Evidence map›Paper›PMID 42018239›Full record

ReviewClinical and experimental nephrology2026

Review no. 3: handling of longitudinal creatinine data to define acute kidney injury.

Yoshihisa Miyamoto, Yuka Sugawara, Megumi Oshima, Hajime Nagasu, Takashige Kuwabara, Tadashi Sofue, Naoki Nakagawa, Masao Iwagami

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Clinical and experimental nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yoshihisa MiyamotoDepartment of Real-World Evidence, The University of Tokyo, Tokyo, Japan.
Yuka SugawaraDivision of Nephrology and Endocrinology, The University of Tokyo, Tokyo, Japan.
Megumi OshimaSubcommittee for the Promotion of Clinical Research, The Japanese Society of Nephrology, Tokyo, Japan.
Hajime NagasuSubcommittee for the Promotion of Clinical Research, The Japanese Society of Nephrology, Tokyo, Japan.
Takashige KuwabaraSubcommittee for the Promotion of Clinical Research, The Japanese Society of Nephrology, Tokyo, Japan.
Tadashi SofueSubcommittee for the Promotion of Clinical Research, The Japanese Society of Nephrology, Tokyo, Japan.
Naoki NakagawaDivision of Cardiology and Nephrology, Department of Internal Medicine, Asahikawa Medical University, Asahikawa, Japan.
Masao IwagamiDepartment of Digital Health, Institute of Medicine, University of Tsukuba, Ibaraki, Japan. iwagami@md.tsukuba.ac.jp.ORCID http://orcid.org/0000-0001-7079-0640

Funding

Ministry of Health, Labour and Welfare 23AA2003
6 · The paper itself

Abstract

Acute kidney injury (AKI) is characterized by a sudden decline in kidney function. The Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guidelines define AKI based on an increase in serum creatinine (SCr) levels as well as a decrease in urine volume. For clinical researchers of AKI, the ability to handle longitudinal SCr data and flag AKI status and stage in individual patients is a fundamental skill. This article provides a practical guide for identifying AKI episodes from longitudinal SCr data using R programming. The methods described in this article are based on a hands-on seminar presented at the 68th Annual Meeting of the Japanese Society of Nephrology in 2025.

Indexed as

Acute Kidney InjuryCreatinineBiomarkersHumansBiomarkersCreatinineAcute kidney injuryKDIGO criteriaReal-world dataSerum creatinine

Identifiers

PMID42018239
PMCPMC13242417

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.