. Table 4 Comparison of dry weight and blood test data pre- and post- COVID-19 in 134 survivors 30 days after the infection Survived spatients Pre-COVID-19 Post-COVID-19 p alue Dry weight Hemoglobin, g/L Albumin, g/L Serum calcium, mmol/L Serum phosphorus, mmol/L IPTH, pg/ml 66.39±11.71 110.80±15.92 38.01±3.21 2.29±0.20 1.88±0.55 225.81±139.82 66.16±12.02 104.32±12.83 35.35±3.36 2.27±0.20 1.69±0.52 268.25±155.25 0.874 0.000 0.000 0.518 0.007 0.023 Table 5 Clinical biomarkers before the onset of SARS-CoV-2 infection to predict SARS-CoV-2 IgG response ranged 90-120 days after the COVID-19 infection. Variable IgG(+) n =72 IgG(-) n = 49 p Male Age, years Body mass index, kg/m2 Diabetic mellitus Smoking Needed to hospitalization Serum calcium, mmol/L Serum phosphorus, mmol/L Intact parathyroid hormone , pg/ml Hemoglobin, g/L Albumin, g/L White blood cell count, ×10 9 /L C-reactive protein 37 63.08±12.62 23.81±2.99 21 38 14 2.29±0.21 1.83±0.58 220.39±118.12 110.51±12.43 37.83±3.20 6.21±1.66 6.45±8.48 31 61.29±11.55 24.10±3.56 12 21 9 2.27±0.18 1.96±0.46 222.81±117.47 111.42±20.32 38.57±2.68 6.49±1.69 10.87±20.49 0.196 0.488 0.284 0.067 0.284 0.882 0.462 0.424 0.707 0.320 0.588 0.713 0.004 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4428998","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":307212097,"identity":"853c2f2e-ec91-4b8c-b59a-f9eb997455a3","order_by":0,"name":"Yuhuan Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYHACNhAhx8befoA0LcZ8PGcSSNOSOE/CwYA49fIzcswe89TcSW+TYEhg+FGxjbAWgxs55sY8x57ltkk3HmDsOXObCC0SOWbSPGyHc9tkDiQwM7YRoQXkMGmef4fT2SQSDIjTwnADqIW37XAC8VoMzjwrk5zbd9iwDRjIB4nyi3x78jaJN98Oy8u3tx988KOCGIcJJCDYB4hQDwT8RKobBaNgFIyCEQwAJlA5RTID72gAAAAASUVORK5CYII=","orcid":"","institution":"Aerospace Center Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuhuan","middleName":"","lastName":"Song","suffix":""},{"id":307212099,"identity":"54d5b13c-597c-48ae-b1fb-d892bfb390ca","order_by":1,"name":"Guangyan Cai","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangyan","middleName":"","lastName":"Cai","suffix":""},{"id":307212101,"identity":"a6856925-1c28-4a26-b3a8-c439521ae400","order_by":2,"name":"Yuefei Xiao","email":"","orcid":"","institution":"Aerospace Center Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuefei","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2024-05-16 06:52:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4428998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4428998/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57706586,"identity":"bdc7e420-10c9-46fe-a0a2-4883ce0913cf","added_by":"auto","created_at":"2024-06-04 15:00:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":981081,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flow diagram.\u003c/p\u003e","description":"","filename":"20240510figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4428998/v1/45e6aa20d07b6f496c345649.png"},{"id":57706023,"identity":"be78af3c-1ec5-4789-a38c-11cd9fb1ec94","added_by":"auto","created_at":"2024-06-04 14:52:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111270,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of IgG antibody levels detected in survived patients ranged 90-120 days after the COVID-19 infection.\u003c/p\u003e","description":"","filename":"20240510figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4428998/v1/ca461508310294d3c13d1cee.png"},{"id":57706022,"identity":"7821c7e0-45d1-46fd-9973-7ab15904e8d7","added_by":"auto","created_at":"2024-06-04 14:52:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42329,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the relationship between serum C-reactive protein level before COVID-19 infection and IgG antibody ranged 90-120 days after COVID-19 infection in surviving MHD patients,IgG was considered as positive(+) if \u0026gt;1.0 s/co.\u003c/p\u003e","description":"","filename":"20240510figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4428998/v1/7fee7f575fc65bda126ec931.png"},{"id":68164402,"identity":"4c8b3292-eadf-4854-bd7a-cd2e1d6e2c98","added_by":"auto","created_at":"2024-11-04 09:32:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1508320,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4428998/v1/89236183-02c9-4707-a981-da1241866093.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors for mortality and antibody responses in chronic hemodialysis patients with coronavirus disease 2019: a single-center experience in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince December 7, 2022, adjustments in the prevention and control policies have increased the number of people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in mainland China (1). The emergence of this pandemic has disproportionately affected people with chronic diseases, such as chronic kidney disease (CKD), particularly CKD requiring maintenance hemodialysis (MHD) (2). Despite protective measures, patients undergoing dialysis are at high risk of contracting coronavirus disease 2019 (COVID-19). These patients have inferior immune responses compared with those of the healthy population. Dialysis is generally administered in large universal and crowded spaces (3). The first reported case of COVID-19 occurred at the dialysis center of the Aerospace Center Hospital on December 6, 2022. Currently, the factors associated with COVID-19 mortality and naturally acquired immunity in patients undergoing hemodialysis are largely unexplored. Variations in the antibody responses to natural infections remain unclear and unpredictable. Therefore, the aim of this study was to determine the factors affecting mortality and levels of specific serum immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against SARS-CoV-2 in dialysis patients with COVID-19 in China.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eAll procedures involving human participants followed the Declaration of Helsinki and its later amendments. Ethics approval for this study was obtained from the Research and Ethics Committee of Aerospace Center Hospital.\u0026nbsp;All the included patients\u0026nbsp;signed informed consent for the release of clinical data for publication.\u003c/p\u003e\n\u003cp\u003eA total of 216 patients who had been on\u0026nbsp;hemodialysis (4 h, 3 times weekly)\u0026nbsp;for more than 3 months at the Aerospace Center Hospital of China as of December 2022\u0026nbsp;were included. Screening of patients prior to entering the dialysis center was conducted in an approved laboratory\u0026nbsp;using reverse transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 infection. Surveillance revealed that the first new case of COVID-19\u0026nbsp;occurred on December 6, 2022. Patients with positive RT-PCR\u0026nbsp;results\u0026nbsp;were diagnosed with COVID-19 and underwent dialysis in\u0026nbsp;segregation.