Skip to main content
main-content

Open Access 29.04.2022 | COVID-19 | Original Paper

SARS-CoV-2 infection in chronic kidney disease patients with pre-existing dialysis: description across different pandemic intervals and effect on disease course (mortality)

verfasst von: Lisa Pilgram, Lukas Eberwein, Bjoern-Erik O. Jensen, Carolin E. M. Jakob, Felix C. Koehler, Martin Hower, Jan T. Kielstein, Melanie Stecher, Bernd Hohenstein, Fabian Prasser, Timm Westhoff, Susana M. Nunes de Miranda, Maria J. G. T. Vehreschild, Julia Lanznaster, Sebastian Dolff, the LEOSS study group

Erschienen in: Infection

Abstract

Purpose

Patients suffering from chronic kidney disease (CKD) are in general at high risk for severe coronavirus disease (COVID-19) but dialysis-dependency (CKD5D) is poorly understood. We aimed to describe CKD5D patients in the different intervals of the pandemic and to evaluate pre-existing dialysis dependency as a potential risk factor for mortality.

Methods

In this multicentre cohort study, data from German study sites of the Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) were used. We multiply imputed missing data, performed subsequent analyses in each of the imputed data sets and pooled the results. Cases (CKD5D) and controls (CKD not requiring dialysis) were matched 1:1 by propensity-scoring. Effects on fatal outcome were calculated by multivariable logistic regression.

Results

The cohort consisted of 207 patients suffering from CKD5D and 964 potential controls. Multivariable regression of the whole cohort identified age (> 85 years adjusted odds ratio (aOR) 7.34, 95% CI 2.45–21.99), chronic heart failure (aOR 1.67, 95% CI 1.25–2.23), coronary artery disease (aOR 1.41, 95% CI 1.05–1.89) and active oncological disease (aOR 1.73, 95% CI 1.07–2.80) as risk factors for fatal outcome. Dialysis-dependency was not associated with a fatal outcome—neither in this analysis (aOR 1.08, 95% CI 0.75–1.54) nor in the conditional multivariable regression after matching (aOR 1.34, 95% CI 0.70–2.59).

Conclusions

In the present multicentre German cohort, dialysis dependency is not linked to fatal outcome in SARS-CoV-2-infected CKD patients. However, the mortality rate of 26% demonstrates that CKD patients are an extreme vulnerable population, irrespective of pre-existing dialysis-dependency.
Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1007/​s15010-022-01826-7.
Julia Lanznaster and Sebastian Dolff contributed equally to this work.
Members of “the LEOSS study group” are listed in acknowledgement section.

Introduction

Several hundred million people were infected and more than 5 million people died since the beginning of the coronavirus disease 2019 (COVID-19) pandemic [1]. COVID-19 as a respiratory syndrome caused by the infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and it is characterized by fever, cough and dyspnea with a broad clinical spectrum ranging from lack of symptoms to death. COVID-19 pneumonia is a well-known and frequent organ manifestation in patients with severe disease. SARS-CoV-2 interacts with the transmembrane protein angiotensin converting enzyme 2 (ACE-2), best known for its role in the renin–angiotensin–aldosterone system (RAAS). ACE-2 is expressed in alveolar cells in the lung, as well as in the kidney, most abundant in proximal tubular cells and podocytes [2]. There is increasing evidence that the kidney is a target organ as well [3, 4]. In line, SARS-CoV-2 RNA can be detected in 60% of kidney specimens of COVID-19 patients suggesting renal tropism and a pivotal role in the pathogenesis [4].
Apart from being a target of the virus itself, pre-existing chronic kidney disease (CKD) has been reported to be both, a risk factor for a more severe course of the disease as well as mortality [5, 6]. This is best epitomized in CKD5D patients adjusted for age and other comorbidities, such as atherosclerotic cardiovascular disease or chronic heart disease [7]. In a previous study, we detected a mortality higher than 30% in these patients but were not able to confirm dialysis as an independent risk factor [8]. However, data at this time was limited and only included 75 patients on dialysis. Results from the European ERA–EDTA Registry presented a COVID-19 attributable mortality of 20.0% among patients undergoing chronic dialysis [9]. In Germany similar numbers could be obtained among dialysis-dependent CKD patients [10]. However, studies including both dialysis-dependent as well as dialysis-independent CKD patients are scarce which might underestimate the risk of dialysis-independent CKD itself.
Unfortunately, therapeutic options in COVID-19 are still limited. Especially in the first interval of the pandemic, the European Medicines Agency (EMA) issued warnings for severe kidney impairment (eGFR < 30 ml/min or dialysis or veno-venous hemofiltration) in the administration of remdesivir, the only authorized drug at this time [11]. The advent of other pharmacological interventions and the changing view on remdesivir might have an impact in later intervals of the pandemic [12].
The goal of the present study was to describe the course of SARS-CoV-2 infection in patients suffering from dialysis-dependent CKD across the pandemic intervals and to evaluate the influence of pre-existing dialysis based on data from the Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS).

Methods

Study design and data collection

We performed our analyses of patients suffering from dialysis-dependent CKD retrieving data from LEOSS (https://​leoss.​net/​) (Fig. 1) [13]. In LEOSS, clinical data is reported anonymously and retrospectively in an electronic case report form using the online platform ClinicalSurveys.net of the University Hospital of Cologne [14]. The anonymization procedure has been published previously [15].

Study population

The transmitted data set consisted of 1277 patients, which were documented in 80 different study sites and diagnosed between January 2020 and May 2021. Exclusion criteria are illustrated in Fig. 1. We excluded in total 8.3% (106/1277) patients resulting in a data set of 207 patients suffering from CKD5D (cases) and 964 patients suffering from CKD not requiring dialysis (potential controls).

