Abstract
Background
Home-based dialysis, including peritoneal dialysis (PD) and home hemodialysis (HHD), has been suggested to reduce SARS-CoV-2 infection rates and improve outcomes compared to in-center dialysis, yet evidence from China remains scarce.
Objective
To investigate the risk and prognosis of Omicron infection among patients receiving home-based dialysis versus in-center dialysis during the Omicron surge in Shanghai, China.
Methods
This single-center retrospective cohort study included patients undergoing maintenance dialysis (home-based dialysis or in-center dialysis) at Ren Ji Hospital from December 1, 2022, to January 31, 2023. The primary endpoint was Omicron infection rate; secondary endpoints included infection timeline, all-cause mortality, and associated risk factors. Logistic regression was used to identify independent predictors.
Results
A total of 465 patients were included: 267 in the home-based dialysis group (263 PD, 4 HHD) and 198 in the in-center dialysis group. The infection rate was significantly lower in the home-based dialysis group than in the in-center dialysis group (52.1% vs. 88.4%, P < .001). Home-based dialysis was an independent protective factor against infection. No significant difference was found in all-cause mortality between home-based dialysis and in-center dialysis groups (5.2% vs. 7.6%, P = .304). Advanced age, heart failure, and low serum albumin were associated with increased risk of death following infection.
Conclusions
Home-based dialysis significantly reduced the risk of Omicron infection without adversely affecting survival outcomes. Expanding home-based dialysis may have public health implications for dialysis care during future pandemics.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-025-04453-0.
Keywords: Home dialysis, Peritoneal dialysis, Hemodialysis, SARS-CoV-2, Omicron variant, Infection rate, Mortality
Introduction
The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed significant challenges to healthcare systems worldwide [1, 2]. Since its emergence in late 2019, SARS-CoV-2 has undergone multiple mutations. Among them, the Omicron variant became the predominant strain in China by the end of 2022, characterized by its high transmissibility and strong immune evasion capacity [3, 4]. These characteristics have led to widespread and rapid community transmission, placing dialysis-dependent patients at increased risk.
Patients with end-stage renal disease (ESRD) represent a particularly vulnerable population due to compromised immune function, multiple comorbidities, and the necessity of regular renal replacement therapy [5–7]. Studies have demonstrated that dialysis patients face a higher risk of SARS-CoV-2 infection and related complications, including hospitalization and mortality, compared to the general population [8, 9]. These risks place considerable pressure on existing dialysis care systems.
In-center dialysis remains the most widely used modality of dialysis in China. However, with the growing prevalence of chronic kidney disease (CKD) and the increasing number of ESRD patients, the reliance on in-center dialysis alone is insufficient to meet patient needs [10–12]. Home-based dialysis, including peritoneal dialysis (PD) and home hemodialysis (HHD), has been increasingly recognized as a feasible and potentially superior alternative [13–15]. Our center has pioneered the first successful implementation of HHD in mainland China. Since 2018, we have successfully trained and established a cohort of patients on HHD, addressing a significant gap in home-based dialysis options previously unavailable in mainland China [16, 17].
Home-based dialysis offers several advantages, including reduced hospital visits, increased flexibility in treatment schedules, and potential improvements in quality of life [18, 19]. During the COVID-19 pandemic, these benefits may become even more pronounced. Several international studies have reported lower SARS-CoV-2 infection rates and better clinical outcomes among home-based dialysis patients compared to those receiving in-center dialysis [20, 21]. For instance, a study from Ontario, Canada, showed that home-based dialysis patients had lower rates of SARS-CoV-2 positivity and COVID-19-related hospitalization between March and November 2020 [22].
Despite growing international evidence, comparative data from China remain lacking. The real-world impact of home-based dialysis on infection risk and prognosis among Chinese dialysis patients during the Omicron wave has yet to be explored. Therefore, this study aimed to compare Omicron infection rates and clinical outcomes between home-based dialysis and in-center dialysis patients in a large tertiary hospital in Shanghai. Additionally, we investigated the risk factors associated with infection and mortality among dialysis patients during the Omicron surge. Our findings aim to inform future dialysis strategies in the context of emerging infectious diseases.
