Yang Chen, Ying Gue, Gregory Y H Lip, David S Gardner, Mark A J Devonald
{"title":"尿微量元素对ICU入院及心脏手术后中重度急性肾损伤的机器学习预测。","authors":"Yang Chen, Ying Gue, Gregory Y H Lip, David S Gardner, Mark A J Devonald","doi":"10.1111/eci.70131","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute kidney injury (AKI) is common and linked to poor outcomes, but early detection remains challenging. Previous research identified urinary trace elements (TE) as early AKI biomarkers in intensive care unit (ICU) or cardiac surgery patients. We aimed to explore whether urinary TE enhance machine learning (ML) models for AKI prediction.</p><p><strong>Methods: </strong>We constructed ML models using the ICU cohort. We filtered the variables and optimized hyperparameters before predicting Kidney Disease: Improving Global Outcomes stage 2-3 AKI using eight ML classifiers: light gradient boosting machine (LightGBM), random forest (RF), ML logistic regression, support vector machine, multilayer perceptron, eXtreme gradient boosting (XGBoost), Gaussian Naive Bayes and k-nearest neighbors. External validation was performed in the cardiac surgery cohort.</p><p><strong>Results: </strong>Among 149 ICU patients (median age 56.0 [interquartile range (IQR): 43.5-67.0], 63.1% male), 25 developed stage 2-3 AKI; among 144 cardiac surgery patients (median age 70.0 [IQR: 62.0-76.0], 72.9% male), 12 developed stage 2-3 AKI. Each ML in the internal validation had area under the curve (AUC) above .7, with XGBoost having the highest (.813); LightGBM had the second highest AUC (.799), highest G-mean (.567) and F1-score (.545). In external validation, RF had the highest AUC (.740), XGBoost had the highest G-mean (.289) and F1-score (.286). Age, strontium and boron were consistently ranked among the top five most important features in LightGBM, RF and XGBoost.</p><p><strong>Conclusion: </strong>ML models primarily based on urinary TE can identify AKI risk in both clinical groups (ICU and cardiac surgery), with LightGBM, RF and XGBoost serving as high-performance models for early prediction of stage 2-3 AKI.</p>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":" ","pages":"e70131"},"PeriodicalIF":3.6000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.\",\"authors\":\"Yang Chen, Ying Gue, Gregory Y H Lip, David S Gardner, Mark A J Devonald\",\"doi\":\"10.1111/eci.70131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Acute kidney injury (AKI) is common and linked to poor outcomes, but early detection remains challenging. Previous research identified urinary trace elements (TE) as early AKI biomarkers in intensive care unit (ICU) or cardiac surgery patients. We aimed to explore whether urinary TE enhance machine learning (ML) models for AKI prediction.</p><p><strong>Methods: </strong>We constructed ML models using the ICU cohort. We filtered the variables and optimized hyperparameters before predicting Kidney Disease: Improving Global Outcomes stage 2-3 AKI using eight ML classifiers: light gradient boosting machine (LightGBM), random forest (RF), ML logistic regression, support vector machine, multilayer perceptron, eXtreme gradient boosting (XGBoost), Gaussian Naive Bayes and k-nearest neighbors. External validation was performed in the cardiac surgery cohort.</p><p><strong>Results: </strong>Among 149 ICU patients (median age 56.0 [interquartile range (IQR): 43.5-67.0], 63.1% male), 25 developed stage 2-3 AKI; among 144 cardiac surgery patients (median age 70.0 [IQR: 62.0-76.0], 72.9% male), 12 developed stage 2-3 AKI. Each ML in the internal validation had area under the curve (AUC) above .7, with XGBoost having the highest (.813); LightGBM had the second highest AUC (.799), highest G-mean (.567) and F1-score (.545). In external validation, RF had the highest AUC (.740), XGBoost had the highest G-mean (.289) and F1-score (.286). Age, strontium and boron were consistently ranked among the top five most important features in LightGBM, RF and XGBoost.</p><p><strong>Conclusion: </strong>ML models primarily based on urinary TE can identify AKI risk in both clinical groups (ICU and cardiac surgery), with LightGBM, RF and XGBoost serving as high-performance models for early prediction of stage 2-3 AKI.</p>\",\"PeriodicalId\":12013,\"journal\":{\"name\":\"European Journal of Clinical Investigation\",\"volume\":\" \",\"pages\":\"e70131\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Clinical Investigation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/eci.70131\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Clinical Investigation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/eci.70131","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.
Background: Acute kidney injury (AKI) is common and linked to poor outcomes, but early detection remains challenging. Previous research identified urinary trace elements (TE) as early AKI biomarkers in intensive care unit (ICU) or cardiac surgery patients. We aimed to explore whether urinary TE enhance machine learning (ML) models for AKI prediction.
Methods: We constructed ML models using the ICU cohort. We filtered the variables and optimized hyperparameters before predicting Kidney Disease: Improving Global Outcomes stage 2-3 AKI using eight ML classifiers: light gradient boosting machine (LightGBM), random forest (RF), ML logistic regression, support vector machine, multilayer perceptron, eXtreme gradient boosting (XGBoost), Gaussian Naive Bayes and k-nearest neighbors. External validation was performed in the cardiac surgery cohort.
Results: Among 149 ICU patients (median age 56.0 [interquartile range (IQR): 43.5-67.0], 63.1% male), 25 developed stage 2-3 AKI; among 144 cardiac surgery patients (median age 70.0 [IQR: 62.0-76.0], 72.9% male), 12 developed stage 2-3 AKI. Each ML in the internal validation had area under the curve (AUC) above .7, with XGBoost having the highest (.813); LightGBM had the second highest AUC (.799), highest G-mean (.567) and F1-score (.545). In external validation, RF had the highest AUC (.740), XGBoost had the highest G-mean (.289) and F1-score (.286). Age, strontium and boron were consistently ranked among the top five most important features in LightGBM, RF and XGBoost.
Conclusion: ML models primarily based on urinary TE can identify AKI risk in both clinical groups (ICU and cardiac surgery), with LightGBM, RF and XGBoost serving as high-performance models for early prediction of stage 2-3 AKI.
期刊介绍:
EJCI considers any original contribution from the most sophisticated basic molecular sciences to applied clinical and translational research and evidence-based medicine across a broad range of subspecialties. The EJCI publishes reports of high-quality research that pertain to the genetic, molecular, cellular, or physiological basis of human biology and disease, as well as research that addresses prevalence, diagnosis, course, treatment, and prevention of disease. We are primarily interested in studies directly pertinent to humans, but submission of robust in vitro and animal work is also encouraged. Interdisciplinary work and research using innovative methods and combinations of laboratory, clinical, and epidemiological methodologies and techniques is of great interest to the journal. Several categories of manuscripts (for detailed description see below) are considered: editorials, original articles (also including randomized clinical trials, systematic reviews and meta-analyses), reviews (narrative reviews), opinion articles (including debates, perspectives and commentaries); and letters to the Editor.