\u003c/p\u003e\n\u003cp\u003eThe inclusion criterion\u0026nbsp;included patients who had been undergoing\u0026nbsp;hemodialysis\u0026nbsp;for \u0026gt;3 months\u0026nbsp;and were positive for SARS-CoV-2. The\u0026nbsp;exclusion criteria were\u0026nbsp;transfer to other dialysis centers, completion of one or more doses of COVID-19 vaccine(s) before SARS-CoV-2 infection, and kidney transplantation\u0026nbsp;or incomplete laboratory results.\u003c/p\u003e\n\u003cp\u003eThe following outcomes were investigated: (1) SARS-CoV-2 infection between December 6, 2022\u0026nbsp;and\u0026nbsp;April 6, 2023\u0026nbsp;and (2) 90-day mortality among infected patients. For\u0026nbsp;patients with SARS-CoV-2, the following variables\u0026nbsp;were also investigated: (3) hospitalization (yes, no) and (4) immune response to immunoglobulin G (IgG) and IgM 90-120 days after infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring the study period, antiviral drugs were not administered to patients who had symptoms but did not require hospitalization. Rather, these patients were administered symptomatic supportive treatments such as antipyretic and oral anti-inflammatory drugs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe collected the clinical data of the hemodialysis patients on December 1, 2022, approximately one week before the first confirmed COVID-19 infection in the dialysis center, rather than collecting the data after confirmed SARS-CoV-2 infection. Blood samples were routinely obtained before the second hemodialysis session.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBackground data, such as age,\u0026nbsp;sex,\u0026nbsp;dialysis duration,\u0026nbsp;and smoking status, were recorded. In addition, data on comorbidities, such as diabetes mellitus, hypertension, cardiovascular disease, and malignancy, were collected. The Clinical Frailty Scale\u0026nbsp;(CFS) was used to determine the patient level of frailty prior to COVID-19 infection According to this scale, a score of 1 indicates that the patient is \u0026ldquo;very fit\u0026rdquo; in that they are \u0026ldquo;robust, active, energetic, and motivated.\u0026rdquo; A score of\u0026nbsp;9 categorizes a patient who is \u0026ldquo;terminally ill\u0026rdquo;\u0026nbsp;(4, 5). Finally, all relevant\u0026nbsp;laboratory\u0026nbsp;test results were recorded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the potential influence of nutritional status on patient outcome, we estimated the trends in dry weight (DW) and laboratory parameters in the trajectories of MHD patients with RT-PCR-certified COVID-19 who survived for approximately 30 days after\u0026nbsp;the diagnosis of SARS-CoV-2.\u0026nbsp;DWs were adjusted according to\u0026nbsp;the body composition monitor (BCM) output data following the\u0026nbsp;DW adjustment strategy. Patients in the survival group underwent BCM measurements at the beginning and approximately 30 days after diagnosis of SARS-CoV-2 infection. The body composition and hydration state were estimated using a portable whole-body bioimpedance spectroscopy device (Fresenius Medical Care, Germany).\u003c/p\u003e\n\u003cp\u003eRT-PCR was performed using a reverse transcription quantitative PCR method according to the manufacturer\u0026rsquo;s protocol (Bojie Co. Ltd, Shanghai, China).\u0026nbsp;The levels of\u0026nbsp;specific serum IgM and IgG antibodies against SARS-CoV-2 (Autobio Co. Ltd., Zhengzhou, China) were measured in surviving MHD patients 90-120 days after infection. The IgG and IgM indices were considered positive if they were \u0026gt;1.0 sample/cutoff (s/co).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eSPSS (version 22.0; SPSS Inc., Chicago, IL, USA) was used for statistical analyses. All data are presented as mean \u0026plusmn; standard deviation (SD); non-normally distributed variables are presented as median and interquartile range (IQR). Categorical variables are expressed as n (%). Continuous variable differences in outcomes were analyzed using one-way analysis of variance (ANOVA), whereas categorical variables were analyzed using the chi-square test. To determine the relative risk of the possible predictor variables, odds ratios (OR) and 95% confidence intervals (95% CI) were calculated using logistic regression analysis. Clinical indices that were significant in the univariate analysis (p\u0026lt;0.05) were identified as potential predictor indices and analyzed using multivariate analysis. A forward stepwise (conditional) model was constructed using a multivariate analysis that incorporated binary logistic regression.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eA flow diagram of the patients who were infected with COVID-19 is shown in Figure 1.\u0026nbsp;A total of 216 patients were on dialysis for more than 3 months at\u0026nbsp;the Aerospace Center Hospital of China during the study period.\u0026nbsp;COVID-19 was diagnosed in 207 patients,\u0026nbsp;representing\u0026nbsp;a positivity rate of 95.8%. Nine patients who were COVID-19 negative have survived to date, including one patient\u0026nbsp;who required hospitalization for myocardial ischemia.\u0026nbsp;Of the 207 COVID-19 positive patients, 55 (26.6%) patients with SARS-CoV-2 presented with clinical worsening and required hospitalization. The three\u0026nbsp;most common causes of hospitalization were respiratory infections, cardiac diseases, and\u0026nbsp;gastrointestinal disorders. These complications were all attributed to COVID-19.\u0026nbsp;Of the 55\u0026nbsp;hospitalized patients, 25\u0026nbsp;COVID-19 positive patients died within 90 days. The cause of death was COVID-19\u0026nbsp;infection.\u0026nbsp;Twenty-three out of the\u0026nbsp;25\u0026nbsp;patients died within 28 days of follow-up. None of the 25 patients who died had received the SARS-CoV-2 vaccination. Among the 182 surviving patients, 39 patients without complete laboratory results, six patients who were transferred to other dialysis centers, and three patients who underwent kidney transplantation were excluded. Patients with complete data (n=134) were included in the survival group.