Abstract
Introduction
The Omicron variant of the novel coronavirus (COVID-19) has been spreading more rapidly and is more infectious, posing a higher risk of death and treatment difficulty for patients undergoing hemodialysis. This study aims to explore the severity rate and risk factors for hemodialysis patients infected with the Omicron variant and to conduct a preliminary analysis of the clinical efficacy of drugs.
Methods
Clinical and biochemical indicators of 219 hemodialysis patients infected with the Omicron variant were statistically analyzed. The patients were divided into two groups based on whether they were severely ill or not, and multiple regression analysis was conducted to determine the risk factors for severe illness. The severely ill patients were then grouped based on discharge or death, and the treatment drugs were included as influencing factors for multiple regression analysis to determine the risk factors and protective factors for death of severely ill patients, and drug efficacy analysis was conducted.
Results
Analysis showed that diabetes, low oxygen saturation, and high C-reactive protein (CRP) were independent risk factors for severe illness in hemodialysis patients infected with the Omicron variant. A history of diabetes and high C-reactive significantly increased the risk of severe illness in patients (aOR: 1.450; aOR: 1.011), while a high oxygen saturation level can reduce this risk (aOR: 0.871). In addition, respiratory distress was an independent risk factor for death in severely patients, significantly reducing the probability of discharge for patients (aOR: 0.152). The drugs thymalfasin and Tanreqing significantly increased the probability of discharge for patients (aOR: 1.472; aOR: 3.104), with the latter having a higher correlation, but with a relatively longer effective course.
Conclusion
Hemodialysis patients infected with the Omicron variant of COVID-19 should pay special attention to their history of diabetes, CRP, and oxygen saturation levels, as well as respiratory distress symptoms, to reduce the risk of severe illness and death. In addition, thymalfasin and Tanreqing may be considered in treatment.
Keywords: Risk factors, Drug efficacy, Severe illness, Hemodialysis patients, Omicron variants
Introduction
The COVID-19 pandemic caused by the SARS-CoV-2 virus has had a significant impact on global health, leading to millions of infections and deaths worldwide. Patients with end-stage renal disease (ESRD) on maintenance hemodialysis are a vulnerable population due to their weakened immune system and comorbidities. They have been reported to have a higher risk of severe illness and death from COVID-19 [1, 2]. Additionally, the emergence of new variants of the virus has further complicated the management of COVID-19 in these patients. The Omicron variant, in particular, has been associated with increased transmissibility and breakthrough infections, leading to concerns about its impact on patients with ESRD on hemodialysis.
Recent studies have suggested that the Omicron variant has a higher transmission rate and may be associated with a higher risk of reinfection and vaccine breakthrough cases [3]. Other studies have suggested that older age, diabetes, cardiovascular disease, male gender, higher levels of inflammatory markers, and certain ethnic/racial groups may be at increased risk for Omicron variant infection in ESRD patients receiving hemodialysis [4]. However, little is known about the factors that may contribute to the severity of COVID-19 caused by this variant in patients on dialysis. Understanding the risk factors associated with severe COVID-19 caused by the Omicron variant and the effectiveness of treatment options is essential for improving patient outcomes and guiding clinical decision-making. To date, several studies have investigated the risk factors associated with severe COVID-19 in patients with ESRD on hemodialysis [5, 6]. However, there are limited data on the risk factors for severe illness and the effectiveness of different treatments in patients infected with the Omicron variant.
Therefore, the purpose of this study is to identify the risk factors associated with severe illness in ESRD patients infected with the Omicron variant and to evaluate the effectiveness of various treatments in this population. The study analyzed data from 219 ESRD patients infected with the Omicron variant, of whom 75 were severely ill. Of these severely ill patients, 53 were discharged after treatment with various medications. By identifying the risk factors and effective treatments for ESRD patients infected with the Omicron variant, this study can help healthcare providers optimize patient care and improve outcomes in this vulnerable population.
Subjects and Methods
Patients
This study retrospectively collected 224 cases of COVID-19 Omicron variant infection in patients with ESRD admitted to the Nephrology Department of Shanghai Sixth People’s Hospital from April to July 2022 (during the Omicron pandemic in Shanghai). Among them, 219 cases of patients (133 males, 86 females) who met the criteria were screened and specimens were collected immediately on the day of admission. The selected patients needed to meet the following criteria at the same time: (1) diagnosed with ESRD and excluding reversible factors; (2) receiving hemodialysis or peritoneal dialysis for more than 3 months; (3) first-time infection with the novel coronavirus; (4) confirmed as infected with the Omicron variant of COVID-19; (5) timely, complete, and accurate recording of relevant data. In this study, patients who met either (1) or (2) and met criterion (3) were diagnosed with Omicron variant infection of COVID-19: (1) positive result on nucleic acid RT-PCR test for the novel coronavirus; (2) positive findings on chest CT based on radiological criteria for COVID-19 infection; (3) whole-genome sequencing and sequence analysis of the novel coronavirus showed the Omicron variant.
