Evidence map›Paper›PMID 42535210›Full record

ArticleTrauma surgery & acute care open2026

Development of a risk calculator for acute kidney injury in trauma patients: an experimental study.

Emad Ramadan, Richard Cook, Raman Srinivasan, Alan Babuji, Deborah Latham, Daniel Gunn, Michael Foreman, Saravanan Ramamoorthy

Abstract read
In one paragraph

Article in Trauma surgery & acute care open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Emad RamadanTexas A&M University System, College Station, Texas, USA.ORCID https://orcid.org/0009-0007-9821-9244
Richard CookUniversity of Texas McGovern Medical School, Houston, Texas, USA.ORCID https://orcid.org/0009-0001-0945-336X
Raman SrinivasanBaylor Scott & White Health, Dallas, Texas, USA.
Alan BabujiTexas A&M University System, College Station, Texas, USA.
Deborah LathamU.S. Anesthesia Partners (USAP), Dallas, Texas, USA.
Daniel GunnBaylor Scott & White Health, Dallas, Texas, USA.
Michael ForemanBaylor Scott & White Health, Dallas, Texas, USA.
Saravanan RamamoorthyTexas A&M University System, College Station, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute kidney injury (AKI) is a frequent and serious complication in trauma populations, associated with significant morbidity and mortality. Current risk stratification tools, derived from general medical populations, often lack trauma-specific physiological variables and demonstrate limited applicability in these patients. This study aimed to develop and validate a specialized risk prediction model for the development of AKI within 72 hours of hospitalization in trauma patients requiring surgical intervention. Methods: A retrospective cohort study was conducted involving 157 consecutive trauma patients requiring surgical intervention at a level I trauma center (2020-2022). AKI was defined and staged according to the Kidney Disease Improving Global Outcomes criteria. Multivariable logistic regression analysis was used to identify independent predictors and develop a risk model, which was then internally validated using a prospective cohort of 53 additional trauma patients. Results: Independent predictors of AKI included advanced age, decreased admission estimated glomerular filtration rate, higher Injury Severity Scores, and lower hemoglobin levels. While body mass index and diabetes mellitus were not independent predictors, their inclusion in the composite model improved predictive accuracy. The final model demonstrated reliable discriminative ability, with an area under the curve of 0.91. Prospective validation confirmed this performance, achieving a probability threshold of 0.91 with only two observed false negatives. Conclusions: This novel trauma-specific risk calculator, using an additive weighting methodology, provides clinicians with a reliable tool for early AKI identification. By incorporating both patient characteristics and injury-related variables, the model may facilitate the timely initiation of renal-protective measures in high-risk trauma populations. Level of evidence: Level II, prognostic/epidemiological.

Indexed as

Acute Kidney Injurypostoperative complicationsrisk factorwounds and injuries

Identifiers

PMID42535210
PMCPMC13422796

What Socratic holds

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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 Socratic graph.