Identifying acute kidney injury subtypes based on serum electrolyte data in ICU via

Wentie Liu, Tongyue Shi, Haowei Xu, Huiying Zhao, Jianguo Hao, Guilan Kong

Abstract read

In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Large Language Models in Critical Care Medicine: Scoping Review.JMIR medical informatics · 2025
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

6 authors.

Wentie LiuNational Institute of Health Data Science, Peking University, Beijing, China.

Tongyue ShiNational Institute of Health Data Science, Peking University, Beijing, China.

Haowei XuNational Institute of Health Data Science, Peking University, Beijing, China.

Huiying ZhaoDepartment of Critical Care Medicine, Peking University People's Hospital, Beijing, China.

Jianguo HaoNational Institute of Health Data Science, Peking University, Beijing, China.

Guilan KongNational Institute of Health Data Science, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement.

Indexed as

Acute Kidney InjuryElectrolytesCluster AnalysisHospital MortalityHumansIntensive Care UnitsPrognosisRenal Replacement TherapyElectrolytes

Identifiers

PMID40417583

PMCPMC12099402

What OpenQuestion holds

Textmetadata

Read underepoch 390

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.