This study was approved by the Ethics Committee of Shanghai Jiao Tong University-Affiliated Sixth People’s Hospital. All subjects were interviewed, signed a written informed consent form for the study, and provided medical histories of hypertension, diabetes, respiratory diseases, renal disease duration, and duration of dialysis, as well as symptoms related to COVID-19 such as cough and fever. It is important to emphasize that all screened patients have received a single dose of the same type of inactivated COVID-19 vaccine within a period of 2 months.
Research Method
Data collection and analysis: the general information collected and analyzed for patients includes gender, age, dialysis duration, comorbidities, and clinical features, as well as laboratory biochemistry data and clinical drug use during hospitalization. Comorbidities of interest include hypertension, diabetes, respiratory system diseases, nervous system diseases, cardiovascular diseases, and malignant tumors. Clinical features of interest include cough, sputum, fatigue, respiratory distress, muscle pain, fever, renal anemia, and metabolic disorders. The laboratory biochemistry data collected mainly include serum albumin (ALB), hemoglobin (Hb), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), oxygen saturation (SaO2), total CO2 (TCO2), serum potassium (K), serum calcium (Ca), serum phosphorus (P), C-reactive protein (CRP), white blood cell count (WBC), lymphocyte count (LY), neutrophil count (NEUT), platelet count (PLT), and D-dimer. To evaluate changes in the level of coronavirus in patients, the study collected records of the novel coronavirus nucleic acid RT-PCR test: Ct values of the ORF1ab gene (ORF1ab-Ct) and the N gene (N-Ct). In addition, for severely ill patients, this study also collected their medication treatment history during hospitalization. The types of treatment used include thymalfasin, interferon, remdesivir, globulin, hormones, Tanreqing, antibiotics, high flow, and CRRT. Indicators such as whether the patient received such treatment and the duration of treatment were also recorded.
Calculation and analysis: Patients were divided into severe (n = 78) and non-severe (n = 141) groups based on whether they required intensive care or were severely ill within 48 h of admission. Univariate analysis was performed on demographic, clinical, and biochemical indicators at admission to identify indicators that were statistically significant (p < 0.05) and enter them into a logistic regression model. Confounding factors were removed to conduct multivariate regression analysis to determine the risk factors for severe infection of COVID-19 Omicron variant in dialysis patients. Subsequently, based on whether the patient improved and was discharged after treatment, the severe group was divided into discharge group (n = 53) and death group (n = 25). Univariate analysis was performed on demographic, clinical indicators and treatment drugs of the patients to identify indicators that were statistically significant (p < 0.05) and enter them into a logistic regression model. Confounding factors were removed to conduct multivariate regression analysis to determine protective drugs that were significantly correlated with patient improvement and discharge. Furthermore, according to the determined duration of treatment with the protective drugs, the corresponding drug course was divided into three groups (group 1: <3 days, group 2: 3–7days, group 3: >7 days). The same research methods were used to analyze the effective treatment duration of the protective drugs.
Statistical Analysis
All data were analyzed and processed using SPSS 22.0 statistical software. The normal distribution of all data was first checked using the Kolmogorov-Smirnov test before statistical analysis was performed. Normally distributed data are expressed as mean ± standard deviation (x ± s), while non-normally distributed data are expressed as median (with interquartile range). Two independent-sample t tests or non-parametric Mann-Whitney tests were used to compare the two groups, and non-parametric Kruskal-Wallis tests were used to compare multiple groups. Count data were expressed as percentages (%), and two independent-sample t tests and χ2 tests were used to compare between groups, selecting variables with statistically significant differences. Multivariate logistic regression was used to analyze risk factors and correlations for indicators with statistical significance, including odds ratio (OR), 95% confidence interval (CI), and the significance level p value. Unless otherwise specified, a p value of less than 0.05 was used as the threshold for statistical significance.