Patients and methods
Study design and participants
This retrospective, single-center cohort study included patients who received maintenance dialysis at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, between December 1, 2022, and January 31, 2023. We included patients with ESRD characterized by an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m² requiring dialysis, aged ≥ 18 years, and undergoing maintenance dialysis for at least 3 months prior to study initiation. We excluded patients who initiated dialysis during the study period, were lost to follow-up, or switched dialysis modality during the observation period. Patients were categorized into two groups: home-based dialysis, including PD and HHD, and in-center dialysis. The primary endpoint was the comparison of Omicron infection rates between home-based dialysis and in-center dialysis groups during the Omicron variant predominance period in China. Secondary endpoints comprised: (1) infection timeline in both groups, (2) risk factors associated with Omicron infection, (3) comparison of all-cause mortality rates between groups during the study period, and (4) specific analysis of Omicron infection and all-cause mortality rates among HHD patients.
Data collection
Demographic data, clinical characteristics, and laboratory parameters were extracted from electronic medical records. Demographic information included age, sex, body mass index (BMI), smoking status, and dialysis vintage (time since dialysis initiation). Clinical data encompassed primary kidney disease, comorbidities (hypertension, diabetes mellitus, heart failure, cardiovascular disease, cerebrovascular events, cancer, respiratory diseases, and autoimmune diseases), dialysis adequacy parameters (e.g., Kt/V), medication use (renin-angiotensin-aldosterone system inhibitors [RAASi], angiotensin-converting enzyme inhibitors [ACEI], angiotensin receptor blockers [ARB], and immunosuppressive agents). Key clinical variables and comorbidities were obtained from documented medical histories and confirmed using standard diagnostic criteria. Hypertension was defined as a confirmed diagnosis, use of antihypertensive medications, or blood pressure ≥ 140/90 mmHg. Diabetes mellitus was defined as type 1 or type 2 diabetes, use of insulin or oral hypoglycemic agents, or glycated hemoglobin ≥ 6.5%. Heart failure was defined by relevant symptoms or signs and/or echocardiographic evidence of impaired left ventricular function. Cardiovascular disease was defined as coronary artery disease, myocardial infarction, angina, or previous coronary interventions. Cerebrovascular events were defined as ischemic or hemorrhagic stroke or transient ischemic attack confirmed by neuroimaging or neurologist assessment. Cancer was defined as any solid or hematologic malignancy diagnosed by pathology, imaging, or oncology consultation. Respiratory diseases included chronic obstructive pulmonary disease, asthma, interstitial lung disease, and similar conditions. Autoimmune diseases included systemic lupus erythematosus, rheumatoid arthritis, Sjögren’s syndrome, and related disorders. Kt/V target achievement rate was defined as the proportion of patients meeting the adequacy threshold for Kt/V, with adequacy determined according to established international guidelines (spKt/V ≥ 1.2 for hemodialysis; weekly total Kt/V ≥ 1.7 for peritoneal dialysis). COVID-19 vaccination status was also recorded. Laboratory parameters included lymphocyte count, hemoglobin, serum albumin, glycated hemoglobin, C-reactive protein, and ferritin levels. Omicron infection was diagnosed based on positive nucleic acid testing or antigen testing, combined with typical clinical symptoms. For patients who tested positive but remained asymptomatic, the date of positive testing was recorded as the infection date. Mortality was documented during the 2-month study period.