Covariables and outcomes

Chosen parameters included sociodemographics, comorbidities, details on CKD, clinical and diagnostic parameters, as well as administered therapies. Symptoms and diagnostic parameters were determined within 48 h after first SARS-CoV-2 positive testing. Pre-existing comorbidities and clinical events were documented by investigators according to clinical definitions using anamnestic information and medical records. Prior immunosuppressive medication at baseline was defined as administered within an interval of 3 months before SARS-CoV-2 infection. Advanced respiratory support was defined as mechanical ventilation or extracorporeal membrane oxygenation (ECMO). The month of first diagnosis was assigned to one of three intervals of pandemic based on infection rates and evolving virus variants in Germany [16]: January 2020–June 2020, July 2020–January 2021, February 2021–May 2021. Death within the observational period was used as end-point in the regression analyses.

Statistical methods

Data management and analyses were performed using R, version 4.1.0 [17]. Figure 1 illustrates the workflow.
We described patients’ characteristics as absolute numbers and percentages. Group differences between the three different intervals of pandemic were determined using Chi-squared test or when applicable Fisher’s exact test. We controlled for multiple comparison using the Bonferroni correction.
Variables relevant for the regression analyses were analysed for missingness (supplementary (S) Table S1) and imputed iteratively via fully conditional specification (FCS) with proportional odds model or polytomous logistic regression depending on the nature of the respective variable using the R package MICE (https://​cran.​rproject.​org/​web/​packages/​mice/​mice.​pdf). This resulted in a total of 5 imputed data sets.
For the propensity-score matched pair analysis, each case was matched to one control in each imputed data set using the R package MatchIt (https://​cran.​rproject.​org/​web/​packages/​MatchIt/​index.​html). Exact matching was performed on age, gender and phase (according to LEOSS criteria, see Figure S1) at first SARS-CoV-2 detection; propensity-score matching (nearest neighbour) on hypertension, chronic heart failure, coronary artery disease, diabetes mellitus type 2, chronic obstructive pulmonary disease (COPD), active oncological disease, obesity, prior immunosuppressive medication and therapy limitations. Matching quality was assessed via standardized mean differences.
We used multivariable logistic regression or conditional logistic regression stratified by dialysis to estimate effects. Results were pooled across all imputed data sets and reported as (adjusted) odds ratios [(a)OR] with 95% confidence intervals (95% CI). p < 0.05 was set as level of significance.

Ethical statement

LEOSS was approved by the applicable local ethics committees of all participating centers and registered at the German Clinical Trials Register (DRKS, No. S00021145).

Results

Cohort

The cohort consisted of 207 patients suffering from CKD5D recruited in LEOSS and diagnosed between January 2020 and May 2021. All patients underwent hemodialysis. Vascular hypertensive (46.2%, 61/132), secondary (22.0%, 29/132) and primary glomerular disease (9.1%, 12/132) were the leading etiologies of CKD. Most CKD5D patients also suffered from hypertension (79.6%, 164/206). Other frequent comorbidities included diabetes mellitus type 2 (44.2%, 88/199), coronary artery disease (36.3%, 74/204) and obesity (31.4%, 44/140). Prior immunosuppressive medication was present in 17.6% (34/193). History of transplantation (51.5%, 17/33), rheumatological disease (15.2%, 5/33) and other reasons (33.3%, 11/33) were given as indication. Details are depicted in Table 1.
Table 1
Characteristics of SARS-CoV-2-infected patients on hemodialysis in the different intervals of COVID-19 pandemic
 
Diagnosed between
p-value
January 2020 and June 2020
July 2020 and January 2021
February 2021 and May 2021
n = 58
%
n = 116
%
n = 33
%
Age
 
 18–45 years
4/58
6.9
8/116
6.9
3/33
9.1
0.751
 46–55 years
3/58
5.2
12/116
10.3
4/33
12.1
 56–65 years
10/58
17.2
25/116
21.6
7/33
21.2
 66–75 years
12/58
20.7
22/116
19.0
8/33
24.3
 76–85 years
21/58
36.2
41/116
35.3
7/33
21.2
 > 85 years
8/58
13.8
8/116
6.9
4/33
12.1
Gender
 Female
20/58
34.5
47/116
40.5
20/33
60.6
0.046
 Male
38/58
65.5
69/116
59.5
13/33
39.4
Comorbidities
 Hypertension
44/58
75.9
96/115
83.5
24/33
72.7
0.283
 Chronic heart failure
16/56
28.6
26/111
23.4
11/33
33.3
0.483
 Coronary artery disease
20/57
35.1
43/114
37.7
11/33
33.3
0.878
 Diabetes mellitus type 2
19/56
33.9
54/111
48.7
15/32
46.9
0.185
 COPD
5/58
8.6
13/112
11.6
5/33
15.2
0.603
 Active oncological disease
2/58
3.5
4/105
3.8
1/33
3.0
1.000
 Obesity
13/52
25.0
25/67
37.3
6/21
28.6
0.341
Prior immunosuppressive medication
 Prior immunosuppressive medication
7/56
12.5
18/107
16.8
9/30
30
0.121
Status at COVID-19 diagnosis
 Uncomplicated phase
39/58
67.2
76/116
65.5
18/33
54.6
0.432
 Complicated phase
17/58
29.3
37/116
31.9
12/33
36.4
 
