Evidence map›Paper›PMID 41095972›Full record

ArticleJournal of clinical medicine2025

Predicting Acute Kidney Injury in Acute Rhabdomyolysis.

Andy K H Lim

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

1 author.

Andy K H LimDepartment of General Medicine, Monash Health, Clayton, VIC 3168, Australia.ORCID 0000-0001-7816-4724

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rhabdomyolysis is a clinical syndrome of significant skeletal muscle damage leading to electrolyte disturbance and kidney toxicity, which may result in acute kidney injury. The short-term impact of acute kidney injury includes a severity-dependent increased mortality and need for renal replacement therapy, while long-term effects include development and progression of chronic kidney disease, and increased cardiovascular risk. The ability to predict acute kidney injury early in the presentation is valuable for providing tailored preventative strategies, planning the intensity of monitoring, and appropriate resource allocation. Several clinical variables and biomarkers are reported to be associated with rhabdomyolysis-associated acute kidney injury, and a number of prediction models have been developed for this purpose. However, heterogeneity in study populations and methodology poses challenges to the utility and clinical integration of these variables and prediction models. This article explores and summarizes some of the relevant variables and models used to predict acute kidney injury in rhabdomyolysis, and discusses the uncertainties around the traditional biomarkers like creatine kinase and myoglobin, along with insights from recent observational studies.

Indexed as

acute kidney injurycreatine kinaseepidemiologypredictionrenal replacement therapyrhabdomyolysisstatistical model

Identifiers

PMID41095972
PMCPMC12525492

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.