Evidence map›Paper›PMID 42440486›Full record

ArticleFrontiers in medicine2026

Development and internal validation of a prediction model for early identification of sepsis-associated acute kidney injury based on admission serum biomarkers: a retrospective cohort study.

Chi Wang, Rui Ye, Xingxin Gong, Meng Tang, Fei Ding, Yi Xie, Yanxi Sheng, Xin Nie, Yong He

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Article in Frontiers in medicine, 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

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Chi WangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Rui YeDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xingxin GongDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Meng TangDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Fei DingDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yi XieDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yanxi ShengDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xin NieDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yong HeDepartment of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Sepsis-associated acute kidney injury (SA-AKI) is a critical complication that substantially increases intensive care unit mortality. Early identification is paramount for timely intervention. This study aimed to develop and internally validate a prediction model relying exclusively on the first serum laboratory indicators after hospital admission to predict SA-AKI risk at the earliest available laboratory assessment. Methods: Clinical data of 1,573 sepsis patients admitted to West China Hospital of Sichuan University (January 2024-December 2025) were retrospectively analyzed. Patients were divided into SA-AKI and non-SA-AKI groups per 2012 KDIGO criteria, and randomly split into training (70%, Results: LASSO regression selected 10 serum indicators, and multivariate logistic regression confirmed 8 independent risk factors: myoglobin (MYO), alanine aminotransferase (ALT), phosphorus (PO Conclusions: This study developed and internally validated a promising predictive model for estimating SA-AKI risk in sepsis patients using solely first routine serum laboratory indicators after admission. A nomogram is provided for individualized bedside risk estimation. This tool may support early risk stratification of high-risk individuals. External validation in multi-center, diverse cohorts is warranted before broader clinical implementation.

Indexed as

acute kidney injuryprediction modelrisk stratificationsepsisserum biomarkers

Identifiers

PMID42440486
PMCPMC13333455

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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.