---
title: "CatBoost Model Predicts In-Hospital Mortality in Diabetic Patients with Sepsis-Associated AKI"
id: "pubmed-42658480"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42658480"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42658480/"
doi: "10.1177/08850666261479295"
published_at: "2026-08-27T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CatBoost Model Predicts In-Hospital Mortality in Diabetic Patients with Sepsis-Associated AKI
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42658480
- **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/42658480/)
- **DOI:** [10.1177/08850666261479295](https://doi.org/10.1177%2F08850666261479295)
- **Published At:** 2026-08-27T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- This study developed and externally validated an interpretable machine learning model to predict early in-hospital mortality in patients with **diabetes** complicated by **sepsis-associated acute kidney injury (SA-AKI)**. - Researchers extracted 94 variables from the first 24 hours after ICU admission for 6,929 patients identified in the MIMIC‑IV database and split the cohort into training and validation sets (70:30). - Twelve machine learning algorithms were trained and compared; the categorical boosting algorithm (**CatBoost**) achieved superior performance. - Recursive feature elimination reduced predictors to 32 clinically accessible variables, including urine output rate, platelet count, lactate, weight, blood glucose, **SOFA score**, pH, blood urea nitrogen, vital signs, coagulation indices, and vasopressor use. - Model interpretability was addressed using SHapley Additive exPlanations (SHAP) to explain predictor contributions. - Performance metrics for the final CatBoost model in internal validation: AUC 0.828, accuracy 70.9%, sensitivity 78.7%, specificity 69.0%, F1 score 0.509, PPV 33.7%, NPV 93.1%. - External testing using the eICU Collaborative Research Database produced an AUC of 0.793, indicating preserved discrimination across datasets. - The model was deployed as a web-based tool to support early risk stratification and clinical decision-making for diabetic patients with SA-AKI. - The study highlights use of routinely collected ICU variables and interpretable ML techniques to predict mortality, but full methodological and implementation details are in the source and not expanded here.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury [Yanni Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+Y&cauthor_id=42658480)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-1 "The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Hongjie Shen](https://pubmed.ncbi.nlm.nih.gov/?term=Shen+H&cauthor_id=42658480)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Shengze Wu](https://pubmed.ncbi.nlm.nih.gov/?term=Wu+S&cauthor_id=42658480)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Suibi Yang](https://pubmed.ncbi.nlm.nih.gov/?term=Yang+S&cauthor_id=42658480)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Feng Guo](https://pubmed.ncbi.nlm.nih.gov/?term=Guo+F&cauthor_id=42658480)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-3 "Intensive Care Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-4 "Zhejiang Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, People's Republic of China."), [Min Yang](https://pubmed.ncbi.nlm.nih.gov/?term=Yang+M&cauthor_id=42658480)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-1 "The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.")[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-5 "Laboratory of Cardiopulmonary Resuscitation and Critical Illness, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China."), [Zhongheng Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+Z&cauthor_id=42658480)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-1 "The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.")[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-6 "School of Medicine, Shaoxing University, Shaoxing, Zhejiang, P.R. China.")[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#full-view-affiliation-7 "Provincial Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.") Affiliations Expand ### Affiliations * 1 The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China. * 2 Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. * 3 Intensive Care Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China. * 4 Zhejiang Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, People's Republic of China. * 5 Laboratory of Cardiopulmonary Resuscitation and Critical Illness, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China. * 6 School of Medicine, Shaoxing University, Shaoxing, Zhejiang, P.R. China. * 7 Provincial Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. * PMID: **42658480** * DOI: [ 10.1177/08850666261479295 ](https://doi.org/10.1177/08850666261479295) Item in Clipboard # Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury Yanni Wang et al. J Intensive Care Med. 2026. Show details Display options Display options Format Abstract PubMed PMID J Intensive Care Med Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22J+Intensive+Care+Med%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22J+Intensive+Care+Med%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42658480/) . 2026 Aug 27:8850666261479295. doi: 10.1177/08850666261479295. Online ahead of print. ### Authors [Yanni Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+Y&cauthor_id=42658480)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-1 "The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Hongjie Shen](https://pubmed.ncbi.nlm.nih.gov/?term=Shen+H&cauthor_id=42658480)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Shengze Wu](https://pubmed.ncbi.nlm.nih.gov/?term=Wu+S&cauthor_id=42658480)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Suibi Yang](https://pubmed.ncbi.nlm.nih.gov/?term=Yang+S&cauthor_id=42658480)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China."), [Feng Guo](https://pubmed.ncbi.nlm.nih.gov/?term=Guo+F&cauthor_id=42658480)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-3 "Intensive Care Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-4 "Zhejiang Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, People's Republic of China."), [Min Yang](https://pubmed.ncbi.nlm.nih.gov/?term=Yang+M&cauthor_id=42658480)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-1 "The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.")[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-5 "Laboratory of Cardiopulmonary Resuscitation and Critical Illness, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China."), [Zhongheng Zhang](https://pubmed.ncbi.nlm.nih.gov/?term=Zhang+Z&cauthor_id=42658480)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-1 "The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-2 "Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.")[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-6 "School of Medicine, Shaoxing University, Shaoxing, Zhejiang, P.R. China.")[ 7 ](https://pubmed.ncbi.nlm.nih.gov/42658480/#short-view-affiliation-7 "Provincial Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.") ### Affiliations * 1 The Second Department of Critical Care Medicine, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China. * 2 Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. * 3 Intensive Care Unit, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China. * 4 Zhejiang Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, People's Republic of China. * 5 Laboratory of Cardiopulmonary Resuscitation and Critical Illness, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China. * 6 School of Medicine, Shaoxing University, Shaoxing, Zhejiang, P.R. China. * 7 Provincial Key Laboratory of Precise Diagnosis and Treatment of Abdominal Infection, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China. * PMID: **42658480** * DOI: [ 10.1177/08850666261479295 ](https://doi.org/10.1177/08850666261479295) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract BackgroundSepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes may further increase adverse outcomes through infection susceptibility, immune dysfunction, and renal vulnerability. However, mortality prediction models for patients with diabetes complicated by SA-AKI remain limited. This study aimed to develop and validate a machine learning-based model for early in-hospital mortality prediction in this population.MethodsA total of 6929 patients with SA-AKI and diabetes were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and randomly divided into training and validation sets at a ratio of 7:3. Ninety-four variables, including demographics, diagnoses, clinical parameters, and medication records within the first 24 h after ICU admission, were extracted. Twelve machine learning algorithms were developed and compared, and the optimal model was selected. Recursive feature elimination was used to identify key predictors, while SHapley Additive exPlanations were applied for model interpretation. The final model was deployed as a web-based tool and externally tested using the eICU Collaborative Research Database.ResultsThirty-two key predictors were ultimately selected, including urine output rate, platelet count, lactate, weight, blood glucose, SOFA score, pH, blood urea nitrogen, vital signs, coagulation indices, vasopressor use, and other clinically relevant variables. The categorical boosting algorithm model presented better predictive performance [receiver operating characteristic (AUC): 0.828] than other models [accuracy (ACC): 70.9%, sensitivity: 78.7%, specificity: 69%, F1 score: 0.509, positive predictive value (PPV): 33.7%, and negative predictive value (NPV): 93.1%]. External testing using data from the eICU database was also well validated (AUC: 0.793).ConclusionsA CatBoost-based machine learning model incorporating 32 clinically accessible variables showed good predictive performance for in-hospital mortality in patients with diabetes and SA-AKI, supporting early risk stratification and clinical decision-making. **Keywords:** acute kidney injury; diabetes; machine learning; mortality; predictive model; sepsis. 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