Results
Risk Factors for Severe Illness
Baseline information of 219 patients (Table 1) indicates that dialysis patients infected with the Omicron variant of COVID-19 are of older age (mean 65.03 years) and have longer duration of dialysis (mean 5.25 years). A high proportion of patients have comorbidities such as diabetes, hypertension, and cardiovascular diseases, with hypertension being the most prevalent (72%). Although COVID-19 primarily affects the respiratory system, the proportion of patients with primary respiratory system diseases is not high (8%) among those infected. Additionally, the typical clinical manifestations of patients mainly include cough, calcium-phosphate metabolism disorder, and renal anemia, accounting for 52%, 70%, and 82%, respectively. On the other hand, fever and respiratory distress, which are the typical symptoms of COVID-19 pneumonia, are not highly prevalent in the study population, accounting for 24% and 17%, respectively. Laboratory examination indicators such as BUN, Scr, CRP, and D-dimer were significantly elevated upon admission, while the mean values of TCO2 and LY were significantly decreased.
Table 1.
Comparison of clinical and laboratory indicators between severe and non-severe groups
| Variables | Total of COVID-19 (n = 219) | Severe (n = 78) | Non-severe (n = 141) | p value |
|---|---|---|---|---|
| Male, n (%) | 133 | 51 (68) | 82 (57) | 0.201 |
| Age, years | 65.03±1.36 | 68.19±1.42 | 61.86±1.27 | 0.003 |
| Dialysis duration, years | 5.22±0.48 | 5.38±0.58 | 5.06±0.40 | 0.776 |
| Dialysis break, days | 3.52±0.21 | 3.77±0.27 | 3.27±0.14 | 0.167 |
| Comorbidities, n (%) | ||||
| Hypertension | 158 (72) | 57 (76) | 101 (69) | 0.645 |
| Respiratory system diseases | 18 (8) | 11 (15) | 7 (5) | 0.020 |
| Cardiovascular diseases | 66 (30) | 21 (28) | 45 (31) | 0.644 |
| Diabetes | 68 (31) | 35 (47) | 33 (23) | 0.006 |
| Nervous system diseases | 21 (10) | 11 (15) | 10 (7) | 0.147 |
| Malignant tumors | 17 (8) | 9 (12) | 8 (6) | 0.117 |
| Symptoms, n (%) | ||||
| Cough | 114 (52) | 52 (69) | 62 (43) | 0.053 |
| Sputum | 75 (34) | 40 (53) | 35 (24) | 0.002 |
| Fatigue | 38 (17) | 16 (21) | 22 (15) | 0.351 |
| Muscle pain | 21 (10) | 8 (11) | 13 (9) | 0.811 |
| Respiratory distress | 37 (17) | 26 (35) | 11 (9) | 0.001 |
| Fever | 53 (24) | 27 (36) | 26 (18) | 0.052 |
| Metabolic disorders | 153 (70) | 49 (65) | 104 (72) | 0.229 |
| Renal anemia | 179 (82) | 60 (80) | 119 (83) | 0.484 |
| Analytical values | ||||
| ALB, g/L | 36.29±0.41 | 35.15±0.55 | 37.43±0.36 | 0.002 |
| Hb, g/L | 101.33±2.02 | 101.01±2.42 | 101.65±1.57 | 0.531 |
| ALT, U/L | 38.42±17.23 | 63.12±32.42 | 13.72±0.82 | 0.166 |
| AST, U/L | 79.39±38.12 | 140.42±71.22 | 18.36±1.03 | 0.007 |
| BUN, mmol/L | 33.35±1.21 | 33.72±1.79 | 32.98±0.88 | 0.283 |
| Scr, µmol/L | 1,108.12±47.52 | 1,050.26±53.83 | 1,167.18±41.93 | 0.058 |
| SaO2, % | 94.94±0.97 | 92.10±1.43 | 97.79±0.62 | 0.001 |
| TCO2, mmol/L | 17.54±0.51 | 17.26±0.66 | 17.82±0.49 | 0.788 |
| K, mmol/L | 6.35±1.11 | 6.75±1.73 | 5.95±0.89 | 0.886 |
| Ca, mmol/L | 2.22±0.01 | 2.21±0.03 | 2.22±0.02 | 0.965 |
| P, mmol/L | 2.30±0.09 | 2.28±0.10 | 2.32±0.07 | 0.840 |
| CRP, mg/L | 29.61±5.23 | 42.83±7.17 | 16.38±2.66 | 0.001 |
| WBC, 109/L | 5.36±0.21 | 5.72±0.36 | 4.99±0.16 | 0.145 |
| LY, 109/L | 0.85±0.07 | 0.72±0.04 | 0.98±0.10 | 0.014 |
| NEUT, 109/L | 4.15±0.40 | 4.33±0.35 | 3.96±0.49 | 0.056 |
| PLT, 109/L | 149.37±6.71 | 143.78±7.01 | 154.97±5.37 | 0.138 |
| D-dimer, mg/L | 2.19±0.31 | 2.70±0.38 | 1.69±0.22 | 0.001 |
| ORF1ab-Ct | 20.78±0.68 | 21.55±0.65 | 20.02±0.71 | 0.873 |
| N-Ct | 19.85±0.71 | 20.50±0.70 | 19.20±0.71 | 0.884 |
Except where indicated otherwise, values are the mean ± SD.
