---
title: "Immuno-inflammatory endotype predicts sepsis-associated acute kidney injury: XGBoost model integra"
id: "pubmed-42661565"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42661565"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42661565/"
doi: "10.23876/j.krcp.26.112"
published_at: "2026-08-28T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Immuno-inflammatory endotype predicts sepsis-associated acute kidney injury: XGBoost model integra
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42661565
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42661565/)
- **DOI:** [10.23876/j.krcp.26.112](https://doi.org/10.23876%2Fj.krcp.26.112)
- **Published At:** 2026-08-28T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- This multicenter prospective cohort enrolled 1,551 septic patients across five tertiary hospitals in Beijing to study risk factors for **sepsis-associated acute kidney injury (SA-AKI)**. - Clinical data and a panel of **immuno-inflammatory biomarkers** (humoral markers, complement, and T-lymphocyte subsets) were collected within 24 hours of sepsis diagnosis. - New-onset SA-AKI occurred in 44.8% of the cohort and was associated with higher mortality compared with non-AKI patients. - A distinct high-risk **immuno-inflammatory endotype** was identified: elevated immunoglobulin A and **procalcitonin (PCT)**, depletion of complement component 3 (C3), and a complex cellular immune pattern with depleted naïve CD4+ T cells alongside expanded CD28+CD4+ T cells. - An Extreme Gradient Boosting (**XGBoost**) model combining immune features with routine clinical variables (APACHE II, **SOFA**, PCT, prothrombin time, sex) was developed to predict SA-AKI. - The integrated clinical-immune XGBoost model achieved superior discrimination (AUC 0.914) versus a clinical-only model (AUC 0.788). - Decision curve analysis indicated greater net clinical benefit for the integrated model compared with the clinical model. - The authors conclude that **immune dysregulation** contributes significantly to SA-AKI pathogenesis and that immune biomarkers can improve early identification of patients at high risk for SA-AKI. - Keywords reported include Immune phenotype, Machine learning, Sepsis-associated acute kidney injury, T-lymphocyte subsets, and **XGBoost**.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Unveiling the immuno-inflammatory endotype of sepsis-associated acute kidney injury: a machine learning-based approach [Jin Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+J&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#full-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Pengchao Tian](https://pubmed.ncbi.nlm.nih.gov/?term=Tian+P&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#full-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Jingyi Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+J&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#full-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Wenxiong Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+W&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#full-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Na Cui](https://pubmed.ncbi.nlm.nih.gov/?term=Cui+N&cauthor_id=42661565)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#full-view-affiliation-2 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. cyyy_cn@163.com.") Affiliations Expand ### Affiliations * 1 Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. * 2 Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. cyyy_cn@163.com. * PMID: **42661565** * DOI: [ 10.23876/j.krcp.26.112 ](https://doi.org/10.23876/j.krcp.26.112) Item in Clipboard # Unveiling the immuno-inflammatory endotype of sepsis-associated acute kidney injury: a machine learning-based approach Jin Zhang et al. Kidney Res Clin Pract. 2026. Show details Display options Display options Format Abstract PubMed PMID Kidney Res Clin Pract Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Kidney+Res+Clin+Pract%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Kidney+Res+Clin+Pract%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42661565/) . 