Evidence map›Paper›PMID 41229773›Full record

ArticleJournal of thoracic disease2025

Development and validation of a predictive model for continuous renal replacement therapy in patients undergoing venovenous extracorporeal membrane oxygenation.

Jinquan Xie, Luyin Han, Yaqi Ouyang, Songqiao Liu, Zhenting Liang, Yin Xi, Xiaoli Chen, Ya Wang, Jianchun Li, Yikun Liu and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of thoracic disease, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

20 authors.

Jinquan Xie *Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Luyin Han *Intensive Care Unit, West China Hospital, Sichuan University, Chengdu, China.
Yaqi Ouyang *Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Songqiao Liu *Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Zhenting Liang *Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Yin Xi *Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Xiaoli ChenDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Ya WangDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Jianchun LiDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Yikun LiuDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Yutong HeDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Jiao LiDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Li ZhaoDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Lihui YangDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Wei LaiIntensive Care Unit, West China Hospital, Sichuan University, Chengdu, China.
You ShangDepartment of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Lingjuan LiuJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Xiaoqing LiuDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Yimin LiDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.
Yonghao XuDepartment of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory and Health, Medical Center for Respiratory Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Acute kidney injury (AKI) and fluid overload are common complications of venovenous extracorporeal membrane oxygenation (VV-ECMO) and are associated with poor outcomes. Continuous renal replacement therapy (CRRT) is often required to manage these complications, but its initiation significantly increases mortality. Currently, there is no accurate prediction model tailored for CRRT in VV-ECMO patients. Therefore, this study aims to develop and validate a risk prediction model for CRRT initiation in VV-ECMO patients. Methods: This retrospective, multicenter study included patients who underwent VV-ECMO at four hospitals in China. The derivation cohort comprised patients from one hospital, while data from three other hospitals were utilized for external validation. Candidate predictors were selected using logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm, and subsequently visualized in a nomogram. Results: A total of 234 patients were included in the study, with 130 patients in the derivation cohort and 104 patients in the external validation cohort. Among these, 57 patients (43.85%) in the derivation cohort and 36 patients (34.62%) in the external validation cohort required CRRT. The final model incorporated the following predictors: coronary artery disease (CAD) {odds ratio (OR) [95% confidence interval (CI)]: 12.58 (3.60-44.03), P<0.001}, Sequential Organ Failure Assessment (SOFA) score [1.28 (1.10-1.48), P=0.001], platelet count (PLT) [0.99 (0.99-0.99), P=0.02], hemoglobin (HB) level [0.98 (0.96-0.99), P=0.03], and blood urea nitrogen (BUN) [1.04 (0.96-1.13), P=0.31]. The model exhibited the area under the curve (AUC) of 0.88 in the derivation cohort, and 0.75 in external validation. Conclusions: This predictive model, incorporating five key predictors, CAD, SOFA score, PLT, HB, and BUN, serves as a practical and reliable tool for assessing CRRT initiation in VV-ECMO patients, facilitating early risk stratification, timely renal risk assessment and implementation of nephroprotective strategies. Further prospective multicenter validation is needed to confirm its generalizability.

Indexed as

continuous renal replacement therapy (CRRT)multivariable analysisnomogramrisk prediction modelVenovenous extracorporeal membrane oxygenation (VV-ECMO)

Identifiers

PMID41229773
PMCPMC12603540

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