 Critical phase
2/58
3.5
3/116
2.6
3/33
9.1
 
Treatment in the course
 Steroids
4/52
7.7
48/111
43.2
10/33
30.3
 < 0.001
 Remdesivir
1/51
2.0
9/107
8.4
1/33
3.0
0.239
 Convalescent plasma
1/41
2.4
7/109
6.4
1/23
4.4
0.785
 Targeted therapy (antibodies)
NA
NA
0/72
0.0
4/19
21.1
 < 0.001
 Apheresis
2/42
4.8
1/106
0.9
0/23
0.0
0.204
 Chloroquin
8/51
15.7
2/106
1.9
1/33
3.0
0.004
 Azithromycin
7/52
13.5
7/108
6.5
2/33
6.1
0.234
Therapy limitation
 Explicit deny of therapy
5/24
20.8
26/109
23.9
5/21
23.8
0.718
 Explicit wish for therapy
1/24
4.2
2/109
1.9
1/21
4.8
 No discussion on therapy limitations
18/24
75.0
81/109
74.3
15/21
71.4
Course of disease
 Fatal outcome
15/58
25.9
28/116
24.1
12/33
36.4
0.370
 Advanced respiratory support
11/54
20.4
14/115
12.2
4/33
12.1
0.345
 Critical phase
17/58
29.3
18/116
15.5
6/33
18.2
0.096
 Thrombotic event
2/44
4.6
3/114
2.6
2/23
8.0
0.317
 Bleeding event
0/39
0.0
4/113
3.5
0/25
0.0
0.760
 Septic shock
3/56
5.4
6/116
5.2
0/33
0.0
0.467
 Congestive heart failure
0/56
0.0
1/115
0.9
1/33
0.3
0.374
All variables are derived from the unimputed data set and expressed as numbers (no.) and percentages (%) referred to the numbers excluding missing data (missing details in Table S1). Obesity was defined by an indicated Body-Mass-Index > 30 kg/m2. Prior immunosuppressive medication includes an interval of 3 months before SARS-CoV-2 infection, therapy limitation defined as Do-Not-Intubate-, Do-Not-Resuscitate-Orders or the refusal of intensive care, advanced respiratory support as invasive or non-invasive mechanical ventilation or ECMO. COPD: chronic obstructive pulmonary disease. ECMO: extracorporeal membrane oxygenation
At first detection of SARS-CoV-2, 46.2% (84/180) of our patients reported fever, 36.0% (64/178) dyspnea, 28.5% (49/172) dry cough, 7.1% (12/170) myalgia, 4.1% (7/171) headache and 2.4% (4/167) hypogeusia and/or hyposmia. At this time point, most patients (64.3%, 133/207) were assigned to the uncomplicated phase according to LEOSS criteria (see Figure S1). During the further course of disease, 19.8% (41/207) underwent critical phase, 14.3% (29/209) required advanced respiratory support and 26.6% (55/207) patients deceased.

Patients’ characteristics, presentation at first SARS-CoV-2 detection and treatment strategies in different pandemic intervals

The pandemic was divided into three intervals based on infection rates and evolving virus variants in Germany: January 2020–June 2020, July 2020–January 2021, February 2021–May 2021. When comparing across the pandemic intervals, patients’ characteristics did not differ except for gender (Table 1). The percentage of recruited female patients increased over time: in the first interval, 34.5% (20/58) of the patients, in the second 40.5% (47/116) and in the third interval 60.6% (20/33).
There was no significant difference between the pandemic intervals regarding existing symptoms and laboratory parameters at first SARS-CoV-2 detection. The latter is illustrated in Fig. 2. CRP was generally elevated ≥ 30 mg/dl in 63.4% (90/142), D-dimers > 2 × upper limit of normal (ULN) in 61.3% (46/75) and lymphocytes were below 800/μl in 58.2% (52/91).
Therapy limitations were reported in 23.4% (36/154) of the patients. Throughout the pandemic, administered treatment changed: Steroids > 0.5 mg/kg prednisolone equivalents were used in 7.7% (4/52) of the patients during the first interval, in 43.2% (48/111) during the second one and in 30.3% (10/33) during the third interval. Use of chloroquine was more frequent in the first interval (15.7%, 8/51 versus 1.9%, 2/106 in the second interval versus 3.0%, 1/33 in the third interval). Remdesivir was administered in 5.8% (11/191) of the patients with no differences in frequency of use throughout the pandemic.

Estimating the effect of dialysis in CKD patients

We performed a multivariable logistic regression on fatal outcome using the whole data set of 1171 patients suffering from CKD in LEOSS after multiple imputation. Pooled results are shown in Table 2. Increasing age was identified as risk factor with the greatest adjusted odds ratio (aOR) in the category > 85 years (aOR 7.34, 95% CI 2.45–21.99, p < 0.001). Chronic heart failure (aOR 1.67, 95% CI 1.25–2.23, p < 0.001), coronary artery disease (aOR 1.41, 95% CI 1.05–1.89, p = 0.021) and active oncological disease (aOR 1.73, 95% CI 1.07–2.80, p = 0.027) were further predictors for fatal outcome. Dialysis dependency did not show a significant association with mortality in CKD patients (aOR 1.08, 95% CI 0.75–1.54, p = 0.692).
Table 2
Pooled results of multivariable logistic regression of predictive factors for fatal outcome in SARS-CoV-2-infected patients suffering from chronic kidney disease
 
Multivariable regression analysis on fatal outcome
aOR
95% CI
p-value
Age
 18–45 years
Reference
   
 46–55 years
3.01
0.95
9.60
0.062
 56–65 years
1.72
0.56
5.33
0.344
 66–75 years
3.08
1.04
9.13
0.043
 76–85 years
3.95
1.35
11.55
0.012
 > 85 years
7.34
2.45
21.99
< 0.001
Gender
 Female
0.87
0.66
1.15
0.340
 Male
Reference
   