\u003c/p\u003e\n\u003cp\u003eTable 1 shows a comparison of the clinical data between survivors and non-survivors before the onset of SARS-CoV-2 infections. The mean hemoglobin was 105.48\u0026plusmn;21.7 g/L in non-survivors, which was significantly lower than that in the survivors (110.80\u0026plusmn;15.92 g/L) (p=0.003). Albumin level in non-survivors was also significantly lower than that in survivors (35.04\u0026plusmn;4.82 g/L vs. 38.01\u0026plusmn;3.21 g/L, respectively; p=0.007). The serum alkaline phosphatase level in non-survivors (83.72\u0026plusmn;32.84 mmol/L) was significantly higher than that in survivors (75.08\u0026plusmn;23.04 mmol/L; p=0.008). White blood cell count in non-survivors was 7.09\u0026plusmn;3.66\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, which is significantly higher than that in survivors (6.24\u0026plusmn;1.61\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L; p=0.011).\u0026nbsp;The percentage of lymphocytes\u0026nbsp;in non-survivors was\u0026nbsp;13.77\u0026plusmn;4.73%, which was significantly lower than that in survivors (17.00\u0026plusmn;6.68%; p=0.023).\u0026nbsp;The CFS was\u0026nbsp;5.44\u0026plusmn;1.04 arbitrary units (AU) in non-survivors and\u0026nbsp;3.71\u0026plusmn;1.00\u0026nbsp;AU in survivors (p=0.000). No significant differences were found in age, dialysis duration, phosphorus level, intact parathyroid hormone\u0026nbsp;level,\u0026nbsp;serum lactate dehydrogenase level, Kt/V (K is the dialyzer clearance of urea, t is the dialysis time, and V is the volume of urea distribution),\u0026nbsp;or other laboratory parameters (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eIn addition, we compared the effects of age, sex, and comorbidities on mortality incidence in our analysis. Mortality was higher among patients with diabetes mellitus (p=0.009) and\u0026nbsp;those receiving dialysis administered through a CVC than among patients with arteriovenous fistulas (p=0.001). No differences were observed between the survivors and non-survivors in terms of age, sex, smoking status, and comorbidity in incidences of hypertension, ischemic heart disease, or malignancy (p\u0026gt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression analysis showed that hemoglobin levels and white blood cell counts had no significant effects on mortality (p\u0026gt;0.05) (Table 2). CVC access, diabetes mellitus, frailty as determined using the\u0026nbsp;CFS,low\u0026nbsp;percentage\u0026nbsp;of\u0026nbsp;lymphocyte, and low serum albumin levels were independent risk factors for mortality (p\u0026lt;0.05). Using multivariate logistic analysis and adjusting the results of independent variables significant in the univariate analysis, three different models were created and applied to the multivariate logistic regression forward elimination analysis (Table 3). In model 1, diabetes mellitus and frailty were significant predictors of mortality. In model 2, after removing lower albumin levels, diabetes mellitus, CVC access, and\u0026nbsp;CFS were significant predictors of mortality. In model 3, upon eliminating\u0026nbsp;the lymphocyte percentage from the list, CVC use (HR 3.48, 95% CI 1.18-10.29, p=0.024), diabetes (HR 3.98, 95% CI 1.25-12.66, p=0.019), and\u0026nbsp;CFS (frailty)\u0026nbsp;(HR 2.10, 95% CI 1.36-3.23, p=0.001) predicted higher mortality.\u003c/p\u003e\n\u003cp\u003eA comparison of the DW and blood test data of\u0026nbsp;the survivors before COVID-19 and 30 days after\u0026nbsp;infection is shown in Table 4. Hemoglobin, albumin, and serum phosphorus levels were lower, whereas intact parathyroid hormone (iPTH) levels were higher in survivors\u0026nbsp;at 30 days post-infection (p\u0026lt;0.05). DW and serum calcium levels remained unchanged before and after COVID-19.\u003c/p\u003e\n\u003cp\u003eIgG and IgM antibodies against SARS-CoV-2 were tested in 121 surviving patients from 90-120 days after COVID-19 infection. The remaining 13 patients refused to undergo antibody testing for SARS-CoV-2. The s/co index for IgG ranged from 0.03 to 417.16 (mean 15.91) and that for IgM ranged from 0.02 to 52.01 (mean 0.57).\u0026nbsp;The distribution of IgG antibody levels\u0026nbsp;in the 121 surviving patients is shown in Figure 2. This analysis included 40.4% of patients who had an IgG antibody titer of \u0026lt;1 s/co, 45.45% of patients who had an IgG antibody titer of 1\u0026ndash;10 s/co, 8.26% of patients who had an IgG antibody titer of 10\u0026ndash;100 s/co,\u0026nbsp;and 5.79% of patients\u0026nbsp;who had an IgG antibody titer \u0026gt;100 s/co. The semi-quantitative results of SARS-CoV-2 antibodies in the MHD patients revealed that 4/121 (3.3%) patients were positive for IgM, and\u0026nbsp;72/121 (59.5%) patients were positive for IgG.\u003c/p\u003e\n\u003cp\u003eAccording to the data collected to determine the factors affecting the antibody response in the patients from 90\u0026ndash;120 days after infection, there were no statistically significant differences in antibody levels according to sex, body mass index (BMI), smoking status, diabetes mellitus, and hospital stay (p\u0026gt;0.05). Blood CRP levels in the IgG (+) group were significantly lower than those in the IgG (-) group (p\u0026lt;0.05) (Table 5). Low blood CRP levels before the onset of SARS-CoV-2 infection were similarly associated with high IgG levels at 90-120 days post-infection, as shown in Figure 3.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWorldwide, the COVID-19 pandemic has disrupted every facet of life, including health service delivery. This is particularly true for people living with chronic diseases who require consistent monitoring of care needs and medication by health professionals to maintain and enhance their health (\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e. Patients undergoing dialysis are categorized as having a high risk of SARS-CoV-2 infection/transmission and are more vulnerable to unfavorable clinical outcomes. Previous studies have shown that the Omicron COVID-19 threat to patients undergoing MHD is lower than that of previous variants (7); however, the present study revealed a higher mortality rate among MHD patients after COVID-19 infection than that of the general population (12.1% and 1.45% to 8%, respectively) (8-10).\u003c/p\u003e\n\u003cp\u003eYavuz\u0026nbsp;et al.