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Record W4407392447 · doi:10.3390/jcm14041212

Influence of Baseline Kidney Function on Patient and Kidney Outcomes in Patients with COVID-19: A Multi-National Observational Study

2025· article· en· W4407392447 on OpenAlexaff
Harin Rhee, Etienne Macedo, Gary Cutter, Eric Judd, Sreejith Parameswaran, Elizabeth Maccariello, Wen-Jiun Liu, Nicholas M. Selby, Josée Bouchard, Guillermo García-García, Javier A. Neyra, Manjusha Yadla, Josephine Abraham, Kent Doi, Guillermo Villamizar, Abdías Hurtado, Ravindra L. Mehta

Bibliographic record

VenueJournal of Clinical Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsHôpital du Sacré-Cœur de Montréal
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCenters for Disease Control and Prevention
KeywordsMedicineKidney diseaseRenal functionAcute kidney injuryInternal medicineIntensive care unitKidneyRetrospective cohort studyIntensive care medicine

Abstract

fetched live from OpenAlex

Background/Objectives: Acute kidney injury (AKI) is a common complication of coronavirus disease-19 (COVID-19), but the impact of baseline kidney function and care processes on outcomes is not well understood. We hypothesized that baseline kidney health status may influence courses and outcomes of AKI. Methods: This is a multinational, multicenter, retrospective cohort study. We included hospitalized adult COVID-19 patients with kidney disease (AKI, end-stage kidney disease (ESKD), chronic kidney disease (CKD), or kidney transplant (KT) recipients) from 1 January 2020 to 31 March 2022, across 52 centers in 23 countries. Patients with no prior kidney function information were classified as acute kidney disease (AKD) if estimated glomerular filtration rate (eGFR) at admission was <60 mL/min/1.73 m2 and as no known kidney disease (NKD) if eGFR was ≥60 mL/min/1.73 m2. We defined combined outcome as death or non-kidney recovery at hospital discharge. Multivariable binary regression models were applied. Results: Among 4158 patients, 882 had ESKD, and 3038 developed AKI. AKI patients were categorized as NKD (31.8%), AKD (38.6%), CKD (23.3%), and KT recipients (3.3%). NKD patients had higher AKI severity and more intensive care unit care needs. In the multivariable analyses, the risk of the combined outcome was higher in AKD (OR 1.459 [1.061, 2.005]) or CKD (OR 1.705 [1.206, 2.410]) patients, although the risk of in-hospital mortality was similar to NKD. Among the survivors at hospital discharge, the risk of partial or non-recovery was higher in CKD (OR 5.445 [3.864, 7.672]) or KT recipients (OR 4.208 [2.383, 7.429]) compared to NKD. These findings were consistent across income categories. Conclusions: Among AKI patients with COVID-19, nearly two-thirds had underlying kidney dysfunction, with 55% identified as having baseline AKD, which had higher risk of death or non-kidney recovery at discharge compared to NKD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.158
GPT teacher head0.493
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2025
Admission routes1
Has abstractyes

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