Statistical analysis
Continuous variables were expressed as mean (standard deviation [SD]) for normally distributed data or median [interquartile range, IQR] for non-normally distributed data. Categorical variables were presented as numbers (percentages). Comparisons between the home-based dialysis and in-center dialysis groups were performed using the independent-samples t test or Mann–Whitney U test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables, as appropriate. Time to Omicron infection was assessed using Kaplan–Meier survival analysis and compared by the log–rank test. To account for potential confounding, Cox proportional hazards regression analyses were performed with sequential covariate adjustment: Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, and lymphocyte count; Model 4: multivariable model including variables with P < .05 in univariable Cox regression. The cumulative infection rates were calculated and plotted, with the time required to reach a 50% infection rate determined for each group. Daily infection rates were also calculated to analyze temporal patterns of infection spread. To identify factors associated with Omicron infection, univariate and multivariate logistic regression analyses were performed. Variables with P < .05 in univariate analysis were included in the multivariate model. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs). For mortality analysis, we similarly conducted univariate and multivariate logistic regression to identify risk factors for death among infected patients, following the same variable selection criteria. Observations with missing values were excluded using complete-case analysis. Additionally, due to potential clinical differences between peritoneal dialysis and home hemodialysis patients within the home dialysis group, a sensitivity analysis was performed by separately describing demographic characteristics and survival outcomes for these two subgroups.
All statistical analyses were performed using SPSS version 26.0 (IBM Corp) and R version 4.1.2 (R Foundation for Statistical Computing). Two-sided P values < 0.05 were considered statistically significant. In all regression analyses, multicollinearity was assessed using variance inflation factors, with values < 5 considered acceptable.
Results
Baseline characteristics
A total of 465 dialysis patients were included in this cohort study, with 267 (57.4%) receiving home-based dialysis and 198 (42.6%) undergoing in-center dialysis. Table 1 presents the demographic, clinical, and laboratory characteristics of the study population stratified by dialysis modality. Patients in the home-based dialysis group were younger, had shorter dialysis vintage and higher BMI, and exhibited distinct comorbidity patterns, laboratory profiles, medication use, and dialysis adequacy compared with the in-center dialysis group, while most other demographic and clinical characteristics were similar. Table S1 and Table S2 summarize the demographic and clinical characteristics of the 263 PD patients and 4 HHD patients, respectively.
Table 1.
Demographic and clinical characteristics of patients by dialysis modality
| Characteristic | Total (n = 465) | Home-Based Dialysis (n = 267) | In-Center Dialysis (n = 198) |
P value |
|---|---|---|---|---|
| Demographics | ||||
| Male sex, No. (%) | 285 (61.3) | 163 (61.0) | 122 (61.6) | 0.978 |
| Age, mean (SD), y | 59.5 (14.4) | 57.0 (15.1) | 62.9 (12.6) | < 0.001 |
| Dialysis vintage, mean (SD), mo | 67.3 (59.5) | 54.2 (44.8) | 84.8 (71.3) | < 0.001 |
| Smoking | 257 (55.3) | 146 (54.7) | 111 (56.1) | 0.840 |
| BMI, median [IQR], kg/m² | 22.9 [20.7, 25.0] | 23.5 [21.3, 25.7] | 21.9 [19.9, 24.2] | < 0.001 |
