Evidence map›Paper›PMID 40950944›Full record

ArticleThe Lancet regional health. Europe2025

Long-term kidney outcomes after COVID-19: a matched cohort study using the OpenSAFELY platform.

Viyaasan Mahalingasivam, Bang Zheng, Kevin Wing, Edward P K Parker, Krishnan Bhaskaran, Juan Jesús Carrero, Sandra Jayacodi, Edith Jumbo, Tamanna Miah, Brian Gracey and 21 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Lancet regional health. Europe, 2025. 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. Article
  3. Article
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

31 authors.

Viyaasan MahalingasivamDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Bang ZhengDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Kevin WingGlasgow Lab for Health Data Science & AI, Public Health, School of Health & Wellbeing, University of Glasgow, Glasgow, UK.
Edward P K ParkerNIHR Health Protection Research Unit in Vaccines and Immunisation, London School of Hygiene & Tropical Medicine, London, UK.
Krishnan BhaskaranDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Juan Jesús CarreroDepartment of Medical Epidemiology & Biostatistics, Karolinska Institutet, Solna, Sweden.
Sandra JayacodiPatient and Public Involvement Partner, UK.
Edith JumboPatient and Public Involvement Partner, UK.
Tamanna MiahPatient and Public Involvement Partner, UK.
Brian GraceyPatient and Public Involvement Partner, UK.
John TazareDepartment of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.
Shalini SanthakumaranUK Kidney Association, Bristol, UK.
Rohini MathurWolfson Institute of Population Health, Queen Mary University of London, London, UK.
Ruth E CostelloDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Emily HerrettDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Qing WenDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Thomas HartneyDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Ian J DouglasDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Amelia GreenBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Louis FisherBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Helen J CurtisBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Alex J WalkerBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Brian MacKennaBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
William J HulmeBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Amir MehrkarBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Sebastian BaconBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Ben GoldacreBennett Institute for Applied Data Science, University of Oxford, Oxford, UK.
Elizabeth WilliamsonDepartment of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK.
Dorothea NitschDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Kathryn E MansfieldSchool of Health and Care Sciences, University of Lincoln, Lincoln, UK.
Laurie TomlinsonDepartment of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: COVID-19 severe enough to require hospitalisation is commonly associated with acute kidney injury. However, it remains unclear whether COVID-19 leads to long-term kidney outcomes in the broader population. Methods: We undertook a population-based, matched cohort study. With the approval of NHS England, we used primary and secondary care electronic health records from England using the OpenSAFELY-TPP platform. We compared people with and without COVID-19 using fully-adjusted, stratified, cause-specific Cox models for kidney failure, 50% reduction in kidney function, and death. Findings: Overall, all outcomes were increased after COVID-19 over the course of follow-up (HR for kidney failure 1.93 [95% CI 1.84-2.03]). Hazards of kidney failure were greatest after hospitalisation (HR 7.74 [95% CI 7.00-8.56]) and remained increased beyond 180 days of follow-up. There was no evidence of increased risk in those not hospitalised (HR 0.85 [95% CI 0.79-0.90]). Increased kidney failure was more pronounced in black ethnicity (HR 4.50 [95% CI 2.92-6.92]) compared to white ethnicity (HR 1.82 [95% CI 1.71-1.94]). Amongst those hospitalised with COVID-19, there was no attenuation of kidney failure between the first wave (HR 8.74 [95% CI 6.88-11.08]) and the Omicron wave (HR 8.36 [95% CI 6.81-10.27]). Interpretation: We observed increased long-term kidney outcomes in people hospitalised with COVID-19, as well as notable ethnic differences. Our results suggest strategies to minimise severe COVID-19 should continue to be optimised among vulnerable groups, and that kidney function should be proactively monitored after hospital discharge. Funding: National Institute for Health and Care Research.

Indexed as

Chronic kidney diseaseCKDCOVID-19End-stage kidney diseaseEnd-stage renal diseaseESKDESRDKidneyKidney failure

Identifiers

PMID40950944
PMCPMC12426834

What OpenQuestion holds

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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.