Comorbidities*
 Hypertension
0.98
0.67
1.37
0.897
 Chronic heart failure
1.67
1.25
2.23
< 0.001
 Coronary artery disease
1.41
1.05
1.89
0.021
 Diabetes mellitus type 2
0.97
0.73
1.28
0.810
 COPD
1.28
0.86
1.91
0.223
 Active oncological disease
1.73
1.07
2.80
0.027
 Obesity
1.03
0.69
1.53
0.895
 Pre-existing dialysis
1.08
0.75
1.54
0.692
Prior immunosuppressive medication*
 Prior immunosuppressive medication
0.98
0.65
1.48
0.918
Multivariable logistic regression on fatal outcome was performed using the imputed data set. Obesity was defined by an indicated Body-Mass-Index > 30 kg/m2. Prior immunosuppressive medication includes an interval of 3 months before SARS-CoV-2 infection, therapy limitation defined as Do-Not-Intubate-, Do-Not-Resuscitate-Orders or the refusal of intensive care, advanced respiratory support as invasive or non-invasive mechanical ventilation or ECMO. aOR: adjusted odds ratio. CI: confidence interval. COPD: chronic obstructive pulmonary disease. ECMO: extracorporeal membrane oxygenation. * No reference level indicated in binary variables
Patients suffering from CKD5D were matched via propensity score to controls suffering from CKD not requiring dialysis. Controls were predominantly assigned to CKD stage 3 (52.1%, 395/758), followed by stage 4 (15.8%, 120/758) and stage 2 (15.0%, 114/758), according to the definition of the international guideline group Kidney Disease Improving Global Outcomes (KDIGO). A detailed description of controls suffering from CKD not requiring dialysis is given in Table S2. In the univariate and multivariable conditional regression stratified by dialysis, dialysis-dependency was not significantly associated with fatal outcome (aOR 1.34, 95% CI 0.70–2.59, p = 0.375). Pooled results are shown in Table 3. We performed sensitivity analyses using the unimputed data set (Table S3) that confirmed our results with dialysis-dependency not being significantly associated with fatal outcome but exhibiting a risk tendency (aOR 1.40, 95% CI 0.73–2.69, p = 0.31). Univariate and multivariable results of the matched-pair analyses using the imputed and unimputed data set are illustrated in Fig. 3.
Table 3
Pooled results of conditional regression analyses on fatal outcome stratified by dialysis
 
Univariate regression analysis on fatal outcome
Multivariable regression analysis on fatal outcome
OR
95% CI
p-value
aOR
95% CI
p-value
Pre-existing dialysis
1.15
0.66
2.01
0.617
1.34
0.70
2.59
0.375
Diagnosed between
 January–June 2020
Reference
       
 July 2020–January 2021
1.10
0.41
2.97
0.853
1.02
0.70
2.59
0.973
 February–May 2021
1.53
0.26
9.20
0.620
1.46
0.33
3.16
0.708
Treatment in the course*
 Steroids
0.92
0.35
2.44
0.869
0.77
0.23
2.61
0.671
 Remdesivir
1.69
0.34
8.43
0.515
2.61
0.03
36.41
0.342
 Convalescent plasma
1.08
0.07
16.52
0.953
1.12
0.34
19.80
0.944
Univariate and multivariable regression analyses were performed after propensity-score matching, results of the imputed data sets pooled. Exact matching was performed on age, gender and phase (according to LEOSS criteria, see Figure S1) at first SARS-CoV-2 detection; propensity-score matching (nearest neighbour) on hypertension, chronic heart failure, coronary artery disease, diabetes mellitus type 2, chronic obstructive pulmonary disease (COPD), active oncological disease, obesity, prior immunosuppressive medication and therapy limitations. Timing of first diagnosis was aggregated into three intervals of pandemic based on the epidemiological waves in Germany: January 2020–June 2020 (reference category), July 2020–January 2021 and February 2021–May 2021. Treatment administered at least once in the course of COVID-19 with no administration serving as reference category. Obesity was defined by an indicated Body-Mass-Index > 30 kg/m2. Prior immunosuppressive medication includes an interval of 3 months before SARS-CoV-2 infection. Phases at COVID-19 diagnosis were assigned according to LEOSS criteria (Figure S1). Therapy limitation were defined as Do-Not-Intubate-, Do-Not-Resuscitate-Orders or the refusal of intensive care, advanced respiratory support as invasive or non-invasive mechanical ventilation or ECMO. (a)OR: (adjusted) odds ratio. CI: confidence interval. COPD: chronic obstructive pulmonary disease. ECMO: extracorporeal membrane oxygenation. * No reference level indicated in binary variables

Discussion

The present study is based on data of LEOSS, which is the largest clinical data collection on SARS-CoV-2-infected patients in Germany and has been active since the very beginning of the pandemic, allowing us to describe SARS-CoV-2-infected CKD5D patients of different pandemic intervals [18]. Using a matched-pair design, we examined the additional effect of dialysis-dependency to the general risk of non-dialysis CKD patients.

Differences across the pandemic intervals

Interestingly—despite of changing patients’ management and testing strategies, evolving virus variants and applied vaccinations—sociodemographics, clinical characteristics and laboratory findings at first diagnosis did not significantly differ in dialysis-dependent CKD patients over time. However, the changing landscape of COVID-19 therapeutics and recommendations for specific strategies, as well as the overall limited options for CKD patients is reflected in our data. While hydroxychloroquine has been more frequently used at the very beginning of the pandemic, it declined after randomized controlled trials failed to detect a benefit [19]. The increasing use of steroids follows recommendations by (inter)national medical societies and the WHO that evolved after the first interval of the pandemic [2022]. Remdesivir, as the first antiviral drug approved but currently without clear recommendation for use [20, 22] and in particular with precaution for patients with reduced GFR [11], has interestingly been administered throughout the whole pandemic in some patients undergoing dialysis for chronic dialysis dependency.

Risk factors in patients suffering from CKD

Our multivariable analysis confirmed already known risk factors also for patients suffering from CKD, such as age, chronic heart failure, coronary artery disease and an active oncological disease [2325], which was described for the whole LEOSS cohort [18, 2628] and which we published in a smaller CKD cohort previously [8]. In contrast, broadly accepted risk factors, such as male sex, hypertension or diabetes mellitus failed to present as additional risk factors in our model, which might be due to the overall high prevalence in our cohort. It might also be important to note that hypertension and diabetes mellitus often have been identified without being adjusted for CKD [23, 29] or in a cohort where prevalence of CKD was low [30, 31].

Dialysis-dependency—an independent risk factor for mortality?