| Primary kidney disease, No. (%) | ||||
| Glomerulonephritis | 159 (34.2) | 96 (36.0) | 63 (31.8) | 0.352 |
| Diabetic Nephropathy | 77 (16.6) | 48 (18.0) | 29 (14.6) | 0.339 |
| Hypertensive nephropathy | 36 (7.7) | 14 (5.2) | 22 (11.1) | 0.018 |
| Obstructive uropathy | 7 (1.5) | 6 (2.2) | 1 (0.5) | 0.247 |
| Polycystic kidney disease | 25 (5.4) | 7 (2.6) | 18 (9.1) | 0.002 |
| Comorbidities, No. (%) | ||||
| Hypertension | 392 (84.3) | 238 (89.1) | 154 (77.8) | 0.001 |
| Diabetes Mellitus | 128 (27.5) | 72 (27.0) | 56 (28.3) | 0.834 |
| Heart failure | 59 (12.7) | 30 (11.2) | 29 (14.6) | 0.341 |
| Autoimmune disease | 11 (2.4) | 4 (1.5) | 7 (3.5) | 0.262 |
| Cerebrovascular events | 72 (15.5) | 38 (14.2) | 34 (17.2) | 0.461 |
| Cardiovascular disease | 34 (7.3) | 9 (3.4) | 25 (12.6) | < 0.001 |
| Respiratory disease | 14 (3.0) | 13 (4.9) | 1 (0.5) | 0.012 |
| Cancer | 53 (11.4) | 22 (8.2) | 31 (15.7) | 0.019 |
| Laboratory parameters, median [IQR] | ||||
| Lymphocytes, ×10⁹/L | 1.2 [0.9–1.5] | 1.2 [0.9–1.6] | 1.1 [0.8–1.4] | 0.001 |
| Hemoglobin, g/L | 115.0 [104.0-125.0] | 114.0 [102.5-123.5] | 115.0 [106.0-125.0] | 0.100 |
| Albumin, g/L | 37.5 [34.6–40.5] | 36.8 [33.7–40.0] | 38.6 [36.3–40.7] | < 0.001 |
| HbA1c, % | 5.6 [5.2–6.2] | 5.6 [5.2–6.2] | 5.7 [5.3–6.3] | 0.380 |
| C-reactive protein, mg/L | 1.4 [0.5–4.8] | 1.9 [0.7–6.2] | 0.9 [0.5–3.1] | 0.001 |
| Ferritin, µg/L | 234.6 [94.0-444.8] | 220.9 [95.4–375.0] | 277.0 [88.0-489.6] | 0.270 |
| Treatment-related factors, No. (%) | ||||
| RAASi | 281 (60.4) | 179 (67.0) | 102 (51.5) | < 0.001 |
| ACEI | 34 (7.3) | 8 (3.0) | 26 (13.1) | < 0.001 |
| ARB | 247 (53.1) | 171 (64.0) | 76 (38.4) | < 0.001 |
| Vaccination | 27 (5.8) | 15 (5.6) | 12 (6.1) | 0.999 |
| Immunosuppressive agents | 16 (3.4) | 11 (4.1) | 5 (2.5) | 0.499 |
| Kt/V target achievement rate | 331 (71.2) | 167 (62.5) | 164 (82.8) | < 0.001 |
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; HbA1c, glycated hemoglobin; IQR, interquartile range; RAASi, renin-angiotensin-aldosterone system inhibitor
Omicron infection rate
During the study period, 314 patients (67.5%) were infected with the Omicron variant. The infection rate was significantly lower in the home-based dialysis group compared to the in-center dialysis group (52.1% [139/267] vs. 88.4% [175/198], P < .001). Within the home-based dialysis group, the infection rate among HHD patients was 50.0% (2/4), while the infection rate among PD patients was 52.1% (137/263). Kaplan–Meier analysis of time to infection further demonstrated a pronounced difference in infection dynamics between the two groups (log–rank test, P < .001) (Fig. 1A). The in-center dialysis group reached a 50% cumulative infection rate by day 24, while the home-based dialysis group did not reach this threshold until day 48, demonstrating a delayed peak in the home-based dialysis group compared to the in-center dialysis group. Cox proportional hazards analyses using three progressively adjusted models (Figs. 1B–D) consistently demonstrated that in-center dialysis was associated with an approximately threefold higher risk of infection relative to home-based dialysis. Model adjustments progressively accounted for demographic, clinical, and laboratory factors. The hazard ratios (HRs) were 3.05 (95% CI, 2.42–3.84, P < .001, Wald test), 3.06 (95% CI, 2.43–3.87, P < .001, Wald test), and 2.92 (95% CI, 2.30–3.72, P < .001, Wald test) for Figs. 1B and C, and D, respectively. Kaplan–Meier curves illustrating the cumulative infection rates are shown for PD patients in Fig. S1 and for HHD patients in Fig.S2.
Fig. 1.
