Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care
AbstractMajor adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Renal Failure |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2215329 |
| _version_ | 1797658432255295488 |
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| author | Lin Wang Shao-Bin Duan Ping Yan Xiao-Qin Luo Ning-Ya Zhang |
| author_facet | Lin Wang Shao-Bin Duan Ping Yan Xiao-Qin Luo Ning-Ya Zhang |
| author_sort | Lin Wang |
| collection | DOAJ |
| description | AbstractMajor adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted to the Second Xiangya Hospital of Central South University between January 2020 and December 2021. Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. Out of the 2366 patients, 1656 were used for model derivation and 710 for testing. The incidence of MAKE30 was 13.8% in the derivation cohort and 13.2% in the test cohort. The average area under the receiver operating characteristic curve of the XGBoost model was 0.930 (95% CI: 0.912–0.946) in the training set and 0.851 (95% CI: 0.810–0.890) in the test set. The top 8 predictors of MAKE30 tentatively identified by the Shapley additive explanations method were Acute Physiology and Chronic Health Evaluation II score, serum creatinine, blood urea nitrogen, Simplified Acute Physiology Score II score, Sequential Organ Failure Assessment score, aspartate aminotransferase, arterial blood bicarbonate, and albumin. The XGBoost model accurately predicted the occurrence of MAKE30 in elderly ICU patients, and the findings of this study provide valuable information to clinicians for making informed clinical decisions. |
| first_indexed | 2024-03-11T17:59:59Z |
| format | Article |
| id | doaj.art-64e0dd675d394939b41288a1f289b803 |
| institution | Directory Open Access Journal |
| issn | 0886-022X 1525-6049 |
| language | English |
| last_indexed | 2024-03-11T17:59:59Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj.art-64e0dd675d394939b41288a1f289b8032023-10-17T09:23:24ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492023-12-0145110.1080/0886022X.2023.2215329Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical careLin Wang0Shao-Bin Duan1Ping Yan2Xiao-Qin Luo3Ning-Ya Zhang4Department of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, ChinaDepartment of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, ChinaDepartment of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, ChinaDepartment of Nephrology, Hunan Key Laboratory of Kidney Disease and Blood Purification, The Second Xiangya Hospital of Central South University, Changsha, Hunan, ChinaInformation Center, The Second Xiangya Hospital of Central South University, Changsha, Hunan, ChinaAbstractMajor adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted to the Second Xiangya Hospital of Central South University between January 2020 and December 2021. Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. Out of the 2366 patients, 1656 were used for model derivation and 710 for testing. The incidence of MAKE30 was 13.8% in the derivation cohort and 13.2% in the test cohort. The average area under the receiver operating characteristic curve of the XGBoost model was 0.930 (95% CI: 0.912–0.946) in the training set and 0.851 (95% CI: 0.810–0.890) in the test set. The top 8 predictors of MAKE30 tentatively identified by the Shapley additive explanations method were Acute Physiology and Chronic Health Evaluation II score, serum creatinine, blood urea nitrogen, Simplified Acute Physiology Score II score, Sequential Organ Failure Assessment score, aspartate aminotransferase, arterial blood bicarbonate, and albumin. The XGBoost model accurately predicted the occurrence of MAKE30 in elderly ICU patients, and the findings of this study provide valuable information to clinicians for making informed clinical decisions.https://www.tandfonline.com/doi/10.1080/0886022X.2023.2215329Critical illnessmajor adverse kidney eventsthe elderlyXGBoostmachine learning |
| spellingShingle | Lin Wang Shao-Bin Duan Ping Yan Xiao-Qin Luo Ning-Ya Zhang Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care Renal Failure Critical illness major adverse kidney events the elderly XGBoost machine learning |
| title | Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care |
| title_full | Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care |
| title_fullStr | Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care |
| title_full_unstemmed | Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care |
| title_short | Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care |
| title_sort | utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care |
| topic | Critical illness major adverse kidney events the elderly XGBoost machine learning |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2023.2215329 |
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