\u0026nbsp;reported that approximately 49.5% of patients on hemodialysis and peritoneal dialysis with SARS-CoV-2 infection required hospitalization (11). The current study showed that approximately 26.6% of COVID-positive MHD patients required hospitalization, demonstrating the need for sufficient resource planning in in-hospital dialysis facilities. In addition, our study showed a mortality rate of 12.1% among unvaccinated patients. Hemodialysis patients have a high rate of hesitancy regarding COVID-19 primary vaccination (12). A previous study revealed lower mortality rates in fully and partially vaccinated individuals (5.3% and 8.3%, respectively) (13). These findings support the use of COVID-19 vaccinations to improve SARS-CoV-2 outcomes of patients undergoing chronic dialysis (14). SARS-CoV-2 booster dose hesitancy among MHD patients is a major concern and emphasizes the need to develop effective strategies to increase vaccine compliance (12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to our results, the risk factors associated with poor outcome in COVID-19 infected dialysis patients included diabetes mellitus, CVC access for dialysis, and frailty. CVC vascular access was an independent risk factor; this result is consistent with that of previous research [15]. CVC use has been reported to result in excess mortality in hemodialysis patients (16), thus, verifying identical findings in cases of COVID-19 is reasonable. Potential microorganisms that adhere to the CVC surface may increase the likelihood of bloodstream contamination in patients undergoing MHD (17). Arteriovenous fistulas (AVFs) are associated with higher survival rates in patients with MHD and COVID-19. In particular, during pandemic situations, clinicians who manage patients with stage 5 CKD should establish appropriate vascular access using an AVF (18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur results showed that diabetes mellitus was an independent risk factor for mortality in patients with COVID-19 undergoing MHD. The estimated prevalence of diabetes in China increased from 10.9% in 2013 to 12.4% in 2018 (19). Our data are consistent with this previous result, as 76% of non-survivors were diagnosed with diabetes mellitus. Uncontrolled blood sugar levels are associated with poor COVID-19 outcomes (20).\u003c/p\u003e\n\u003cp\u003eFrailty is common among patients undergoing dialysis and may contribute to impaired immunogenicity\u0026nbsp;(21).\u0026nbsp;This study demonstrated that higher clinical frailty scores\u0026nbsp;were associated with\u0026nbsp;higher mortality in\u0026nbsp;dialysis patients with COVID-19.\u0026nbsp;This indicates that an easy-to-use clinical frailty score such as the CFS should be used to guide treatment decisions in dialysis patients with COVID-19 (22).\u0026nbsp;Therefore, strategies aimed at preventing or attenuating frailty in the dialysis population are warranted\u0026nbsp;(21).\u003c/p\u003e\n\u003cp\u003eIn addition, we found that among patients undergoing MHD with COVID-19, the decline in albumin and hemoglobin levels was pronounced in survivors. This implies a profound exacerbation of the catabolic effects of COVID-19 due to factors such as eating disorders, oxygen deficiency, immobilization, and increased levels of proinflammatory cytokines (23). Therefore, clinicians should consider allowing intradialytic meals and complementary oral nutrition protocols to be continued during COVID-19 outbreaks.\u003c/p\u003e\n\u003cp\u003eCKD requiring\u0026nbsp;dialysis can decrease the immune response against COVID-19 (24). Tian et al. reported antibody responses to SARS-CoV-2 in 484 non-dialysis patients with mild or moderate symptoms ranging from 154 to 193 days, with 92% of the recovered patients presenting a positive IgG response and 63% presenting a positive IgM response (25). The current study revealed a shortage of IgG seroconversion in 40.5% of the hemodialysis patients. The increased risk of severe COVID-19 in patients undergoing MHD may, to some extent, be due to a decreased antibody response (24). As multiple COVID-19 vaccines have been authorized for use, we expect that dialysis patients who receive vaccinations will develop protective immunity. CRP is a component of the innate immune system and is elevated in response to various inflammatory conditions (26). Our analysis revealed a statistical association between CRP and SARS-CoV-2-specific IgG concentrations 90\u0026ndash;120 days after infection, suggesting that elevated levels of CRP before acute COVID-19 infection may decrease the concentration of IgG antibodies post-infection. A persistent, unresolved infection may decrease the concentration of IgG antibodies after COVID-19 infection.\u003c/p\u003e\n\u003cp\u003eDespite the significance of our findings, some limitations of this study must be considered. The main limitation is that this was a single-center retrospective study with a relatively small sample size. Second, approximately 95% of patients in the dialysis center had not received a COVID-19 vaccine; therefore, vaccine-related statistical analysis was not conducted. Third,\u0026nbsp;the outbreak of this particular COVID-19 strain was so rapid that\u0026nbsp;we were unable to obtain\u0026nbsp;clinical data\u0026nbsp;on\u0026nbsp;oxygen saturation (SatO2), respiratory rate, and other vital signs, particularly for the outpatient dialysis patients. Therefore, we were unable to determine the\u0026nbsp;World Health Organization\u0026nbsp;classification of COVID-19 severity.\u0026nbsp;Moreover, we\u0026nbsp;did not acquire data on the SARS-CoV-2 antibody responses in non-survivors. Thus, the impact of such responses on mortality remains uninvestigated. Finally, antiviral drugs were not available in the dialysis center during the epidemic period. Thus, the effects of antiviral drugs such as remdesivir were not analyzed in this study.\u0026nbsp;Given the unpredictable clearance of the drug, the clinical safety of remdesivir therapy in COVID-19 patients with renal insufficiency should be evaluated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith further multi-center studies with larger sample sizes, the identification of prognostic risk factors associated with poor outcomes in patients undergoing hemodialysis and the characterization of the SARS-CoV-2 antibody response may improve future therapeutic and preventive methods, including vaccination strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePatients undergoing MHD are susceptible to COVID-19 and have high mortality rates. Diabetes mellitus, frailty, and CVC vascular access are poor prognostic factors of COVID-19 in patients undergoing hemodialysis. Furthermore, patients undergoing MHD have a decreased ability to produce specific antibodies against COVID-19. The persistence of an unresolved infection may decrease the concentration of IgG antibodies after COVID-19 infection. Understanding the risk factors for mortality and antibody responses to COVID-19 is critical for interventions aimed at decreasing mortality in dialysis patients. This understanding and subsequent strategies to decrease mortality could improve preparedness for future pandemics.