This study classified patients into severe (n = 78) and non-severe (n = 141) groups based on whether they required intensive care or were in severe condition within 48 h of admission. Single-factor analysis of variance between the two groups using Table 1 revealed that the mean age, CRP, and D-dimer values of severe patients were significantly higher than those of non-severe patients (68.19 ± 1.42 years vs. 61.86 ± 1.27 years, 42.83 ± 7.17 mg/L vs. 16.38 ± 2.66 mg/L, 2.70 ± 0.38 mg/L vs. 1.69 ± 0.22 mg/L, respectively), while the mean values of ALB, SaO2, and LY were significantly lower than those of non-severe patients (35.15 ± 0.55 g/L vs. 37.43 ± 0.36 g/L, 92.10 ± 1.43% vs. 97.79 ± 0.62%, 0.72 ± 0.04 109/L vs. 0.98 ± 0.10 109/L, respectively). These differences were statistically significant (p < 0.05). In addition, the proportion of patients with underlying respiratory system and diabetes mellitus, as well as the proportion of patients with cough and respiratory distress symptoms, was significantly higher in the severe group than in the non-severe group (53% vs. 24%, 35% vs. 9%, 15% vs. 5%, 47% vs. 23%), and these differences were statistically significant. It should be noted that the proportion of severe patients with the original typical symptoms of COVID-19, cough, and fever was also significantly higher than that of non-severe patients, but the difference was not statistically significant (p = 0.053, p = 0.052).
Based on the single-factor inter-group difference analysis above, 10 variables were selected with statistically significant differences among groups (age, respiratory system disease, diabetes, cough, respiratory distress, ALB, SaO2, CRP, LY, and D-dimer). Furthermore, a multivariate logistic regression analysis was performed to adjust for potential confounding factors, including gender, other comorbidities, and biochemical indicators. Table 2 shows the analysis results based on different types of indicators; only variables determined to be risk factors are listed. The results indicate that diabetes, low SaO2, and high CRP levels are independent risk factors for severe COVID-19 in hemodialysis patients. Among them, the risk of severe illness in diabetic patients is 1.45 times that of non-diabetic patients (adjusted OR [aOR] = 1.45, 95% CI: 1.016–1.614, p = 0.036), and the risk of severe illness in patients with higher CRP levels increased by 1.01 (aOR = 1.011, CI: 1.001–1.021, p = 0.026). Additionally, patients with lower SaO2 upon admission were more likely to develop severe pneumonia (aOR = 0.871, CI: 0.788–0.962, p = 0.003), with their risk increasing by 1.15 times.
Table 2.
Risk factors for severe illness in hemodialysis patients infected with the Omicron variant
| Variables | B | SE | p value | aOR | 95% CI |
|---|---|---|---|---|---|
| Comorbidities | |||||
| Diabetes | 0.725 | 0.356 | 0.036 | 1.450 | 1.016∼1.614 |
| Analytical values | |||||
| SaO2 | −0.139 | 0.051 | 0.003 | 0.871 | 0.788∼0.962 |
| CRP | 0.011 | 0.005 | 0.026 | 1.011 | 1.001∼1.021 |
Risk or Protective Factors for Death
After targeted treatment, 26 out of 219 hospitalized patients died, of which 25 were in the severe group and only one had mild symptoms upon admission before deteriorating and passing away. To study the factors and drug-related correlations associated with patient recovery or death, we focused on the severe group and further divided 78 severe patients into two subgroups based on discharge (n = 53) and death (n = 25), with specific grouping information presented in Table 3. Subsequently, one-way analysis of variance was conducted to assess differences between discharged and death in terms of demographic and clinical characteristics, comorbidities, and treatment medications. The results demonstrated a significant disparity in the average age between the deceased group (70.16 ± 2.82) and the discharged group (67.26 ± 1.62), highlighting the heightened vulnerability of older patients to the condition. Furthermore, a markedly greater percentage of patients in the deceased group presented respiratory distress symptoms compared to the discharged group (64% vs. 18%). However, the proportion of patients treated with thymalfasin, hormones, and Tanreqing injection was significantly lower in the death group than in the discharged group (56% vs. 87%, 28% vs. 55%, 40% vs. 85%). All of these differences were statistically significant (p < 0.05). It should be noted that variables such as respiratory system diseases, diabetes, and cough, which showed significant statistical differences between the severe and non-severe patient groups, did not exhibit significant differences between discharged group and death group after treatment.