Pre-existing need for dialysis by itself was neither in our multivariable regression analysis nor in our propensity-score matched-pair analyses significantly associated with fatal outcome in SARS-CoV-2-infected patients, thus confirming our previously published results [8]. The OpenSAFELY project with 17,278,392 individuals similarly identified CKD as one of the highest risk factors for death but, in contrast, identifies a history of dialysis as an additional factor in a secondary analysis [24]. The slight difference in a history of dialysis and dialysis-dependency might account for these discrepancies. Flythe et al. addressed in a retrospective cohort study (STOP-COVID) in 4264 critical ill patients with COVID-19 (143 patients with preexisting kidney failure receiving maintenance dialysis) a similar question as the present study [5]. They demonstrated that dialysis-dependent CKD patients had a shorter interval from symptom onset to intensive care treatment than non-dialysis-dependent CKD patients and detected higher mortality rates for both—dialysis-dependent and -independent CKD patients. In line, they showed that hazards of in-hospital death is higher in patients with dialysis-dependent kidney failure compared to patients without pre-existing CKD [5]. Further studies report high mortality within the range of 20–30% among SARS-CoV-2-infected patients suffering from dialysis-dependent CKD [10, 25, 3234] which is comparable to our results (26.6%, 55/207). A UK registry study stressed kidney replacement therapy as crucial risk factor. In particular in center hemodialysis patients had a high mortality with a peak in April 2020 [35]. A more recent prospective observational study demonstrated an increased mortality (35.7%) of hemodialysis patients within the first year after infection [36]. Remarkably, these patients died also after discharge of the hospital. Moreover, anti SARS-CoV2 antibodies decrease with time indicating that humoral responses were low after infection. A similar response has been described after vaccination in this vulnerable cohort [37]. However, in these studies, a direct comparison to dialysis-independent CKD patients is lacking. Thus, the studies analyzed different dialysis cohorts in different countries at different time point during the pandemic. Subsequently the results might differ. Our study highlights CKD and decreased glomerular filtration rate (GFR) independent of dialysis as relevant risk factors for severe COVID-19.
As 100% of our dialysis patients were on hemodialysis and none on peritoneal dialysis, we are unable to generate insights in potential benefits of hemodialysis, i.e., the intermittent anticoagulation wit heparin, usually three times a week, or the regular health care utilization that would allow swifter diagnosis and therapy. Previous reports have, however, not shown any difference in the disease course between peritoneal dialysis and hemodialysis [25].
One of the strengths of our study lies in being based on data of LEOSS, which has uniformly and standardized collected data since the beginning of the pandemic. Thus, all three intervals of the pandemic, as well as cases and controls derive from one data source operating on a transregional level with more than 131 sites. Nevertheless, there are still several limitations as outpatient sites are underrepresented in LEOSS, study sites have changed over time and important confounders (e.g., socioeconomic background, COVID-19 vaccination status, frailty) might not have sufficiently been considered in the matching or regression analysis. Dialysis could also have an impact on other endpoints, such as ICU admission or thromboembolic complications which should be addressed in further analyses.
In conclusion, our results indicate that not chronic dialysis dependency itself but rather the associated age, co-morbidities and underlying diseases are important modifiers of disease severity and death. However, the high mortality in both, cases and controls, should raise awareness for SARS-CoV-2-infected patients suffering from CKD, and should be considered when discussing about recommendations for vaccine booster shots.

Acknowledgements

We express our deep gratitude to all study teams supporting the LEOSS study. The LEOSS study group contributed at least 5 per mille to the analyses of this study: Hospital Passau (Julia Lanznaster), University Hospital Duesseldorf (Bjoern-Erik Jensen), Klinikum Dortmund gGmbH, Hospital of University Witten/Herdecke (Martin Hower), Nephrological Center Villingen-Schwenningen (Bernd Hohenstein), Marien Hospital Herne Ruhr University Bochum (Timm Westhoff), University Hospital Frankfurt (Maria Vehreschild), Technical University of Munich (Christoph Spinner), University Hospital Jena (Maria Madeleine Ruethrich), Hospital Ernst von Bergmann (Lukas Tometten), Hospital Ingolstadt (Stefan Borgmann), University Hospital Cologne (Norma Jung), Hospital Bremen-Center (Bernd Hertenstein), Municipal Hospital Karlsruhe (Christian Degenhardt), Elisabeth Hospital Essen (Ingo Voigt), University Hospital Regensburg (Frank Hanses), Johannes Wesling Hospital Minden Ruhr University Bochum (Kai Wille), Hospital Maria Hilf GmbH Moenchengladbach (Juergen vom Dahl), Robert-Bosch-Hospital Stuttgart (Katja Rothfuss), Catholic Hospital Bochum (St. Josef Hospital) Ruhr University Bochum (Kerstin Hellwig), University Hospital Schleswig-Holstein Luebeck (Jan Rupp), University Hospital Wuerzburg (Nora Isberner), Hospital Leverkusen (Lukas Eberwein), University Hospital Bonn (Jacob Nattermann), University Hospital Erlangen (Richard Strauss), University Hospital Essen (Sebastian Dolff), University Hospital Tuebingen (Siri Göpel).
The LEOSS study infrastructure group: Jörg Janne Vehreschild (Goethe University Frankfurt), Susana M. Nunes de Miranda (University Hospital of Cologne), Carolin E. M. Jakob (University Hospital of Cologne), Melanie Stecher (University Hospital of Cologne), Lisa Pilgram (Goethe University Frankfurt), Nick Schulze (University Hospital of Cologne), Sandra Fuhrmann (University Hospital of Cologne), Max Schons (University Hospital of Cologne), Annika Claßen (University Hospital of Cologne), Bernd Franke (University Hospital of Cologne) und Fabian Prasser (Berlin Institute of Health @ Charité—Universitätsmedizin Berlin).