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMHD, maintenance hemodialysis; COVID-19, coronavirus disease 2019; CFS, Clinical Frailty Scale; CVC, central venous catheter; s/co, sample/cutoff; DW, dry weight; BCM, body composition monitor; AU, arbitrary unit\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants followed the Declaration of Helsinki and its later amendments. Ethics approval for this study was obtained from the Research and Ethics Committee of Aerospace Center Hospital. All the included patients signed informed consent for the release of clinical data for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability\u0026nbsp;of\u0026nbsp;data\u0026nbsp;and materials\u003c/p\u003e\n\u003cp\u003eThe data generated during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the Scientific Research Fund of the Aerospace Center Hospital (YN202209), Natural Science Foundation of China (NSFC) (82170686), and\u0026nbsp;a grant from GYC (22KJLJ001).\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eYu-Huan Song conceived and designed the study, participated in the literature search, and drafted the manuscript. Guang-Yan Cai and Yue-Fei Xiao designed the study and\u0026nbsp;performed the literature search.Yu-Huan Song\u0026nbsp;revised the manuscript for intellectual content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Nephrology, Aerospace Center Hospital (Peking University Aerospace School of Clinical Medicine), Beijing, China\u003c/p\u003e\n\u003cp\u003eYu-Huan Song\u0026nbsp; and\u0026nbsp;Yue-Fei Xiao\u003c/p\u003e\n\u003cp\u003eDepartment of Nephrology, State Key Laboratory of Kidney Diseases, Chinese PLA General Hospital, Beijing, China\u003c/p\u003e\n\u003cp\u003eGuang-Yan Cai\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun Y, Wang M, Lin W, Dong W, Xu J. Evolutionary analysis of Omicron variant Q19 BF.7 and BA.5.2 pandemic in China. J Biosaf Biosecur (2023) 5:14\u0026ndash;20. doi: 10.1016/j.jobb.2023.01.002.\u003c/li\u003e\n\u003cli\u003eChung EYM, Palmer SC, Natale P, Krishnan A, Cooper TE, Saglimbene VM, et al. Incidence and outcomes of COVID-19 in people with CKD: A systematic review andmeta-analysis. Am J Kidney Dis (2021) 78:804\u0026ndash;15. doi: 10.1053/j.ajkd.2021.07.003\u003c/li\u003e\n\u003cli\u003eMoore LR, Al-Jaddou N, Wodeyar H, Sharma A, Schulz M, Rao A, et al. SARSCoV-2 in dialysis patients and the impact of vaccination. BMC Nephrol (2022) 23:317. doi: 0.1186/s12882-022-02940-2\u003c/li\u003e\n\u003cli\u003eRockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ (2005) 173:489\u0026ndash;95. doi: 10.1503/cmaj.050051\u003c/li\u003e\n\u003cli\u003eIkram A, Norrish AR, Marson BA, Craxford S, Gladman JRF, Ollivere BJ. Can the Clinical Frailty Scale on admission predict 30-day survival, postoperative complications, and institutionalization in patients with fragility hip fracture?: a cohort study of 1,255 patients. Bone Joint J (2022) 104-B:980\u0026ndash;6. doi: 10.1302/0301-620X.104B8.BJJ-2020-1835\u003c/li\u003e\n\u003cli\u003eAbraham SA, Agyare DF, Yeboa NK, Owusu-Sarpong AA, Banulanzeki ES, DokuDT, et al. The influence of COVID-19 pandemic on the health seeking behaviors ofadults living with chronic conditions: A view through the health belief model. J PrimCare Community Health (2023) 14:21501319231159459. doi: 10.1177/21501319231159459\u003c/li\u003e\n\u003cli\u003eAl Madani AK, Al Obaidli AK, Ahmed W, AlKaabi NA, Holt SG. The Omicron COVID-19 threat to dialysis patients is dramatically lower than previous variants. Nephrol (Carlton) (2022) 27:725\u0026ndash;6. doi: 10.1111/nep.14065\u003c/li\u003e\n\u003cli\u003eYe Q, Wang B, Mao J. The pathogenesis and treatment of the `Cytokine Storm\u0026rsquo; in COVID-19. J Infect (2020) 80:607\u0026ndash;13. doi: 10.1016/j.jinf.2020.03.037\u003c/li\u003e\n\u003cli\u003eGrasselli G, Zangrillo A, Zanella A, Antonelli M, Cabrini L, Castelli A, et al. COVID-19 Lombardy ICU network. Baseline characteristics and outcomes of 1591 patients infected with SARS-coV-2 admitted to ICUs of the Lombardy region, Italy. JAMA (2020) 323:1574\u0026ndash;81. doi: 10.1001/jama.2020.5394.\u003c/li\u003e\n\u003cli\u003eGoicoechea M, Sanchez Camara LA, Macıas N, Mu\u0026ntilde;oz de Morales A, Rojas AG, Bascu\u0026ntilde;ana A, et al. COVID-19: clinical course and outcomes of 36 hemodialysis patients in Spain. Kidney Int (2020) 98:27\u0026ndash;34. doi: 10.1016/j.kint.2020.04.031\u003c/li\u003e\n\u003cli\u003eYavuz D, Karag\u0026ouml;z \u0026Ouml;zen DS, Demirag MD. COVID-19: mortality rates of patients on hemodialysis and peritoneal dialysis. Int Urol Nephrol (2022) 54:2713\u0026ndash;8. doi: 10.1007/s11255-022-03193-6\u003c/li\u003e\n\u003cli\u003eAbdulaziz HMM, Saleh MA, Elrggal ME, Omar ME, Hawash SA, Attiya AMN, et al. Egyptian hemodialysis patients\u0026apos; willingness to receive the COVID-19 vaccine booster dose: a multicenter survey. J Nephrol (2023) 36:1329\u0026ndash;40. doi: 10.1007/ s40620-023-01586-z\u003c/li\u003e\n\u003cli\u003eMosconi G, Fantini M, Righini M, Flachi M, Semprini S, Hu L, et al. Efficacy of SARS-coV-2 vaccination in dialysis patients: epidemiological analysis and evaluation of the clinical progress. J Clin Med (2022) 11:4723. doi: 10.3390/jcm11164723\u003c/li\u003e\n\u003cli\u003eMiao J, Olson E, Houlihan S, Kattah A, Dillon J, Zoghby Z. Effects of SARS-CoV- 2 vaccination on the severity of COVID-19 infection in patients on chronic dialysis. J Nephrol (2023) 5:1\u0026ndash;8. doi: 10.1007/s40620-023-01617-9\u003c/li\u003e\n\u003cli\u003eLugon JR, Neves PDMM, Pio-Abreu A, do Nascimento MM, Sesso R. COVID-19 HD-Brazil Investigators. Evaluation of central venous catheter and other risk factors for mortality in chronic hemodialysis patients with COVID-19 in Brazil. Int Urol Nephrol. (2022) 54(1):193\u0026ndash;9. doi: 10.1007/s11255-021-02920-9.