Table 3.
Comparison of clinical indicators and drugs between discharge and death groups in severe patients
| Variables | Severe (n = 78) | p value | |
|---|---|---|---|
| discharge (n = 53) | death (n = 25) | ||
| Male, n (%) | 36 (68) | 15 (60) | 0.611 |
| Age, years | 67.26±1.62 | 70.16±2.82 | 0.003 |
| Dialysis duration, years | 5.57±0.69 | 5.04±1.11 | 0.776 |
| Dialysis break, days | 3.83±0.32 | 3.64±0.52 | 0.167 |
| Comorbidities, n (%) | |||
| Hypertension | 42 (80) | 15 (60) | 0.101 |
| Respiratory system diseases | 7 (13) | 4 (16) | 0.738 |
| Cardiovascular diseases | 25 (47) | 16 (64) | 0.225 |
| Diabetes | 23 (43) | 10 (40) | 0.811 |
| Nervous system diseases | 6 (11) | 5 (20) | 0.316 |
| Malignant tumors | 19 (36) | 7 (28) | 0.136 |
| Symptoms, n (%) | |||
| Cough | 34 (64) | 11 (44) | 0.140 |
| Sputum | 29 (55) | 11 (44) | 0.469 |
| Fatigue | 12 (23) | 4 (16) | 0.564 |
| Muscle pain | 6 (11) | 2 (8) | 0.916 |
| Respiratory distress | 10 (18) | 16 (64) | 0.002 |
| Fever | 15 (28) | 12 (48) | 0.126 |
| Metabolic disorders | 30 (56) | 19 (76) | 0.133 |
| Renal anemia | 39 (74) | 21 (84) | 0.396 |
| Drugs, n (%) | |||
| Thymalfasin | 46 (87) | 14 (56) | 0.004 |
| Interferon | 37 | 12 | 0.081 |
| Remdesivir | 18 | 9 | 0.921 |
| Globulin | 37 | 14 | 0.308 |
| Hormones | 29 (55) | 7 (28) | 0.031 |
| Tanreqing | 45 (85) | 10 (40) | 0.001 |
| Antibiotics | 27 | 13 | 0.842 |
| High flow | 16 | 13 | 0.081 |
| CRRT | 28 | 17 | 0.230 |
Except where indicated otherwise, values are the mean ± SD.
After the above univariate analysis, variables with statistically significant differences between groups, including age, respiratory distress, thymalfasin, hormones, and Tanreqing, were selected for multivariate logistic regression analysis, with potential confounding factors adjusted. The results showed that respiratory distress was an independent risk factor for death in critically ill patients, while thymalfasin and Tanreqing were protective factors. Table 4 shows the ORs and 95% CIs of independent risk factors. The aOR for respiratory distress was 0.152 (CI: 0.040–0.576, p = 0.006), indicating that critically ill patients with respiratory distress before hospitalization had a 6.58 times higher risk of death compared to those without respiratory distress. The aOR for thymalfasin was 1.472 (CI: 1.168–2.725, p = 0.030), indicating that critically ill patients treated with thymalfasin during hospitalization had a 1.47 times higher chance of being discharged compared to those not treated with this drug. The aOR for Tanreqing was 3.104 (CI: 1.585–4.456, p = 0.010), indicating that critically ill patients treated with Tanreqing during hospitalization had a 3.1 times higher chance of being discharged compared to those not treated with this drug.
Table 4.
Risk or protective factors for death in hemodialysis patients infected with the severe Omicron
| Variables | B | SE | p value | aOR | 95% CI |
|---|---|---|---|---|---|
| Symptoms | |||||
| Respiratory distress | −1.884 | 0.680 | 0.006 | 0.152 | 0.040∼0.576 |
| Drugs | |||||
| Thymalfasin | 1.631 | 0.753 | 0.030 | 1.472 | 1.168∼2.725 |
| Tanreqing | 1.898 | 0.733 | 0.010 | 3.104 | 1.585∼4.456 |
Effective Course of Treatment
In order to further explore the effective treatment of two drugs in reducing the risk of death, patients in two drug groups were divided into three study groups based on the time of improvement and discharge or death: <3 days, 3 days∼7 days, and >7 days. Preliminary univariate analysis was performed on the information of each study group, and Table 5 shows the analysis results. It indicates that for severe patients treated with thymalfasin, most (39.6%) improved and were discharged within 3–7 days, while for severe patients treated with Tanreqing, most (55.6%) improved and were discharged after more than 7 days. Therefore, it can be concluded that both drugs have significant statistical efficacy, and traditional Chinese medicine Tanreqing is more effective in reducing the risk of death and improving the discharge rate, but the effective treatment duration is relatively longer than that of thymalfasin.