Declarations

Conflict of interest

Felix C. Koehler reports grants by Else Kröner-Fresenius-Stiftung, by the German Research Foundation under Germany’s Excellence Strategy—EXC 2030: CECAD—Excellent in Aging Research—Project number 390661388, by the Maria-Pesch Stiftung, Cologne and by the Koeln Fortune program/Faculty of Medicine, University of Cologne outside of this project. All authors declare no relevant conflicts of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://​creativecommons.​org/​licenses/​by/​4.​0/​.
Anhänge

Supplementary Information

Below is the link to the electronic supplementary material.
Literatur
2.
Zurück zum Zitat Pan XW, Xu D, Zhang H, Zhou W, Wang LH, Cui XG. Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: a study based on single-cell transcriptome analysis. Intensive Care Med. 2020;46:1114–6. CrossRef Pan XW, Xu D, Zhang H, Zhou W, Wang LH, Cui XG. Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: a study based on single-cell transcriptome analysis. Intensive Care Med. 2020;46:1114–6. CrossRef
3.
Zurück zum Zitat Batlle D, Soler MJ, Sparks MA, Hiremath S, South AM, Welling PA, Swaminathan S, Covid, Ace2 in Cardiovascular L, Kidney Working G. Acute kidney injury in COVID-19: emerging evidence of a distinct pathophysiology. J Am Soc Nephrol. 2020;31:1380–3. CrossRef Batlle D, Soler MJ, Sparks MA, Hiremath S, South AM, Welling PA, Swaminathan S, Covid, Ace2 in Cardiovascular L, Kidney Working G. Acute kidney injury in COVID-19: emerging evidence of a distinct pathophysiology. J Am Soc Nephrol. 2020;31:1380–3. CrossRef
4.
Zurück zum Zitat Braun F, Lutgehetmann M, Pfefferle S, Wong MN, Carsten A, Lindenmeyer MT, Norz D, Heinrich F, Meissner K, Wichmann D, et al. SARS-CoV-2 renal tropism associates with acute kidney injury. Lancet. 2020;396:597–8. CrossRef Braun F, Lutgehetmann M, Pfefferle S, Wong MN, Carsten A, Lindenmeyer MT, Norz D, Heinrich F, Meissner K, Wichmann D, et al. SARS-CoV-2 renal tropism associates with acute kidney injury. Lancet. 2020;396:597–8. CrossRef
5.
Zurück zum Zitat Flythe JE, Assimon MM, Tugman MJ, Chang EH, Gupta S, Shah J, Sosa MA, Renaghan AD, Melamed ML, Wilson FP, et al. Characteristics and outcomes of individuals with pre-existing kidney disease and COVID-19 admitted to intensive care units in the United States. Am J Kidney Dis. 2021;77:190–203. CrossRef Flythe JE, Assimon MM, Tugman MJ, Chang EH, Gupta S, Shah J, Sosa MA, Renaghan AD, Melamed ML, Wilson FP, et al. Characteristics and outcomes of individuals with pre-existing kidney disease and COVID-19 admitted to intensive care units in the United States. Am J Kidney Dis. 2021;77:190–203. CrossRef
6.
Zurück zum Zitat Council E-E, Group EW. Chronic kidney disease is a key risk factor for severe COVID-19: a call to action by the ERA-EDTA. Nephrol Dial Transplant. 2021;36:87–94. CrossRef Council E-E, Group EW. Chronic kidney disease is a key risk factor for severe COVID-19: a call to action by the ERA-EDTA. Nephrol Dial Transplant. 2021;36:87–94. CrossRef
7.
Zurück zum Zitat Ng JH, Hirsch JS, Wanchoo R, Sachdeva M, Sakhiya V, Hong S, Jhaveri KD, Fishbane S, Northwell C-RC, the Northwell Nephrology C-RC. Outcomes of patients with end-stage kidney disease hospitalized with COVID-19. Kidney Int. 2020;98:1530–9. CrossRef Ng JH, Hirsch JS, Wanchoo R, Sachdeva M, Sakhiya V, Hong S, Jhaveri KD, Fishbane S, Northwell C-RC, the Northwell Nephrology C-RC. Outcomes of patients with end-stage kidney disease hospitalized with COVID-19. Kidney Int. 2020;98:1530–9. CrossRef
8.
Zurück zum Zitat Pilgram L, Eberwein L, Wille K, Koehler FC, Stecher M, Rieg S, Kielstein JT, Jakob CEM, Ruthrich M, Burst V et al. Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease. Infection. 2021. Pilgram L, Eberwein L, Wille K, Koehler FC, Stecher M, Rieg S, Kielstein JT, Jakob CEM, Ruthrich M, Burst V et al. Clinical course and predictive risk factors for fatal outcome of SARS-CoV-2 infection in patients with chronic kidney disease. Infection. 2021.
9.
Zurück zum Zitat Jager KJ, Kramer A, Chesnaye NC, Couchoud C, Sanchez-Alvarez JE, Garneata L, Collart F, Hemmelder MH, Ambuhl P, Kerschbaum J, et al. Results from the ERA-EDTA Registry indicate a high mortality due to COVID-19 in dialysis patients and kidney transplant recipients across Europe. Kidney Int. 2020;98:1540–8. CrossRef Jager KJ, Kramer A, Chesnaye NC, Couchoud C, Sanchez-Alvarez JE, Garneata L, Collart F, Hemmelder MH, Ambuhl P, Kerschbaum J, et al. Results from the ERA-EDTA Registry indicate a high mortality due to COVID-19 in dialysis patients and kidney transplant recipients across Europe. Kidney Int. 2020;98:1540–8. CrossRef
10.
Zurück zum Zitat Hoxha E, Suling A, Turner JE, Haubitz M, Floege J, Huber TB, Galle JC. COVID-19 prevalence and mortality in chronic dialysis patients. Dtsch Arztebl Int. 2021;118:195–6. PubMedPubMedCentral Hoxha E, Suling A, Turner JE, Haubitz M, Floege J, Huber TB, Galle JC. COVID-19 prevalence and mortality in chronic dialysis patients. Dtsch Arztebl Int. 2021;118:195–6. PubMedPubMedCentral
12.