\u003c/li\u003e\n\u003cli\u003eAllon M. Vascular access for hemodialysis patients: new data should guide decision making. Clin J Am Soc Nephrol. (2019) 14:954\u0026ndash;61. doi: 10.2215/ CJN.00490119\u003c/li\u003e\n\u003cli\u003eLiakopoulos V, Roumeliotis S, Gorny X, Dounousi E, Mertens PR. Oxidative stress in hemodialysis patients: a review of the literature. Oxid Med Cell Longev (2017) 2017:3081856. doi: 10.1155/2017/3081856\u003c/li\u003e\n\u003cli\u003eMurt A, Yadigar S, Yalin SF, Dincer MT, Parmaksiz E, Altiparmak MR. Arteriovenous fistula as the vascular access contributes to better survival of hemodialysis patients with COVID-19 infection. J Vasc Access (2023) 24:22\u0026ndash;6. doi: 10.1177/11297298211021253\u003c/li\u003e\n\u003cli\u003eWang L, Peng W, Zhao Z, Zhang M, Shi Z, Song Z, et al. Prevalence and treatment of diabetes in China, 2013-2018. JAMA (2021) 326:2498\u0026ndash;506. doi: 10.1001/jama.2021.22208\u003c/li\u003e\n\u003cli\u003eUnnikrishnan R, Misra A. Infections and diabetes: Risks and mitigation with reference to India. Diabetes Metab Syndr (2020) 14:1889\u0026ndash;94. doi: 10.1016/ j.dsx.2020.09.022\u003c/li\u003e\n\u003cli\u003eLin TY, Hung SC. Frailty and Humoral Immune Responses Following COVID-19 Vaccination among Patients Undergoing Hemodialysis. J Nutr Health Aging (2023) 27:980\u0026ndash;6. doi: 10.1007/s12603-023-1994-x\u003c/li\u003e\n\u003cli\u003eBouwmans P, Brandts L, Hilbrands LB, Duivenvoorden R, Vart P, Franssen CFM, Covic A, Islam M, Rabat\u0026eacute; C, Jager KJ, Noordzij M, Gansevoort RT, Hemmelder MH; ERACODA collaborators. The Clinical Frailty Scale as a triage tool for ICU admission of dialysis patients with COVID-19: an ERACODA analysis. Nephrol Dial Transplant (2022) 37:2264\u0026ndash;74. doi: 10.1093/ndt/gfac246\u003c/li\u003e\n\u003cli\u003eDevoe D J, Han A, Anderson A, Katzman DK, Patten SB, Soumbasis A, et al. The impact of the COVID-19 pandemic on eating disorders: A systematic review. Int J Eat Disord (2023) 56:5\u0026ndash;25. doi: 10.1002/eat.23704\u003c/li\u003e\n\u003cli\u003eBeppu H, Fukuda T, Kawanishi T, Yasui F, Toda M, Kimura H, et al. Hemodialysis patients with coronavirus disease 2019: reduced antibody response. Clin Exp Nephrol (2022) 26:170\u0026ndash;7. doi: 10.1007/s10157-021-02130-8\u003c/li\u003e\n\u003cli\u003eTian X, Liu L, Jiang W, Zhang H, Liu W, Li J. Potent and Persistent Antibody Response in COVID-19 Recovered Patients. Front Immunol. 2021 May 28;12:659041. doi: 10.3389/fimmu.2021.659041. \u003c/li\u003e\n\u003cli\u003eHachim SK, Ali AS, Arif KB. Effect of IL-6 and CRP titer with antibody level on severity of COVID-19 infection. Hum Antibodies. (2023) 31:45\u0026ndash;9. doi: 10.3233/HAB-23000\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1\u0026nbsp;Demographic and laboratory characteristics of\u0026nbsp;hemodialysis\u0026nbsp;patients with COVID-19\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"528\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSurvivors n = 134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.893939393939394%\" valign=\"top\"\u003e\n \u003cp\u003eNon-Survivors n = 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.712121212121213%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.39393939393939%\" valign=\"top\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003cp\u003eMale(%)\u003c/p\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003cp\u003eDialysis vintage, months\u003c/p\u003e\n \u003cp\u003eFrailty index,arbitrary units\u003c/p\u003e\n \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e\n \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e\n \u003cp\u003eDialysis catheter\u003c/p\u003e\n \u003cp\u003einterdialytic weight gain\u003c/p\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e Comorbidities,n(%)\u003c/p\u003e\n \u003cp\u003eDiabetic mellitus\u003c/p\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003cp\u003eIschemic heart disease\u003c/p\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003cp\u003eLaboratory values\u003c/p\u003e\n \u003cp\u003eSerum creatinine, umol/L \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSerum uric acid, umol/L \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSerum calcium, mmol/L\u003c/p\u003e\n \u003cp\u003eSerum phosphorus, mmol/L\u003c/p\u003e\n \u003cp\u003eAlkaline phosphatase, U/L\u003c/p\u003e\n \u003cp\u003eSerum lactate dehydrogenase, U/L\u003c/p\u003e\n \u003cp\u003eKt/V\u003c/p\u003e\n \u003cp\u003eIPTH, pg/ml\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003cp\u003eTriglyceride, mmol/L\u003c/p\u003e\n \u003cp\u003eCholesterol, mmol/L\u003c/p\u003e\n \u003cp\u003eWhite blood\u0026nbsp;cell\u0026nbsp;count, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003cp\u003eThe percentage of lymphocyte(%)\u003c/p\u003e\n \u003cp\u003eC-reactive protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77(57.5%)\u003c/p\u003e\n \u003cp\u003e62.33\u0026plusmn;12.27\u003c/p\u003e\n \u003cp\u003e36.0(24.0-98.0)\u003c/p\u003e\n \u003cp\u003e3.71\u0026plusmn;1.00\u003c/p\u003e\n \u003cp\u003e142.93\u0026plusmn;19.91\u003c/p\u003e\n \u003cp\u003e73.58\u0026plusmn;13.57\u003c/p\u003e\n \u003cp\u003e21(15.7%)\u003c/p\u003e\n \u003cp\u003e2.57\u0026plusmn;0.75\u003c/p\u003e\n \u003cp\u003e23.87\u0026plusmn;3.23\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64(47.8%)\u003c/p\u003e\n \u003cp\u003e109(81.3%)\u003c/p\u003e\n \u003cp\u003e70(52.2%)\u003c/p\u003e\n \u003cp\u003e32(23.9%)\u003c/p\u003e\n \u003cp\u003e52(38.8%)\u003c/p\u003e\n \u003cp\u003e23(17.2%)\u003c/p\u003e\n \u003cp\u003e15(11.2%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e821.14\u0026plusmn;246.96\u003c/p\u003e\n \u003cp\u003e380.8 (341.9-416.5)\u003c/p\u003e\n \u003cp\u003e2.29\u0026plusmn;0.20\u003c/p\u003e\n \u003cp\u003e1.88\u0026plusmn;0.55\u003c/p\u003e\n \u003cp\u003e75.08\u0026plusmn;23.04\u003c/p\u003e\n \u003cp\u003e202.81\u0026plusmn;49.71\u003c/p\u003e\n \u003cp\u003e1.21\u0026plusmn;0.32\u003c/p\u003e\n \u003cp\u003e225.81\u0026plusmn;139.82\u003c/p\u003e\n \u003cp\u003e110.80\u0026plusmn;15.92\u003c/p\u003e\n \u003cp\u003e38.01\u0026plusmn;3.21\u003c/p\u003e\n \u003cp\u003e2.67\u0026plusmn;6.99\u003c/p\u003e\n \u003cp\u003e3.78\u0026plusmn;1.11\u003c/p\u003e\n \u003cp\u003e6.24\u0026plusmn;1.61\u003c/p\u003e\n \u003cp\u003e17.00\u0026plusmn;6.68\u003c/p\u003e\n \u003cp\u003e7.80\u0026plusmn;14.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.893939393939394%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e16(64.0%)\u003c/p\u003e\n \u003cp\u003e71.56\u0026plusmn;10.65\u003c/p\u003e\n \u003cp\u003e30.0(15.5\u0026plusmn;76.0)\u003c/p\u003e\n \u003cp\u003e5.44\u0026plusmn;1.04\u003c/p\u003e\n \u003cp\u003e147.92\u0026plusmn;25.17\u003c/p\u003e\n \u003cp\u003e74.12\u0026plusmn;10.24\u003c/p\u003e\n \u003cp\u003e12(48.0%)\u003c/p\u003e\n \u003cp\u003e2.01\u0026plusmn;0.86\u003c/p\u003e\n \u003cp\u003e23.26\u0026plusmn;4.44\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19(76.0%)\u003c/p\u003e\n \u003cp\u003e22(88.0%)\u003c/p\u003e\n \u003cp\u003e15(60.0%)\u003c/p\u003e\n \u003cp\u003e9(36.0%)\u003c/p\u003e\n \u003cp\u003e8(32.0%)\u003c/p\u003e\n \u003cp\u003e6(24.0%)\u003c/p\u003e\n \u003cp\u003e2(8.0%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e673.70\u0026plusmn;298.07\u003c/p\u003e\n \u003cp\u003e362.5(308.9-456.25)\u003c/p\u003e\n \u003cp\u003e2.21\u0026plusmn;0.18\u003c/p\u003e\n \u003cp\u003e1.87\u0026plusmn;0.51\u003c/p\u003e\n \u003cp\u003e83.72\u0026plusmn;32.84\u003c/p\u003e\n \u003cp\u003e210.85\u0026plusmn;48.19\u003c/p\u003e\n \u003cp\u003e1.22\u0026plusmn;0.21\u003c/p\u003e\n \u003cp\u003e236.93\u0026plusmn;135.89\u003c/p\u003e\n \u003cp\u003e105.48\u0026plusmn;21.7\u003c/p\u003e\n \u003cp\u003e35.04\u0026plusmn;4.82\u003c/p\u003e\n \u003cp\u003e1.87\u0026plusmn;1.17\u003c/p\u003e\n \u003cp\u003e3.70\u0026plusmn;1.13\u003c/p\u003e\n \u003cp\u003e7.09\u0026plusmn;3.66\u003c/p\u003e\n \u003cp\u003e13.77\u0026plusmn;4.73\u003c/p\u003e\n \u003cp\u003e10.79\u0026plusmn;13.