Evidence map›Paper›PMID 36035616›Full record

ArticleKidney medicine2022

COVID-19 and Hospitalization Among Maintenance Dialysis Patients: A Retrospective Cohort Study Using Time-Dependent Modeling.

Xuemei Ding, Xi Wang, Garrett W Gremel, Kevin He, Jian Kang, Joseph M Messana, Claudia Dahlerus, Wenbo Wu, Richard A Hirth, John D Kalbfleisch

Abstract read
In one paragraph

Article in Kidney medicine, 2022. 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
–field-weighted citation impact
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.

  1. Article
  2. Competing Risk Modeling with Bivariate Varying Coefficients to Understand the Dynamic Impact of COVID-19.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024
    Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Xuemei DingKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Xi WangKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Garrett W GremelKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Kevin HeKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Jian KangKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Joseph M MessanaKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Claudia DahlerusKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Wenbo WuKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Richard A HirthKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.
John D KalbfleischKidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor, Michigan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale & Objective: The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on hospitalizations in general and on dialysis patients in particular. This study modeled the impact of COVID-19 on hospitalizations of dialysis patients in 2020. Study Design: Retrospective cohort study. Setting & Participants: Medicare patients on dialysis in calendar year 2020. Predictors: COVID-19 status was divided into 4 stages: COVID1 (first 10 days after initial diagnosis), COVID2 (extends until the Post-COVID stage), Post-COVID (after 21 days with no COVID-19 diagnosis), and Late-COVID (begins after a hospitalization with a COVID-19 diagnosis); demographic and clinical characteristics; and dialysis facilities. Outcome: The sequence of hospitalization events. Analytical Approach: A proportional rate model with a nonparametric baseline rate function of calendar time on the study population. Results: A total of 509,609 patients were included in the study, 63,521 were observed to have a SARS-CoV-2 infection, 34,375 became Post-COVID, and 1,900 became Late-COVID. Compared with No-COVID, all 4 stages had significantly greater adjusted risks of hospitalizations with relative rates of 18.50 (95% CI, 18.19-18.81) for COVID1, 2.03 (95% CI, 1.99-2.08) for COVID2, 1.37 (95% CI, 1.35-1.40) for Post-COVID, and 2.00 (95% CI, 1.89-2.11) for Late-COVID. Limitations: For Medicare Advantage patients, we only had inpatient claim information. The analysis was based on data from the year 2020, and the effects may have changed due to vaccinations, new treatments, and new variants. The COVID-19 effects may be somewhat overestimated due to missing information on patients with few or no symptoms and possible delay in COVID-19 diagnosis. Conclusions: We discovered a marked time dependence in the effect of COVID-19 on hospitalization of dialysis patients, beginning with an extremely high risk for a relatively short period, with more moderate but continuing elevated risks later, and never returning to the No-COVID level.

Indexed as

COVID-19dialysishospital admissionsnonparametric baseline ratetime dependent effect

Identifiers

PMID36035616
PMCPMC9398821

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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.