Zurück zum Zitat Schieber TJ, Bennett N, Aragon L, Ploetz J, Boyd S. Real-world risk evaluation of remdesivir in patients with an estimated glomerular filtration rate of less than 30 mL/min. Am J Health Syst Pharm. 2021. Schieber TJ, Bennett N, Aragon L, Ploetz J, Boyd S. Real-world risk evaluation of remdesivir in patients with an estimated glomerular filtration rate of less than 30 mL/min. Am J Health Syst Pharm. 2021.
13.
Zurück zum Zitat Pilgram L, Schons M, Jakob CEM, Classen AY, Franke B, Tscharntke L, Schulze N, Fuhrmann S, Sauer G, de Miranda SMN, et al. The COVID-19 pandemic as an opportunity and challenge for registries in health services research: lessons learned from the lean European open survey on SARS-CoV-2 infected patients (LEOSS). Gesundheitswesen. 2021;83:S45–53. CrossRef Pilgram L, Schons M, Jakob CEM, Classen AY, Franke B, Tscharntke L, Schulze N, Fuhrmann S, Sauer G, de Miranda SMN, et al. The COVID-19 pandemic as an opportunity and challenge for registries in health services research: lessons learned from the lean European open survey on SARS-CoV-2 infected patients (LEOSS). Gesundheitswesen. 2021;83:S45–53. CrossRef
14.
Zurück zum Zitat group TLs. LEOSS metadata on medical data models (mdm) portal. In .; 2021. group TLs. LEOSS metadata on medical data models (mdm) portal. In .; 2021.
15.
Zurück zum Zitat Jakob CEM, Kohlmayer F, Meurers T, Vehreschild JJ, Prasser F. Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19. Sci Data. 2020;7:435. CrossRef Jakob CEM, Kohlmayer F, Meurers T, Vehreschild JJ, Prasser F. Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19. Sci Data. 2020;7:435. CrossRef
17.
Zurück zum Zitat Team RC. R: A language and environment for statistical computing. In . Vienna, Austria: R Foundation for Statistical Computing; 2021. Team RC. R: A language and environment for statistical computing. In . Vienna, Austria: R Foundation for Statistical Computing; 2021.
18.
Zurück zum Zitat Jakob CEM, Borgmann S, Duygu F, Behrends U, Hower M, Merle U, Friedrichs A, Tometten L, Hanses F, Jung N, et al. First results of the “Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS).” Infection. 2021;49:63–73. CrossRef Jakob CEM, Borgmann S, Duygu F, Behrends U, Hower M, Merle U, Friedrichs A, Tometten L, Hanses F, Jung N, et al. First results of the “Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS).” Infection. 2021;49:63–73. CrossRef
19.
Zurück zum Zitat The RECOVERY Collaborative Group. Effect of hydroxychloroquine in hospitalized patients with Covid-19. N Engl J Med. 2020;383:2030–40. CrossRef The RECOVERY Collaborative Group. Effect of hydroxychloroquine in hospitalized patients with Covid-19. N Engl J Med. 2020;383:2030–40. CrossRef
20.
Zurück zum Zitat Kluge S, Janssens U, et al. S2k Leitlinie—Empfehlungen zur stationären Therapie von Patienten mit COVID-19. In . AWMF online. 2021. Kluge S, Janssens U, et al. S2k Leitlinie—Empfehlungen zur stationären Therapie von Patienten mit COVID-19. In . AWMF online. 2021.
21.
Zurück zum Zitat Group RC, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, Staplin N, Brightling C, Ustianowski A, et al. Dexamethasone in hospitalized patients with covid-19. N Engl J Med. 2021;384:693–704. CrossRef Group RC, Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, Staplin N, Brightling C, Ustianowski A, et al. Dexamethasone in hospitalized patients with covid-19. N Engl J Med. 2021;384:693–704. CrossRef
22.
Zurück zum Zitat Rochwerg B, Agarwal A, Siemieniuk RA, Agoritsas T, Lamontagne F, Askie L, Lytvyn L, Leo Y-S, Macdonald H, Zeng L, et al. A living WHO guideline on drugs for covid-19. BMJ. 2020;370:m3379. PubMed Rochwerg B, Agarwal A, Siemieniuk RA, Agoritsas T, Lamontagne F, Askie L, Lytvyn L, Leo Y-S, Macdonald H, Zeng L, et al. A living WHO guideline on drugs for covid-19. BMJ. 2020;370:m3379. PubMed
23.
Zurück zum Zitat Wu Z, McGoogan JM. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese center for disease control and prevention. JAMA. 2020;323:1239–42. CrossRef Wu Z, McGoogan JM. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese center for disease control and prevention. JAMA. 2020;323:1239–42. CrossRef
24.
Zurück zum Zitat Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–6. CrossRef Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, Curtis HJ, Mehrkar A, Evans D, Inglesby P, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–6. CrossRef
25.
Zurück zum Zitat Kikuchi K, Nangaku M, Ryuzaki M, Yamakawa T, Yoshihiro O, Hanafusa N, Sakai K, Kanno Y, Ando R, Shinoda T, et al. Survival and predictive factors in dialysis patients with COVID-19 in Japan: a nationwide cohort study. Ren Replace Ther. 2021;7:59. CrossRef Kikuchi K, Nangaku M, Ryuzaki M, Yamakawa T, Yoshihiro O, Hanafusa N, Sakai K, Kanno Y, Ando R, Shinoda T, et al. Survival and predictive factors in dialysis patients with COVID-19 in Japan: a nationwide cohort study. Ren Replace Ther. 2021;7:59. CrossRef
26.
Zurück zum Zitat Consortium C-CC, Group LS. Clinical presentation, disease course, and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease: a cohort study across 18 countries. Eur Heart J. 2021. Consortium C-CC, Group LS. Clinical presentation, disease course, and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease: a cohort study across 18 countries. Eur Heart J. 2021.
27.