Cumulative Omicron infection rate in home-based and in-center dialysis patients. Kaplan–Meier curves showing cumulative Omicron infection rates in patients receiving home-based dialysis (n = 267) versus in-center dialysis (n = 198) during the Omicron surge in China (December 2022 to January 2023). Shaded areas represent 95% confidence intervals. The horizontal dashed line indicates the 50% infection rate threshold. Vertical dashed lines show that the in-center dialysis group reached 50% cumulative infection by approximately day 24, while the home-based dialysis group took approximately 48 days to reach the same threshold. The difference between infection rates was statistically significant (A: P < .001, two-sided log–rank test). Cox proportional hazards models adjusted for age and sex (B), age, sex, and lymphocyte count (C), and multivariable adjustment including variables with P < .05 in univariable Cox regression, namely body mass index (BMI), angiotensin-converting enzyme inhibitor (ACEI) use, heart failure, and cardiovascular disease (D), consistently showed a higher infection risk in the in-center dialysis group compared to the home-based dialysis group. Hazard ratios (HRs) were 3.05 (95% CI, 2.42–3.84; P < .001, Wald test), 3.06 (95% CI, 2.43–3.87; P < .001, Wald test), and 2.92 (95% CI, 2.30–3.72; P < .001, Wald test), respectively. HR, hazard ratio; CI, confidence interval
Analysis of the infection timeline revealed that both groups reached their peak daily infection rates during the Omicron surge in late December 2022 in China, with the in-center dialysis group experiencing a maximum daily infection rate of 11.1%, compared to 4.7% in the home-based dialysis group. Throughout the observation period, the home-based dialysis group tended to exhibit lower daily infection rates compared to the in-center dialysis group (Fig. 2).
Fig. 2.
Daily Omicron infection rates in home-based and in-center dialysis patients. Daily Omicron infection rates in patients receiving home-based dialysis (n = 267) versus conventional in-center dialysis (n = 198) from December 1, 2022, to January 31, 2023. The graph depicts the 7-day moving average of new infections as a percentage of the at-risk population for each dialysis modality. Both groups experienced peak infection rates in late December 2022, coinciding with the height of the Omicron surge in China, followed by a gradual decline through January 2023. Throughout the observation period, the home-based dialysis group (red line) generally showed lower daily infection rates than the in-center dialysis group (blue line), particularly during the peak infection period in late December when the in-center group reached a maximum daily infection rate of 11.1% compared to 4.7% in the home-based group
Risk factors for Omicron infection
To identify factors associated with Omicron infection risk among dialysis patients, we conducted univariate and multivariate logistic regression analyses (Table 2). In univariate analysis, home-based dialysis was strongly associated with reduced infection risk (odds ratio [OR], 0.14; 95% CI, 0.09–0.24; P < .001). Serum albumin levels were associated with a slightly increased infection risk (OR, 1.04; 95% CI, 1.00-1.09; P = .038), as was ACEI use (OR, 2.82; 95% CI, 1.07–7.44; P = .036). Heart failure (OR, 1.93; 95% CI, 0.99–3.76; P = .053) and cardiovascular disease (OR, 2.25; 95% CI, 0.91–5.55; P = .080) showed trends toward increased infection risk, although these associations did not reach statistical significance. In multivariate analysis, after adjusting for relevant covariates, home-based dialysis remained significantly associated with reduced Omicron infection risk (adjusted odds ratio [AOR], 0.15; 95% CI, 0.09–0.26; P < .001), representing an 85% lower odds of infection compared with in-center dialysis. Neither serum albumin (AOR, 1.02; 95% CI, 0.98–1.06; P = .413) nor ACEI use (AOR, 1.52; 95% CI, 0.53–4.34; P = .437) maintained statistical significance in the adjusted model.
Table 2.