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.712121212121213%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBP, blood pressure; iPTH, Intact parathyroid hormone. Kt/V (where K is the dialyzer clearance of urea; t is the dialysis time; and V is the volume of distribution of urea).\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp; Univariate logistic regression for death in hemodialysis patients with COVID-19.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.848375451263536%\" valign=\"top\" style=\"width: 47.8343%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.37906137184115%\" valign=\"top\" style=\"width: 36.9115%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.620938628158845%\" valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.848375451263536%\" valign=\"top\" style=\"width: 47.8343%;\"\u003e\n \u003cp\u003eUse of Central venous catheter\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFrailty,arbitrary units\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003cp\u003eAlkaline phosphatase, U/L\u003c/p\u003e\n \u003cp\u003eWhite blood cell count, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003cp\u003eThe percentage of lymphocyte(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.37906137184115%\" valign=\"top\" style=\"width: 36.9115%;\"\u003e\n \u003cp\u003e5.829(2.341-14.517)\u003c/p\u003e\n \u003cp\u003e2.370(1.600-3.510)\u003c/p\u003e\n \u003cp\u003e3.464(1.302-9.214)\u003c/p\u003e\n \u003cp\u003e0.984(0.963-1.007)\u003c/p\u003e\n \u003cp\u003e0.819(0.730-0.919)\u003c/p\u003e\n \u003cp\u003e1.013(0.997-1.028)\u003c/p\u003e\n \u003cp\u003e1.176(0.973-1.420)\u003c/p\u003e\n \u003cp\u003e0.902(0.827-0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.620938628158845%\" valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Multivariate logistic regression for death in hemodialysis patients with COVID-19.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"925\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.216216216216218%\" rowspan=\"2\" valign=\"top\" style=\"width: 19.4567%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.216216216216218%\" colspan=\"2\" valign=\"top\" style=\"width: 23.0221%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.216216216216218%\" colspan=\"2\" valign=\"top\" style=\"width: 23.0221%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.81081081081081%\" colspan=\"2\" valign=\"top\" style=\"width: 14.3633%;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.529411764705884%\" valign=\"top\" style=\"width: 16.4007%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.411764705882353%\" valign=\"top\" style=\"width: 6.6214%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91176470588235%\" valign=\"top\" style=\"width: 15.382%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.029411764705882%\" valign=\"top\" style=\"width: 7.6401%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91176470588235%\" valign=\"top\" style=\"width: 9.983%;\"\u003e\n \u003cp\u003eOR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.117647058823529%\" valign=\"top\" style=\"width: 4.3803%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.216216216216218%\" valign=\"top\" style=\"width: 19.4567%;\"\u003e\n \u003cp\u003eUse of Central venous catheter\u0026nbsp;\u0026nbsp;Frailty,arbitrary units\u003c/p\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe percentage of lymphocyte(%)\u003c/p\u003e\n \u003cp\u003eAlbumin, g/L\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.2972972972973%\" valign=\"top\" style=\"width: 16.4007%;\"\u003e\n \u003cp\u003e2.929(0.894-9.597)\u003c/p\u003e\n \u003cp\u003e1.945(1.230-3.077)\u003c/p\u003e\n \u003cp\u003e4.684(1.385-15.838)\u003c/p\u003e\n \u003cp\u003e0.951(0.869-1.041)\u003c/p\u003e\n \u003cp\u003e0.907(0.792-1.040)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.918918918918919%\" valign=\"top\" style=\"width: 6.6214%;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.10810810810811%\" valign=\"top\" style=\"width: 15.382%;\"\u003e\n \u003cp\u003e3.746(1.210-11.599)\u003c/p\u003e\n \u003cp\u003e1.974(1.261-3.090)\u003c/p\u003e\n \u003cp\u003e4.286(1.308-14.041)\u003c/p\u003e\n \u003cp\u003e0.934(0.853-1.022)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.108108108108109%\" valign=\"top\" style=\"width: 7.6401%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.10810810810811%\" valign=\"top\" style=\"width: 9.983%;\"\u003e\n \u003cp\u003e3.483(1.179-10.291\u003c/p\u003e\n \u003cp\u003e2.096(1.358-3.233)\u003c/p\u003e\n \u003cp\u003e3.984(1.254-12.657)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.702702702702703%\" valign=\"top\" style=\"width: 4.3803%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1, adjusted for Use of Central venous catheter, Frailty,Diabetes mellitus, The percentage of lymphocyte and Albumin; Model 2, adjusted for Use of Central venous catheter, Frailty,Diabetes mellitus, The percentage of lymphocyte; Model 3, adjusted for Use of Central venous catheter, Frailty,Diabetes mellitus.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4 Comparison of dry weight and blood test data pre- and post- COVID-19 in 134 survivors 30 days after the infection\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.876221498371336%\" valign=\"top\"\u003e\n \u003cp\u003eSurvived\u0026nbsp;spatients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.872964169381106%\" valign=\"top\"\u003e\n \u003cp\u003ePre-COVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45602605863192%\" valign=\"top\"\u003e\n \u003cp\u003ePost-COVID-19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003ealue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.876221498371336%\" valign=\"top\"\u003e\n \u003cp\u003eDry weight\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003cp\u003eSerum calcium, mmol/L\u003c/p\u003e\n \u003cp\u003eSerum phosphorus, mmol/L\u003c/p\u003e\n \u003cp\u003eIPTH, pg/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.872964169381106%\" valign=\"top\"\u003e\n \u003cp\u003e66.39\u0026plusmn;11.71\u003c/p\u003e\n \u003cp\u003e110.80\u0026plusmn;15.92\u003c/p\u003e\n \u003cp\u003e38.01\u0026plusmn;3.21\u003c/p\u003e\n \u003cp\u003e2.29\u0026plusmn;0.20\u003c/p\u003e\n \u003cp\u003e1.88\u0026plusmn;0.55\u003c/p\u003e\n \u003cp\u003e225.81\u0026plusmn;139.