Table 5.
Effective course of treatment in hemodialysis patients infected with the severe Omicron
| Treatment duration | Discharge (n = 53) | Death (n = 25) | p value |
|---|---|---|---|
| Thymalfasin, n (%) | |||
| <3 days | 8 (15) | 4 (16) | 0.843 |
| 3 days∼7 days | 21 (40) | 4 (12) | 0.031 |
| >7 days | 17 (32) | 6 (24) | 0.095 |
| Tanreqing, n (%) | |||
| <3 days | 7 (16) | 3 (12) | 0.669 |
| 3 days∼7 days | 14 (31) | 4 (16) | 0.068 |
| >7 days | 25 (56) | 3 (12) | 0.012 |
Discussion
The COVID-19 pandemic has caused serious morbidity and mortality rates worldwide, particularly among vulnerable populations such as hemodialysis patients. The Omicron variant of COVID-19 adds another layer of complexity to this already challenging situation due to its high transmissibility and potential for immune evasion [7, 8]. This study aimed to investigate the risk factors and potential treatment options for severe COVID-19 in hemodialysis patients infected with the Omicron variant. Data from 219 dialysis patients admitted to hospitals during the Omicron variant outbreak in Shanghai in 2022 were collected and screened, and an accurate database of study subjects was established. Multivariate regression analysis identified diabetes, low SaO2, and high CRP levels as independent risk factors for severe illness in hemodialysis patients infected with the COVID-19 Omicron variant, while respiratory distress was an independent risk factor for death in severe cases. Thymalfasin and the traditional Chinese medicine Tanreqing were potential protective drugs for severe cases, with Tanreqing showing a higher degree of correlation with a reduced risk of death and an increased discharge rate, although the effective treatment duration for Tanreqing was relatively longer than that for thymalfasin.
Diabetes is one of the most common comorbidities in COVID-19 patients. A study conducted in a hemodialysis center in Wuhan, China, found that age, diabetes, and low ALB levels were independent predictors of severe COVID-19 [9]. Against the background of the Omicron variant of COVID-19, a recent study in South Africa found that diabetes was an important risk factor for hospitalization and death among patients infected with the Omicron variant [10]. In our study, 31% of dialysis patients with COVID-19 Omicron had diabetes. Regression analysis showed that dialysis patients with diabetes who were infected with the COVID-19 Omicron variant had a 1.45 times higher risk of severe illness (aOR = 1.45, CI:1.016∼1.614), further confirming the independent role of diabetes as a risk factor for severe illness in hemodialysis patients infected with the Omicron variant. However, the underlying mechanisms of diabetes as a risk factor for severe COVID-19 in hemodialysis patients are not fully understood. Nevertheless, it is well known that diabetes is associated with immune dysfunction, including impaired T-cell and macrophage function, elevated levels of inflammatory cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), and these immune abnormalities may contribute to the development of inflammation and cytokine storms in severe cases of COVID-19 [11, 12].
In the general population of COVID-19 patients, low SaO2 has been identified as a risk factor for mortality. In a study of 201 patients with COVID-19, low oxygen saturation at admission was associated with an increased risk of ICU admission, mechanical ventilation, and death [13]. Another study showed that dialysis patients infected with the coronavirus had a higher proportion of severe cases among those with low SaO2 (<90%) compared to those with normal saturation (≥90%) [14]. In our study, the mean oxygen saturation in the severe patient group was significantly lower (92.1%), with 45% of patients having saturation levels below 90%. Regression analysis showed that dialysis patients with lower SaO2 had a 1.15-fold increased risk of severe illness from COVID-19 (aOR = 0.871, CI: 0.788–0.962, p = 0.003), further confirming the independent role of low SaO2 as a risk factor for severe illness in hemodialysis patients infected with the Omicron variant of COVID-19. Dialysis patients may have oxygenation disorders due to lung disease or other reasons, leading to low SaO2 and an increased risk of respiratory failure and cardiovascular events after infection [15]. In addition, dialysis patients may have other complications related to hypoxemia, such as pulmonary thromboembolism and pulmonary heart disease, further increasing the incidence of severe cases [16]. Therefore, after dialysis patients are infected with the Omicron variant of COVID-19, SaO2 should be closely monitored, and hypoxemia should be corrected in a timely manner to prevent the occurrence of severe cases.
