Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury

Critical Care

Research

Kun Chen¹  Lin Chen¹  Jian Gao²  Decong Wang²  Yucai Hong³  Zhongheng Zhang³  Pengfei Shui⁴  Huilin Jiang⁵  Yanling Li⁵  Yujing Sun⁶  Huiheng Liu⁶ 

[1] Department of Critical Care Medicine, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, People’s Republic of China;Department of Critical Medicine, Pi County Peoples Hospital, Chengdu, People’s Republic of China;Department of Emergency Medicine, Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310016, Hangzhou, People’s Republic of China;Department of Emergency, People’s Hospital of Anji, Anji County, Zhejiang, People’s Republic of China;Emergency Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China;Emergency Department, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, People’s Republic of China;

关键词: Sepsis;    Acute kidney injury;    Support vector machine;    RNA-seq;    Genetic algorithms;   

DOI  :  10.1186/s13054-022-04234-3

 received in 2022-06-22, accepted in 2022-11-10,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundAcute kidney injury (AKI) is a common complication in sepsis. However, the trajectories of sepsis-induced AKI and their transcriptional profiles are not well characterized.MethodsSepsis patients admitted to centres participating in Chinese Multi-omics Advances In Sepsis (CMAISE) from November 2020 to December 2021 were enrolled, and gene expression in peripheral blood mononuclear cells was measured on Day 1. The renal function trajectory was measured by the renal component of the SOFA score (SOFArenal) on Days 1 and 3. Transcriptional profiles on Day 1 were compared between these renal function trajectories, and a support vector machine (SVM) was developed to distinguish transient from persistent AKI.ResultsA total of 172 sepsis patients were enrolled during the study period. The renal function trajectory was classified into four types: non-AKI (SOFArenal = 0 on Days 1 and 3, n = 50), persistent AKI (SOFArenal > 0 on Days 1 and 3, n = 62), transient AKI (SOFArenal > 0 on Day 1 and SOFArenal = 0 on Day 3, n = 50) and worsening AKI (SOFArenal = 0 on Days 1 and SOFArenal > 0 on Day 3, n = 10). The persistent AKI group showed severe organ dysfunction and prolonged requirements for organ support. The worsening AKI group showed the least organ dysfunction on day 1 but had higher serum lactate and prolonged use of vasopressors than the non-AKI and transient AKI groups. There were 2091 upregulated and 1,902 downregulated genes (adjusted p < 0.05) between the persistent and transient AKI groups, with enrichment in the plasma membrane complex, receptor complex, and T-cell receptor complex. A 43-gene SVM model was developed using the genetic algorithm, which showed significantly greater performance predicting persistent AKI than the model based on clinical variables in a holdout subset (AUC: 0.948 [0.912, 0.984] vs. 0.739 [0.648, 0.830]; p < 0.01 for Delong’s test).ConclusionsOur study identified four subtypes of sepsis-induced AKI based on kidney injury trajectories. The landscape of host response aberrations across these subtypes was characterized. An SVM model based on a gene signature was developed to predict renal function trajectories, and showed better performance than the clinical variable-based model. Future studies are warranted to validate the gene model in distinguishing persistent from transient AKI.

【 授权许可】

CC BY   
© The Author(s) 2022

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