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.45602605863192%\" valign=\"top\"\u003e\n \u003cp\u003e66.16\u0026plusmn;12.02\u003c/p\u003e\n \u003cp\u003e104.32\u0026plusmn;12.83\u003c/p\u003e\n \u003cp\u003e35.35\u0026plusmn;3.36\u003c/p\u003e\n \u003cp\u003e2.27\u0026plusmn;0.20\u003c/p\u003e\n \u003cp\u003e1.69\u0026plusmn;0.52\u003c/p\u003e\n \u003cp\u003e268.25\u0026plusmn;155.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.794788273615636%\" valign=\"top\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Clinical biomarkers before the onset of SARS-CoV-2 infection to predict SARS-CoV-2 IgG response ranged 90-120 days after the COVID-19 infection.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.5985401459854%\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\" valign=\"top\"\u003e\n \u003cp\u003eIgG(+) n =72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"top\"\u003e\n \u003cp\u003eIgG(-) \u0026nbsp;n = 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.401459854014599%\" valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.5985401459854%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003cp\u003eBody mass index, kg/m2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDiabetic mellitus\u003c/p\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNeeded to hospitalization \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSerum calcium, mmol/L\u003c/p\u003e\n \u003cp\u003eSerum phosphorus, mmol/L\u003c/p\u003e\n \u003cp\u003eIntact parathyroid hormone , pg/ml\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHemoglobin, g/L\u003c/p\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003cp\u003eWhite blood cell count, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003cp\u003eC-reactive protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003cp\u003e63.08\u0026plusmn;12.62\u003c/p\u003e\n \u003cp\u003e23.81\u0026plusmn;2.99\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e2.29\u0026plusmn;0.21\u003c/p\u003e\n \u003cp\u003e1.83\u0026plusmn;0.58\u003c/p\u003e\n \u003cp\u003e220.39\u0026plusmn;118.12\u003c/p\u003e\n \u003cp\u003e110.51\u0026plusmn;12.43\u003c/p\u003e\n \u003cp\u003e37.83\u0026plusmn;3.20\u003c/p\u003e\n \u003cp\u003e6.21\u0026plusmn;1.66\u003c/p\u003e\n \u003cp\u003e6.45\u0026plusmn;8.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003cp\u003e61.29\u0026plusmn;11.55\u003c/p\u003e\n \u003cp\u003e24.10\u0026plusmn;3.56\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e2.27\u0026plusmn;0.18\u003c/p\u003e\n \u003cp\u003e1.96\u0026plusmn;0.46\u003c/p\u003e\n \u003cp\u003e222.81\u0026plusmn;117.47\u003c/p\u003e\n \u003cp\u003e111.42\u0026plusmn;20.32\u003c/p\u003e\n \u003cp\u003e38.57\u0026plusmn;2.68\u003c/p\u003e\n \u003cp\u003e6.49\u0026plusmn;1.69\u003c/p\u003e\n \u003cp\u003e10.87\u0026plusmn;20.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.401459854014599%\" valign=\"top\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hemodialysis, mortality, COVID-19, antibody responses, chronic kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-4428998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4428998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e: This single-center retrospective research aimed to depict the outcomes of coronavirus disease 2019 (COVID-19) in a cohort of patients from China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We traced the outcomes of 216 MHD patients admitted to the Aerospace Center Hospital of China during a COVID-19 wave . Clinical information was assembled and compared between survivors and non-survivors. Serum immunoglobulin M (IgM) and IgG antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were detected in MHD survivors 90–120 days post-infection. Clinical information was analyzed to find outfactors influencing mortality and antibody responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Among 216 patients, 207 (95.8%) were evaluated as COVID-19, and 25 (12.1%) passed away within 90 days. Fifty-five (26.6%) patients were needed of hospital admission. Non-survivors had lower levels of hemoglobin and serum albumin, higher levels of alkaline phosphatase, higher white blood cell counts and lower percentage of lymphocytes than survivors. Furthermore, the Clinical Frailty Scale (CFS) scores were higher in non-survivors significantly (p\u0026lt;0.05). Multivariate analysis showed that diabetes (HR 3.98, 95% CI 1.25–12.66, p=0.019), level of frailty according to the CFS (HR 2.10, 95% CI 1.36-3.23, p=0.001) and central venous catheter (CVC) use (hazard ratio [HR] 3.48, 95% confidence interval [CI] 1.18-10.29, p=0.024), had significant impacts on mortality. IgG was positive in 59.5%, and IgM was positive in 3.3% of patients (\u0026gt;1 sample/cutoff) between 90–120 days post-infection. Lower blood C-reactive protein (CRP) levels before the onset of SARS-CoV-2 infection were associated with significantly higher IgG antibody levels at 90-120 days after COVID-19 infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e: This study identified high mortality rates of SARS-CoV-2 infection among hemodialysis patients. Risk factors associated with mortality include diabetes, frailty, and CVC access. Low level of blood CRP before SARS-CoV-2 infection may predict high antibody responses during convalescence.\u003c/p\u003e","manuscriptTitle":"Risk factors for mortality and antibody responses in chronic hemodialysis patients with coronavirus disease 2019: a single-center experience in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-04 14:52:17","doi":"10.21203/rs.3.rs-4428998/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f04605dc-6930-4ddd-a0d9-41b4d2262cc1","owner":[],"postedDate":"June 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32447469,"name":"Biological sciences/Immunology"},{"id":32447470,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2024-11-04T09:24:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-04 14:52:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4428998","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4428998","identity":"rs-4428998","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}