Logistic regression analysis of factors associated with Omicron infection in Dialysis patients
| Characteristic | Univariate Analysis | Multivariate Analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95%CI | P value | AOR | 95%CI | P value | |
| Demographics | ||||||
| Male sex | 0.94 | 0.63–1.40 | 0.756 | |||
| Age, y | 1.01 | 1.00-1.03 | 0.062 | |||
| Dialysis vintage, mo | 1.00 | 1.00-1.01 | 0.150 | |||
| Smoking | 1.03 | 0.69–1.52 | 0.889 | |||
| BMI, kg/m2 | 0.96 | 0.91–1.01 | 0.146 | |||
| Comorbidities | ||||||
| Hypertension | 0.73 | 0.42–1.29 | 0.283 | |||
| Diabetes mellitus | 1.06 | 0.68–1.65 | 0.790 | |||
| Heart failure | 1.93 | 0.99–3.76 | 0.053 | |||
| Cardiovascular disease | 2.25 | 0.91–5.55 | 0.080 | |||
| Cerebrovascular events | 0.72 | 0.43–1.22 | 0.226 | |||
| Autoimmune disease | 0.80 | 0.23–2.76 | 0.720 | |||
| Cancer | 0.97 | 0.52–1.78 | 0.910 | |||
| Respiratory disease | 1.15 | 0.35–3.73 | 0.817 | |||
| Laboratory parameters | ||||||
| Lymphocytes, ×10⁹/L | 0.93 | 0.64–1.35 | 0.716 | |||
| Albumin, g/L | 1.04 | 1.00-1.09 | 0.038 | 1.02 | 0.98–1.06 | 0.413 |
| Treatment-related factors | ||||||
| Home-based dialysis | 0.14 | 0.09–0.24 | < 0.001 | 0.15 | 0.09–0.26 | < 0.001 |
| Vaccination | 1.64 | 0.65–4.16 | 0.294 | |||
| Immunosuppressive agents | 0.76 | 0.27–2.12 | 0.594 | |||
| RAASi | 0.92 | 0.62–1.38 | 0.698 | |||
| ACEI | 2.82 | 1.07–7.44 | 0.036 | 1.52 | 0.53–4.34 | 0.437 |
| ARB | 0.77 | 0.52–1.14 | 0.197 | |||
| Kt/V target achievement rate | 1.39 | 0.91–2.12 | 0.127 | |||
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; AOR, adjusted odds ratio; ARB, angiotensin receptor blocker; BMI, body mass index; CI, confidence interval; OR, odds ratio; RAASi, renin-angiotensin-aldosterone system inhibitor
Mortality during the Omicron surge
A total of 29 patients (6.2%) died during the study period. The all-cause mortality rate was 5.2% (14/267) in the home-based dialysis group and 7.6% (15/198) in the in-center dialysis group, with no statistically significant difference between the two groups (P = .304). Within the home-based dialysis group, the all-cause mortality rate among PD patients was 5.3% (14/263), whereas no deaths were observed among HHD patients during the study period. Among infected patients, the overall all-cause mortality rate was 9.2% (29/314), with 10.1% (14/139) in the home-based dialysis group and 8.6% (15/175) in the in-center dialysis group (P = .648). Among home-based dialysis patients who were infected with Omicron, the all-cause mortality rate in the PD group was 10.2% (14/137), while no deaths occurred among the infected HHD patients (0/4).
Factors associated with mortality in dialysis patients with Omicron infection
To identify factors associated with mortality among dialysis patients who contracted Omicron infection, we conducted univariate and multivariate logistic regression analyses (Table 3). In the univariate analysis, several factors were significantly associated with increased mortality risk: increasing age (OR, 1.06 per year; 95% CI, 1.02–1.09; P < .001), heart failure (OR, 4.49; 95% CI, 1.95–10.36; P < .001), and lower serum albumin (OR, 0.89 per g/L; 95% CI, 0.82–0.96; P < .001). BMI showed a borderline significant association with mortality (OR, 1.10 per kg/m²; 95% CI, 1.00-1.22; P = .051). In the multivariate analysis, after adjusting for potential confounders, three factors remained independently associated with mortality: increasing age (AOR, 1.06 per year; 95% CI, 1.03–1.10; P < .001), heart failure (AOR, 4.82; 95% CI, 1.95–11.93; P < .001), and lower serum albumin (AOR, 0.91 per g/L; 95% CI, 0.84–0.98; P = .019). Notably, dialysis modality (home-based dialysis versus in-center dialysis) did not show a significant association with mortality risk (OR 1.10, 95% CI 0.51–2.40, P = .809), indicating that although home dialysis may influence infection susceptibility, it does not independently impact survival once infection has occurred.
Table 3.
