Xinyuan Wang, Chenyu Li, Lingyu Xu, Siqi Jiang, Chen Guan, Lin Che, Yanfei Wang, Xiaofei Man, Yan Xu
{"title":"急性肾脏疾病和营养不良风险患者死亡率预后模型的构建和验证：可解释的机器学习方法。","authors":"Xinyuan Wang, Chenyu Li, Lingyu Xu, Siqi Jiang, Chen Guan, Lin Che, Yanfei Wang, Xiaofei Man, Yan Xu","doi":"10.1093/ckj/sfaf080","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Acute kidney injury (AKI) is a prevalent complication in patients at risk of malnutrition, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. This study aimed to develop and validate machine learning (ML) models for predicting the occurrence of AKD, AKI and mortality in patients at risk of malnutrition.</p><p><strong>Methods: </strong>We retrospectively reviewed the medical records of patients at risk of malnutrition. Eight ML algorithms were employed to predict AKD, AKI and mortality. The performance of the best model was evaluated using various metrics and interpreted using the SHapley Additive exPlanation (SHAP) method. An artificial intelligence (AI)-driven web application was also created based on the best model.</p><p><strong>Results: </strong>A total of 13 395 patients were included in our study. Among them, 1751 (13.07%) developed subacute AKD, 1253 (9.35%) were transient AKI, and 1455 (10.86%) met both AKI and AKD criteria. The incidence rate of mortality was 6.74%. The light gradient boosting machine (LGBM) outperformed other models in predicting AKD, AKI and mortality, with area under curve values of 0.763, 0.801 and 0.881, respectively. The SHAP method revealed that AKI stage, lactate dehydrogenase, albumin, aspirin usage and serum creatinine were the top five predictors of AKD. An online prediction website for AKI, AKD and mortality was developed based on the final models.</p><p><strong>Conclusions: </strong>The LGBM models provide an effective method for predicting AKD, AKI and mortality at an early stage in patients at risk of malnutrition, enabling prompt interventions. Compared with the AKD model, the models for predicting AKI and mortality perform better. The AI-driven web application can significantly aid in creating personalized preventive measures. Future work will aim to expand the application to larger, more diverse populations, incorporate additional biomarkers and refine ML algorithms to improve predictive accuracy and clinical utility.</p>","PeriodicalId":10435,"journal":{"name":"Clinical Kidney Journal","volume":"18 4","pages":"sfaf080"},"PeriodicalIF":3.9000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11997651/pdf/","citationCount":"0","resultStr":"{\"title\":\"Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach.\",\"authors\":\"Xinyuan Wang, Chenyu Li, Lingyu Xu, Siqi Jiang, Chen Guan, Lin Che, Yanfei Wang, Xiaofei Man, Yan Xu\",\"doi\":\"10.1093/ckj/sfaf080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Acute kidney injury (AKI) is a prevalent complication in patients at risk of malnutrition, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. This study aimed to develop and validate machine learning (ML) models for predicting the occurrence of AKD, AKI and mortality in patients at risk of malnutrition.</p><p><strong>Methods: </strong>We retrospectively reviewed the medical records of patients at risk of malnutrition. Eight ML algorithms were employed to predict AKD, AKI and mortality. The performance of the best model was evaluated using various metrics and interpreted using the SHapley Additive exPlanation (SHAP) method. An artificial intelligence (AI)-driven web application was also created based on the best model.</p><p><strong>Results: </strong>A total of 13 395 patients were included in our study. Among them, 1751 (13.07%) developed subacute AKD, 1253 (9.35%) were transient AKI, and 1455 (10.86%) met both AKI and AKD criteria. The incidence rate of mortality was 6.74%. The light gradient boosting machine (LGBM) outperformed other models in predicting AKD, AKI and mortality, with area under curve values of 0.763, 0.801 and 0.881, respectively. The SHAP method revealed that AKI stage, lactate dehydrogenase, albumin, aspirin usage and serum creatinine were the top five predictors of AKD. An online prediction website for AKI, AKD and mortality was developed based on the final models.</p><p><strong>Conclusions: </strong>The LGBM models provide an effective method for predicting AKD, AKI and mortality at an early stage in patients at risk of malnutrition, enabling prompt interventions. Compared with the AKD model, the models for predicting AKI and mortality perform better. The AI-driven web application can significantly aid in creating personalized preventive measures. Future work will aim to expand the application to larger, more diverse populations, incorporate additional biomarkers and refine ML algorithms to improve predictive accuracy and clinical utility.</p>\",\"PeriodicalId\":10435,\"journal\":{\"name\":\"Clinical Kidney Journal\",\"volume\":\"18 4\",\"pages\":\"sfaf080\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11997651/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Kidney Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/ckj/sfaf080\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Kidney Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/ckj/sfaf080","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach.
Background: Acute kidney injury (AKI) is a prevalent complication in patients at risk of malnutrition, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. This study aimed to develop and validate machine learning (ML) models for predicting the occurrence of AKD, AKI and mortality in patients at risk of malnutrition.
Methods: We retrospectively reviewed the medical records of patients at risk of malnutrition. Eight ML algorithms were employed to predict AKD, AKI and mortality. The performance of the best model was evaluated using various metrics and interpreted using the SHapley Additive exPlanation (SHAP) method. An artificial intelligence (AI)-driven web application was also created based on the best model.
Results: A total of 13 395 patients were included in our study. Among them, 1751 (13.07%) developed subacute AKD, 1253 (9.35%) were transient AKI, and 1455 (10.86%) met both AKI and AKD criteria. The incidence rate of mortality was 6.74%. The light gradient boosting machine (LGBM) outperformed other models in predicting AKD, AKI and mortality, with area under curve values of 0.763, 0.801 and 0.881, respectively. The SHAP method revealed that AKI stage, lactate dehydrogenase, albumin, aspirin usage and serum creatinine were the top five predictors of AKD. An online prediction website for AKI, AKD and mortality was developed based on the final models.
Conclusions: The LGBM models provide an effective method for predicting AKD, AKI and mortality at an early stage in patients at risk of malnutrition, enabling prompt interventions. Compared with the AKD model, the models for predicting AKI and mortality perform better. The AI-driven web application can significantly aid in creating personalized preventive measures. Future work will aim to expand the application to larger, more diverse populations, incorporate additional biomarkers and refine ML algorithms to improve predictive accuracy and clinical utility.
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About the Journal
Clinical Kidney Journal: Clinical and Translational Nephrology (ckj), an official journal of the ERA-EDTA (European Renal Association-European Dialysis and Transplant Association), is a fully open access, online only journal publishing bimonthly. The journal is an essential educational and training resource integrating clinical, translational and educational research into clinical practice. ckj aims to contribute to a translational research culture among nephrologists and kidney pathologists that helps close the gap between basic researchers and practicing clinicians and promote sorely needed innovation in the Nephrology field. All research articles in this journal have undergone peer review.
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