CRP levels rapidly increase during infections and inflammatory diseases. A study of dialysis patients infected with COVID-19 found that high CRP levels were one of the independent risk factors affecting their prognosis [17]. Another multicenter study conducted on dialysis patients in Wuhan also came to similar conclusions [18]. These research results indicate that CRP levels are one of the important indicators for predicting the prognosis of dialysis patients after contracting COVID-19. Our study results show that the mystery of dialysis patients with high CRP levels contracting COVID-19 is associated with a 1.01-fold increase in the risk of developing severe illness (aOR = 1.01, CI: 1.001–1.021, p = 0.026), confirming the role of high CRP as an independent risk factor for severe illness in hemodialysis patients infected with the Omicron variant. Although the exact mechanism by which CRP levels increase in dialysis patients with COVID-19 has not yet been determined, some studies suggest that this may be related to the sustained presence of inflammation and the decreased function of the body’s immune system [19]. During infections, the body’s immune system releases a series of inflammatory mediators, triggering an inflammatory response. When the inflammatory response lasts too long, the immune system’s function in the body may decrease, making the condition of dialysis patients with COVID-19 more severe. In addition, dialysis patients usually have many chronic diseases and inflammation, which may cause an increase in CRP levels, thereby increasing the risk of severe illness after contracting COVID-19 [20]. Therefore, doctors should pay attention to monitoring the CRP levels of dialysis patients after contracting COVID-19 and take appropriate treatment measures if necessary to reduce the risk of developing severe illness.
Interestingly, our study found that although respiratory distress was more common in critically ill patients (35% vs. 9%, p = 0.001), it was not an independent risk factor for severe illness in hemodialysis patients infected with the Omicron variant. This may be because most patients experience respiratory difficulties in the early stages of COVID-19, which may be due to coughing or phlegm symptoms, and may not indicate widespread lung infection or dysfunction. However, for critically ill patients, respiratory distress is often a sign that mechanical ventilation is needed, indicating a very serious condition [21]. Our study also confirmed that respiratory distress was an independent risk factor for death in critically ill patients. The risk of death in critically ill patients with respiratory distress during hospitalization was 6.58 times higher than that of patients without this symptom (aOR = 0.152, CI: 0.040–0.576, p = 0.006). The mechanism by which respiratory distress leads to death in dialysis patients with severe COVID-19 Omicron infection is not yet fully understood. Some studies have shown [22, 23] that pulmonary inflammation and injury caused by COVID-19 infection may be the main cause of respiratory distress. Especially in critically ill patients, the occurrence of respiratory distress often indicates severe hypoxemia, which has exceeded the body’s compensatory capacity, greatly increasing the risk of death. Therefore, after dialysis patients are infected with COVID-19 Omicron, especially in critically ill patients, close monitoring of respiratory status should be carried out to reduce the risk of respiratory distress and death.
Regarding drug treatment options, thymosin has been shown to have an immunomodulatory effect and may help alleviate the inflammatory response and improve immune function in COVID-19 patients. Some studies have demonstrated that thymosin, as well as thymosin alpha-1, is associated with decreased mortality and improved clinical outcomes in COVID-19 patients, and has been identified as the key protective factor for the treatment of COVID-19 patients [24, 25]. Similarly, Tanreqing (traditional Chinese medicine formulas) has been used to treat respiratory infections and has been shown to have antiviral and anti-inflammatory effects [26]. A retrospective cohort study of 60 COVID-19 patients found that the use of Tanreqing was associated with reduced mortality and improved clinical outcomes and could serve as a potential adjunctive therapy for mild to moderate COVID-19 patients [27]. Similarly, our study results suggest that thymosin and Tanreqing are potential protective agents for COVID-19 patients with the Omicron variant who undergo hemodialysis. The likelihood of clinical improvement and discharge from the hospital was 1.47 times higher in severe patients treated with thymosin during hospitalization compared to those who did not receive this drug (aOR = 1.472, CI: 1.168∼2.725, p = 0.030) and 3.1 times higher in severe patients treated with Tanreqing compared to those who did not receive this drug (aOR = 3.104, CI: 1.585∼4.456, p = 0.010). Moreover, Tanreqing was more strongly associated with reducing the risk of death and increasing the rate of discharge. However, our study results also suggest that the effective course of treatment for Tanreqing is relatively longer compared to thymosin. Most severe patients treated with thymosin (39.6%) showed clinical improvement and were discharged within 3–7 days, while the majority of severe patients treated with Tanreqing (55.6%) took more than 7 days to show clinical improvement and be discharged.
