Evidence map›Paper›PMID 42125697›Full record

ArticleFrontiers in oncology2026

Development of a prediction model for acute kidney injury in critically ill patients with advanced colorectal cancer based on white blood cell-related indicators.

Lingjie Li, Pu Zhou, YuanJin Guo, Haragakiza Jean Docile, David Fisher, Nguyen Thi Thu Hien, Erkin Musabaev, Vadim V Shmanai, Yiping Dang, Zhide Zhu and 2 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 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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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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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

12 authors.

Lingjie Li *China Three Gorges University, Yichang, Hubei, China.
Pu Zhou *Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
YuanJin Guo *Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Haragakiza Jean DocileDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
David FisherDepartment of Medical Biosciences, Faculty of Natural Sciences, University of the Western Cape, Cape Town, South Africa.
Nguyen Thi Thu HienHai Phong University of Medicine and Pharmacy, Hai Phong, Vietnam.
Erkin MusabaevThe Research Institute of Virology, Ministry of Health, Tashkent, Uzbekistan.
Vadim V ShmanaiInstitute of Physical Organic Chemistry, National Academy of Sciences of Belarus, Minsk, Belarus.
Yiping DangDepartment of Vascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhide ZhuThe First Clinical Medical College, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Lei ZhaoDepartment of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Jie XuDepartment of Spleen and Stomach Diseases, Yichang Hospital of Chinese Medicine & China Three Gorges University Chinese Medicine Hospital, Yichang, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Currently, most prediction models for acute kidney injury (AKI) in patients with colorectal cancer (CRC) are only applicable to postoperative situations, or rely on complex machine learning algorithms. There is currently no simple and feasible AKI prediction model for severely ill patients with advanced CRC. Objective: This study endeavors to devise and validate a novel clinical risk prediction model utilizing white blood cell (WBC)-related indicators to forecast AKI in critically ill patients with advanced CRC. Methods: A retrospective cohort study design was employed. The training cohort was derived from the eICU Collaborative Research Database, and the external validation cohort came from the Intensive Care Unit (ICU) of Wuhan Union Hospital. To screen for independent predictors of AKI and develop a new risk score model using multivariable logistic regression analysis. The assessment of model performance was conducted using receiver operating characteristic (ROC) curve analysis and compared with other scoring systems. The associations between different risk groups and survival outcomes as well as WBC counts were visualized using a heatmap. Kaplan-Meier survival analysis was used to compare in-hospital survival rates between the two groups. ROC curve analysis and decision curve analysis (DCA) served to evaluate the model's predictive ability for in-hospital mortality. Subgroup analysis was applied to assess the consistency of this model across different critically ill cohorts with advanced CRC. Results: A total of 981 critically ill patients with advanced CRC were included, of whom 185 (24.2%) from the eICU database and 59 (27.2%) from the Wuhan Union Hospital ICU developed AKI. A new risk model was successfully established: Conclusion: This study developed a novel prediction model for AKI in critically ill patients with advanced CRC using WBC-related indicators. This model offers a simple, readily implementable tool for AKI risk stratification in critically ill patients with advanced CRC, supporting early clinical intervention and prognostic assessment.

Indexed as

acute kidney injurycolorectal cancereICUprediction modelwhite blood cell

Identifiers

PMID42125697
PMCPMC13158105

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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.