Factors associated with mortality among dialysis patients with Omicron infection
| Characteristic | Univariate Analysis | Multivariate Analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95%CI | P value | AOR | 95%CI | P value | |
| Demographics | ||||||
| Male sex | 0.72 | 0.33–1.57 | 0.411 | |||
| Age, y | 1.06 | 1.02–1.09 | < 0.001 | 1.06 | 1.03–1.10 | < 0.001 |
| Dialysis vintage, mo | 1.00 | 1.00-1.01 | 0.329 | |||
| BMI, kg/m² | 1.10 | 1.00-1.22 | 0.051 | |||
| Smoking | 1.08 | 0.49–2.37 | 0.847 | |||
| Comorbidities | ||||||
| Hypertension | 0.72 | 0.28–1.87 | 0.502 | |||
| Diabetes mellitus | 2.04 | 0.92–4.50 | 0.079 | |||
| Heart failure | 4.49 | 1.95–10.36 | < 0.001 | 4.82 | 1.95–11.93 | < 0.001 |
| Cardiovascular disease | 2.49 | 0.86–7.15 | 0.091 | |||
| Cerebrovascular events | 0.76 | 0.22–2.64 | 0.666 | |||
| Cancer | 0.57 | 0.13–2.51 | 0.457 | |||
| Respiratory disease | 1.14 | 0.14–9.34 | 0.903 | |||
| Laboratory parameters | ||||||
| Lymphocyte count, ×10⁹/L | 0.45 | 0.17–1.18 | 0.105 | |||
| Albumin, g/L | 0.89 | 0.82–0.96 | < 0.001 | 0.91 | 0.84–0.98 | 0.019 |
| Treatment-related factors | ||||||
| Home-based dialysis | 1.10 | 0.51–2.40 | 0.809 | |||
| RAASi | 0.59 | 0.27–1.28 | 0.183 | |||
| ACEI | 0.74 | 0.17–3.28 | 0.690 | |||
| ARB | 0.49 | 0.22–1.10 | 0.083 | |||
| Kt/V target achievement rate | 0.64 | 0.28–1.45 | 0.284 | |||
Abbreviations: ACEI, angiotensin-converting enzyme inhibitor; AOR, adjusted odds ratio; ARB, angiotensin receptor blocker; BMI, body mass index; CI, confidence interval; OR, odds ratio; RAASi, renin-angiotensin-aldosterone system inhibitor
Discussion
In this retrospective cohort study of 465 dialysis patients during the Omicron surge in China, we found that patients receiving home-based dialysis had a significantly lower infection rate compared to those undergoing conventional in-center dialysis. However, once infected, the all-cause mortality rates were similar between the two modalities. Advanced age, pre-existing heart failure, and hypoalbuminemia emerged as independent risk factors for mortality among infected patients, regardless of dialysis modality. These findings are consistent with international reports and underscore the importance of minimizing exposure to communal healthcare environments during pandemic surges [6, 22–24].
The lower infection rate in the home-based dialysis group is likely attributable to decreased patient movement and reduced exposure to healthcare workers and other patients [23, 25, 26]. In contrast, in-center dialysis patients frequently visit dialysis centers multiple times a week, which may increase their risk of viral transmission despite protective measures [7, 27].
Importantly, home-based dialysis was identified as an independent protective factor against Omicron infection in our multivariate analysis. While ACEI usage showed potential protective effects in univariate models, it was not significant after adjustment—highlighting the complexity of interpreting medication effects in infectious disease contexts [28–31].
All-cause mortality rates between home-based dialysis and in-center dialysis groups did not differ significantly, although in-center dialysis patients showed a numerically higher all-cause mortality rate. Among infected patients, age, heart failure, and hypoalbuminemia were independently associated with death. These findings align with prior COVID-19 studies and reinforce the need for individualized risk stratification in dialysis populations [24, 32–35].
Notably, Qiu et al. studied the psychological health and quality of life of peritoneal dialysis patients during the Omicron lockdown in Shanghai. Using the SF-36 questionnaire, they found improvements in both physical and psychological states during the quarantine period, along with a significant reduction in peritonitis incidence, reflecting patients’ strong self-management abilities [36]. Their study focused on psychological and quality-of-life aspects within a single dialysis modality, whereas our research complements this by examining infection risk and clinical outcomes across multiple dialysis modalities, providing a more comprehensive perspective on the impact of the pandemic on dialysis patients.