Possible reasons for this phenomenon may be due to the different components and mechanisms of action of the two drugs. Thymalfasin is a thymic peptide drug, whose main components are thymic peptides and fentizol. Thymic peptides are peptide substances secreted by thymic cells, which have the function of stimulating the immune system, enhancing the vitality and quantity of immune cells, and strengthening the immune system’s resistance to pathogens. Fentizol is a non-steroidal anti-inflammatory drug that can inhibit inflammation and relieve structural changes in the lungs caused by inflammation. These components act together on the human body and can quickly relieve symptoms of phlegm and heat [28]. In addition, thymalfasin has been shown to enhance the function of natural killer cells and T cells. T cells are an important component of the immune system, and they can also reduce inflammation by inhibiting the production of pro-inflammatory cytokines [29]. Moreover, it is a chemical drug with a single component and a fast absorption rate, so its onset time is relatively short. The main components of Tangerine-Heat-Clearing Formula, a traditional Chinese medicine for treating phlegm and heat, are Scutellaria baicalensis, Platycodon grandiflorum, Forsythia suspensa, Lonicera japonica, and others. These components have the effects of clearing heat and detoxifying, resolving phlegm, and relieving cough [30]. Among them, Scutellaria baicalensis, Platycodon grandiflorum, and Forsythia suspensa have the effects of clearing heat and detoxifying, resolving phlegm, and relieving cough, as well as anti-allergic and bronchodilating effects. Lonicera japonica has been shown to have antiviral activity against various respiratory viruses [31, 32]. These components act together on the human body, can reduce the body’s inflammatory response, promote the discharge of respiratory tract phlegm, and thus relieve symptoms of phlegm and heat [33, 34]. However, due to the complexity of the preparation and components of traditional Chinese medicine, it takes some time for the body to absorb them, so the onset time is relatively slow.
It should be noted that although our study suggests that thymalfasin and Tanreqing may have a protective effect on hemodialysis patients infected with the Omicron variant of COVID-19, further research is needed to confirm these findings. Randomized controlled trials are needed to evaluate the effectiveness and safety of these treatments in this population and to determine the optimal dosage and duration of treatment. In addition, due to the small sample size, retrospective experimental design, and limitations only to hemodialysis patients, our study results may not be generalizable to other patient populations. Future studies with larger sample sizes and prospective designs are needed to further explore the risk factors and treatment options for hemodialysis patients with severe COVID-19.
Acknowledgments
The author extraordinarily thanks the Department of Nephrology of Shanghai Jiao Tong University-Affiliated Sixth People’s Hospital for providing the opportunity to collect patient data. Special thanks also go to Professor Guo Yongping for offering helpful advice in preparing this paper. In addition, the author would also like to thank the Department of Nephrology and Rheumatology of East Branch of the Sixth People’s Hospital for providing a good environment for data calculation and analysis.
Statement of Ethics
The research protocol was reviewed and approved by the Shanghai Sixth People’s Hospital Ethics Committee (approval number 20220139). All participants provided written informed consent prior to their participation in the study. Any personal data collected during the research process were anonymized and securely stored to ensure the confidentiality and privacy of the participants. All experimental procedures were performed following relevant guidelines and regulations.
Conflict of Interest Statement
The authors have no conflicts of interest to declare.
Funding Sources
This work was supported by the Seed Fund of Shanghai University of Medicine and Health Sciences [NO. HMSF-17-22-016] and the Scientific Research Foundation of Shanghai Sixth People’s Hospital East [NO. DY2018006].
Author Contributions
All authors were involved in drafting the article or revising it critically for intellectual content, and all authors approved the manuscript to be submitted. Yan Wu and Lingling He had full access to all of the data in the study and take responsibility for the integrity of the data. Yan Wu was responsible for the study conception and design. Lingling He and Yongping Guo were responsible for the acquisition of data. Yan Wu, Lingling He, and Niansong Wang were responsible for the analysis and interpretation of data. All patients provided their detailed clinical data.
Funding Statement
This work was supported by the Seed Fund of Shanghai University of Medicine and Health Sciences [NO. HMSF-17-22-016] and the Scientific Research Foundation of Shanghai Sixth People’s Hospital East [NO. DY2018006].
Data Availability Statement
Extra data are available by emailing zhaoguoyongping@163.com. Data are not publicly available due to ethical reasons. Further inquiries can be directed to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Extra data are available by emailing zhaoguoyongping@163.com. Data are not publicly available due to ethical reasons. Further inquiries can be directed to the corresponding author.