Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2023 Feb;51(1):71-81.
doi: 10.1007/s15010-022-01826-7. Epub 2022 Apr 29.

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

Lisa Pilgram  1   2 , Lukas Eberwein  3 , Bjoern-Erik O Jensen  4 , Carolin E M Jakob  5   6 , Felix C Koehler  7   8 , Martin Hower  9 , Jan T Kielstein  10 , Melanie Stecher  5   6 , Bernd Hohenstein  11 , Fabian Prasser  12 , Timm Westhoff  13 , Susana M Nunes de Miranda  5 , Maria J G T Vehreschild  14 , Julia Lanznaster  15 , Sebastian Dolff  16 ; LEOSS study group
Collaborators, Affiliations
Multicenter Study

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

Lisa Pilgram et al. Infection. 2023 Feb.

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.

Keywords: CKD5D; COVID-19; Hemodialysis; Kidney; SARS-CoV-2.

PubMed Disclaimer

Conflict of interest statement

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.

Figures

Fig. 1
Fig. 1
Study flow chart. We extracted patients suffering from CKD from LEOSS and applied the indicated exclusion criteria. Patients on dialysis were described and compared throughout the different phases of pandemic using the original data set. Missing values were multiply imputed. Missing analyses are displayed in Table S1. Each case of each imputed data set was matched via propensity score matching to one control. The latter was defined as patients suffering from CKD not requiring dialysis. Results of the conditional logistic regression stratified by dialysis were pooled across the 5 imputed data sets. CKD: chronic kidney disease. LEOSS: Lean European Open Survey on SARS-CoV-2-infected patients
Fig. 2
Fig. 2
Diagnostic parameters for SARS-CoV-2-infected patients on hemodialysis at first diagnosis of COVID-19 in the different intervals of COVID-19 pandemic. Proportion referred to the numbers excluding missing data (missing details in Table S1) and numbers in the specified categories of the indicated diagnostic parameters are displayed using the unimputed data set. The diagnostic parameters were determined closest to the first diagnosis but did not exceed 48 h after SARS-CoV-2 positive testing. Timing of first diagnosis was aggregated into three intervals of pandemic based on the epidemiological waves in Germany: January 2020–June 2020, July 2020–January 2021 and February 2021–May 2021; diagnostic assessment into three categories as defined in the legend. CKD: chronic kidney disease. SO2: oxygen saturation in arterial blood. CRP: C-reactive protein. LDH: lactate dehydrogenase. ULN: upper limit of normal in the respective local laboratory
Fig. 3
Fig. 3
Forest plot of odds ratios from conditional regression analyses on fatal outcome stratified by dialysis. Univariate and multivariable regression analyses were performed after propensity-score matching in the imputed and unimputed data sets with the respective (adjusted) odds ratio ((a)OR) displayed in the figure. 95% confidence interval is plotted for the aOR of the multivariable conditional regression of imputed data. 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
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Coronavirus (COVID-19) Dashboard. https://covid19.who.int/.
    1. 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;46:1114–1116. doi: 10.1007/s00134-020-06026-1. - DOI - PMC - PubMed
    1. 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;31:1380–1383. doi: 10.1681/ASN.2020040419. - DOI - PMC - PubMed
    1. 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;396:597–598. doi: 10.1016/S0140-6736(20)31759-1. - DOI - PMC - PubMed
    1. 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. doi: 10.1053/j.ajkd.2020.09.003. - DOI - PMC - PubMed

Publication types