Evidence map›Paper›PMID 37637818›Full record

ArticleFrontiers in public health2023

Reduced COVID-19 morbidity and mortality in hemodialysis patients across the various Omicron sublineages-A retrospective analysis.

Max Schuller, Noemi Elisabeth Ginthör, Astrid Paller, Maximilian Waller, Martin Köstenbauer, Nikolaus Gustav Oskar Schreiber, Corinna Schabhüttl, Kathrin Mischinger, Hildegard Hafner-Giessauf, Alexander R Rosenkranz and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.4field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 1 country.

Max SchullerDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Noemi Elisabeth GinthörDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Astrid PallerDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Maximilian WallerDepartment of Medicine I, Klinik Favoriten, Vienna, Austria.
Martin KöstenbauerDepartment of Internal Medicine, Krankenhaus der Barmherzigen Brüder Graz, Graz, Austria.
Nikolaus Gustav Oskar SchreiberDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Corinna SchabhüttlDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Kathrin MischingerDialysezentrum Graz-West, Graz, Austria.
Hildegard Hafner-GiessaufDialyseinstitut Gießauf Gmbh, Graz, Austria.
Alexander R RosenkranzDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Philipp EllerIntensive Care Unit, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Kathrin EllerDivision of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Medical University of Graz · ATHospital of the Brothers of St. John of God · ATMedical University of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Hemodialysis (HD) patients are a COVID-19 high risk population due to comorbidities and impaired immune response. Vaccines, advent of effective treatment and the emergence of novel variants have fundamentally changed the pandemic. We aimed to assess temporal changes of COVID-19 in HD patients of our catchment area, and risk factors for severe and fatal course. Methods and materials: We retrospectively collected data from 274 patients admitted to the Medical University Graz, Austria for HD between 1st of May 2020 and 31st of August 2022. We analyzed clinical and demographic data between different COVID-19 waves and assessed factors associated with hospitalization, ICU admission and mortality by logistic regression. To further evaluate the dialysis at-risk population, we collected demographic and vaccination data between August 2021 and August 2022. Results: Time of infection and SARS-CoV-2 sequencing data allowed for distinction of five separate waves of infection with different impact on the dialysis population: While in the initial four waves frequencies of hospitalization, necessity of critical care and mortality were around 60%, 10% and 20%, respectively. These events became rare during the large fifth wave, when Omicron had become the dominant variant. Although only 16.9% had to be hospitalized, this resulted in 29 hospital admissions, due to the high prevalence of COVID-19 during the Omicron era. Furthermore, we observed similar clinical outcomes with BA.4/5 as with BA.1/BA.2 Omicron sublineages. The proportion of previously infected increased simultaneously with the number of vaccination doses in our dialysis population. Vaccination at time of positivity and infection with an Omicron variant conferred protection against hospitalization and mortality in univariate analysis, but only infection with an Omicron variant remained a robust predictor for these outcomes in multivariable analysis. Discussion: While a fourth of our at-risk population became infected during the Omicron wave, mortality was almost non-existent. Several concomitant factors have contributed to the decrease of COVID-19 severity in HD patients. This trend appears to be continued with BA.4/5, which was equally mild as BA.1 and BA.2 in our well vaccinated dialysis population.

Indexed as

COVID-19HumansMorbidityRenal DialysisRetrospective StudiesSARS-CoV-2BA.4/5COVID-19hemodialysisOmicronvaccination

Identifiers

PMID37637818
PMCPMC10448811
OpenAlexW4385737508

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.