Zurück zum Zitat Werfel S, Jakob CEM, Borgmann S, Schneider J, Spinner C, Schons M, Hower M, Wille K, Haselberger M, Heuzeroth H, et al. Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients. J Med Virol. 2021;93:6703–13. CrossRef Werfel S, Jakob CEM, Borgmann S, Schneider J, Spinner C, Schons M, Hower M, Wille K, Haselberger M, Heuzeroth H, et al. Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients. J Med Virol. 2021;93:6703–13. CrossRef
28.
Zurück zum Zitat Jakob CEM, Mahajan UM, Oswald M, Stecher M, Schons M, Mayerle J, Rieg S, Pletz M, Merle U, Wille K, et al. Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning. Infection. 2021;50:359. CrossRef Jakob CEM, Mahajan UM, Oswald M, Stecher M, Schons M, Mayerle J, Rieg S, Pletz M, Merle U, Wille K, et al. Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning. Infection. 2021;50:359. CrossRef
29.
Zurück zum Zitat Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. CrossRef Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497–506. CrossRef
30.
Zurück zum Zitat Team CC-R. Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019—United States, February 12-March 28, 2020. MMWR Morb Mortal Wkly Rep. 2020; 69:382–386. Team CC-R. Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019—United States, February 12-March 28, 2020. MMWR Morb Mortal Wkly Rep. 2020; 69:382–386.
31.
Zurück zum Zitat Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, Ma K, Xu D, Yu H, Wang H, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ. 2020;368:m1091. CrossRef Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, Ma K, Xu D, Yu H, Wang H, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ. 2020;368:m1091. CrossRef
32.
Zurück zum Zitat Taji L, Thomas D, Oliver MJ, Ip J, Tang Y, Yeung A, Cooper R, House AA, McFarlane P, Blake PG. COVID-19 in patients undergoing long-term dialysis in Ontario. CMAJ. 2021;193:E278–84. CrossRef Taji L, Thomas D, Oliver MJ, Ip J, Tang Y, Yeung A, Cooper R, House AA, McFarlane P, Blake PG. COVID-19 in patients undergoing long-term dialysis in Ontario. CMAJ. 2021;193:E278–84. CrossRef
33.
Zurück zum Zitat Couchoud C, Bayer F, Ayav C, Bechade C, Brunet P, Chantrel F, Frimat L, Galland R, Hourmant M, Laurain E, et al. Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients. Kidney Int. 2020;98:1519–29. CrossRef Couchoud C, Bayer F, Ayav C, Bechade C, Brunet P, Chantrel F, Frimat L, Galland R, Hourmant M, Laurain E, et al. Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients. Kidney Int. 2020;98:1519–29. CrossRef
34.
Zurück zum Zitat Zou R, Chen F, Chen D, Xu CL, Xiong F. Clinical characteristics and outcome of hemodialysis patients with COVID-19: a large cohort study in a single Chinese center. Ren Fail. 2020;42:950–7. CrossRef Zou R, Chen F, Chen D, Xu CL, Xiong F. Clinical characteristics and outcome of hemodialysis patients with COVID-19: a large cohort study in a single Chinese center. Ren Fail. 2020;42:950–7. CrossRef
35.
Zurück zum Zitat Savino M, Santhakumaran S, Evans KM, Steenkamp R, Benoy-Deeney F, Medcalf JF, Nitsch D. Outcomes of patients with COVID-19 on kidney replacement therapy: a comparison among modalities in England. Clin Kidney J. 2021;14:2573–81. CrossRef Savino M, Santhakumaran S, Evans KM, Steenkamp R, Benoy-Deeney F, Medcalf JF, Nitsch D. Outcomes of patients with COVID-19 on kidney replacement therapy: a comparison among modalities in England. Clin Kidney J. 2021;14:2573–81. CrossRef
36.
Zurück zum Zitat Carriazo S, Mas-Fontao S, Seghers C, Cano J, Goma E, Avello A, Ortiz A, Gonzalez-Parra E. Increased 1-year mortality in haemodialysis patients with COVID-19: a prospective, observational study. Clin Kidney J. 2022;15:432–41. CrossRef Carriazo S, Mas-Fontao S, Seghers C, Cano J, Goma E, Avello A, Ortiz A, Gonzalez-Parra E. Increased 1-year mortality in haemodialysis patients with COVID-19: a prospective, observational study. Clin Kidney J. 2022;15:432–41. CrossRef
37.
Zurück zum Zitat Jahn M, Korth J, Dorsch O, Anastasiou OE, Krawczyk A, Brochhagen L, van de Sand L, Sorge-Hadicke B, Tyczynski B, Witzke O et al. Decline of humoral responses 6 months after vaccination with BNT162b2 (Pfizer-BioNTech) in patients on hemodialysis. Vaccines (Basel). 2022; 10. Jahn M, Korth J, Dorsch O, Anastasiou OE, Krawczyk A, Brochhagen L, van de Sand L, Sorge-Hadicke B, Tyczynski B, Witzke O et al. Decline of humoral responses 6 months after vaccination with BNT162b2 (Pfizer-BioNTech) in patients on hemodialysis. Vaccines (Basel). 2022; 10.
Metadaten
Titel
SARS-CoV-2 infection in chronic kidney disease patients with pre-existing dialysis: description across different pandemic intervals and effect on disease course (mortality)
verfasst von
Lisa Pilgram
Lukas Eberwein
Bjoern-Erik O. Jensen
Carolin E. M. Jakob
Felix C. Koehler
Martin Hower
Jan T. Kielstein
Melanie Stecher
Bernd Hohenstein
Fabian Prasser
Timm Westhoff
Susana M. Nunes de Miranda
Maria J. G. T. Vehreschild
Julia Lanznaster
Sebastian Dolff
the LEOSS study group
Publikationsdatum
29.04.2022
Verlag
Springer Berlin Heidelberg
Schlagwort
COVID-19
Erschienen in
Infection
Print ISSN: 0300-8126
Elektronische ISSN: 1439-0973
DOI
https://doi.org/10.1007/s15010-022-01826-7

Corona-Update

Die aktuelle Entwicklung im Überblick: Nachrichten, Webinare, Übersichtsarbeiten.