This study has several strengths, including a relatively large sample size of dialysis patients during a well-defined Omicron surge, comprehensive assessment of baseline characteristics and laboratory parameters, and robust statistical analysis including time-to-event data. Notably, our center is the only one in mainland China that provides HHD, and this represents the first study from mainland China to include HHD patients in analyses during the Omicron surge [16, 17]. This unique aspect enhances the novelty of our research and provides valuable insights into a dialysis modality that remains uncommon in China. The focus on the Omicron variant, which has distinct clinical and epidemiological features compared to earlier SARS-CoV-2 variants, provides valuable insights specific to the current phase of the pandemic.
Our study has several limitations. First, its retrospective and single-center design may introduce selection bias. Second, despite statistical adjustment, residual confounding cannot be ruled out due to the non-randomized design. The home-based dialysis and in-center dialysis groups differed in multiple baseline characteristics, which may have influenced outcomes independently of dialysis modality. Third, although our study is the first to include HHD patients during the Omicron surge in mainland China, the number of HHD patients was limited, restricting our ability to draw definitive conclusions about this specific modality compared to other home-based therapies or conventional hemodialysis. Fourth, given the inherent differences between dialysis modalities, patients in the home-based dialysis group with asymptomatic or mildly symptomatic Omicron infection might have been less likely to undergo testing or seek medical attention, potentially introducing ascertainment bias. Although COVID-19 antigen self-testing rates were high during the study period, this remains a limitation of our study. Fifth, the relatively small sample size and short observation period limited the feasibility of formal time-series or transmission modeling analyses to fully assess transmission dynamics. Thus, interpretations based on visual inspection of infection trends are exploratory. Future studies with larger cohorts and longer follow-up are needed to validate these findings using advanced modeling. Although the Omicron surge examined in this study has subsided, our findings have broader implications for the management of ESRD patients during future public health emergencies.
Conclusion
In conclusion, home-based dialysis is associated with a significantly lower risk of Omicron infection in maintenance dialysis patients, without adversely affecting short-term survival. Home-based dialysis should be considered a strategic modality for dialysis care, particularly during infectious disease outbreaks. Expanding access to home-based dialysis, optimizing patient education, and integrating remote monitoring technologies may enhance pandemic resilience and improve long-term outcomes in dialysis populations.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to express our sincere gratitude to all patients who participated in this study and to the medical staff at the Department of Nephrology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, for their dedicated care during the Omicron surge. We thank the Clinical Research Center of Ren Ji Hospital for their technical support in data analysis and statistical expertise.
Abbreviations
- ACEI
Angiotensin-converting enzyme inhibitors
- ARB
Angiotensin receptor blockers
- BMI
Body mass index
- CIs
Confidence intervals
- CKD
Chronic kidney disease
- eGFR
Estimated glomerular filtration rate
- ESRD
End-stage renal disease
- HHD
Home hemodialysis
- HRs
Hazard ratios
- IQR
Interquartile range
- ORs
Odds ratios
- PD
Peritoneal dialysis
- RAASi
Renin-angiotensin-aldosterone system inhibitors
- SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
- SD
Standard deviation
- spKt/V
Single-pool Kt/V
Author contributions
Z.N., Y. Z., H.J. and W.G. contributed to the study conception and design. Data collection and analysis were performed by W.G. The first draft of the manuscript was written by H.J. and W.G., all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
The National Natural Science Foundation of China (Grant No. 82400790 & Grant No. 82070693). Institute of Molecular Medicine, the Shanghai Medical Youth Talent Training and Support Program ([2025]71). Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Nucleic Acid Chemistry and Nanomedicine, “Clinical+” Excellence Project (2024ZY004). The Multicenter Clinical Research Project of Shanghai Jiao Tong University School of Medicine (DLY201805).
Data availability
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of Shanghai Jiao Tong University School of Medicine and conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization Guidelines for Good Clinical Practice. The requirement for individual informed consent was waived by the ethics committee due to the retrospective nature of the study, the analysis of deidentified data, and the minimal risk to participants. All patient data were anonymized and protected according to institutional protocols. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for cohort studies.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Wen Gu, Yijun Zhou and Haijiao Jin contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.