2026 Aug 28. doi: 10.23876/j.krcp.26.112. Online ahead of print. ### Authors [Jin Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+J&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#short-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Pengchao Tian](https://pubmed.ncbi.nlm.nih.gov/?term=Tian+P&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#short-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Jingyi Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+J&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#short-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Wenxiong Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+W&cauthor_id=42661565)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#short-view-affiliation-1 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China."), [Na Cui](https://pubmed.ncbi.nlm.nih.gov/?term=Cui+N&cauthor_id=42661565)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42661565/#short-view-affiliation-2 "Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. cyyy_cn@163.com.") ### Affiliations * 1 Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. * 2 Department of Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. cyyy_cn@163.com. * PMID: **42661565** * DOI: [ 10.23876/j.krcp.26.112 ](https://doi.org/10.23876/j.krcp.26.112) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Background:** Sepsis-associated acute kidney injury (SA-AKI) is a frequent and high-mortality complication in critically ill patients. Current diagnostic criteria rely on functional markers that often lag behind the onset of renal injury. While immune dysregulation is central to SA-AKI pathogenesis, few prediction models systematically integrate multidimensional immune phenotypes. **Methods:** The present analysis was based on a multicenter prospective cohort study involving 1,551 septic patients. The study was carried out across five tertiary hospitals in Beijing. Clinical data and immuno-inflammatory biomarkers (including humoral, complement, and T lymphocyte subsets) were collected within 24 hours of sepsis diagnosis. An Extreme Gradient Boosting (XGBoost) model was then developed to predict SA-AKI. **Results:** New-onset SA-AKI occurred in 44.8% of the cohort and was associated with increased mortality. We identified a distinct high-risk immuno-inflammatory endotype characterized by elevated immunoglobulin A and procalcitonin (PCT), consumptive complement component 3 depletion, and a "complex and divergent cellular immune pattern"-manifested as depleted naïve CD4+ T cells coexisting with expanded CD28+CD4+ T cells. Integrating these immune features with routine clinical variables (Acute Physiology and Chronic Health Evaluation II (APACHE II), the Sequential Organ Failure Assessment [SOFA], PCT, prothrombin time [PT], sex) yielded an XGBoost-based clinical-immune model with superior discrimination (area under the curve [AUC], 0.914), significantly outperforming the clinical model (AUC, 0.788). Decision curve analysis further confirmed greater net clinical benefit for the integrated model. **Conclusion:** Immune dysregulation plays an important role in developing AKI in sepsis patients. Immune biomarkers could significantly improve early identification of high SA-AKI risk patients. **Keywords:** Immune phenotype; Machine learning; Sepsis-associated acute kidney injury; T-lymphocyte subsets; XGBoost. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Similar articles * [ A Single-Center Study: XGBoost-Based Multi-Omics Prediction Model for Sepsis-Associated Acute Kidney Injury (SA-AKI) and Personalized Immune Therapy Efficacy in Pancreatic Cancer ICU Patients. ](https://pubmed.ncbi.nlm.nih.gov/42008769/) Long Z, Fu Y, Wang S, Bao Y, Li Y.Long Z, et al.Shock. 2026 Apr 7. doi: 10.1097/SHK.0000000000002859. Online ahead of print.Shock. 2026.PMID: 42008769 * [ Establishment and validation of the prediction model based on lymphocyte subsets for acute kidney injury in sepsis patients. ](https://pubmed.ncbi.nlm.nih.gov/41080572/) Wang L, Liu Q, Wu C, Hou M, Li Z.Wang L, et al.Front Immunol. 2025 Sep 25;16:1674673. doi: 10.3389/fimmu.2025.1674673. eCollection 2025.Front Immunol. 2025.PMID: 41080572Free PMC article. * [ An interpretable machine-learning model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury. ](https://pubmed.ncbi.nlm.nih.gov/41767533/) Wang JZ, Zhang N, Ma RR, Yang M, Chen YG, Zhou WJ.Wang JZ, et al.Front Med (Lausanne). 2026 Feb 13;13:1756831. doi: 10.3389/fmed.2026.1756831. eCollection 2026.Front Med (Lausanne). 2026.PMID: 41767533Free PMC article. * [ Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review. ](https://pubmed.ncbi.nlm.nih.gov/41133165/) Li X, Hu X, Xu H, Yu P, Ju H.Li X, et al.Front Med (Lausanne). 2025 Oct 8;12:1680180. doi: 10.3389/fmed.2025.1680180. eCollection 2025.Front Med (Lausanne). 2025.PMID: 41133165Free PMC article. * [ Prognostic Value of Lactate/Albumin Ratio and NEWS-Lactate in Predicting Sepsis-Associated Acute Kidney Injury: A Retrospective Analysis. ](https://pubmed.ncbi.nlm.nih.gov/40801064/) Le Xuan D, Nguyen Hai G, Vu Anh D, Do Thanh H.Le Xuan D, et al.Arch Acad Emerg Med. 2025 Jul 1;13(1):e61. doi: 10.22037/aaemj.v13i1.2723. eCollection 2025.Arch Acad Emerg Med. 2025.PMID: 40801064Free PMC article.Review. 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