Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach
Background Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2438858 |
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| author | Lingyu Xu Siqi Jiang Chenyu Li Xue Gao Chen Guan Tianyang Li Ningxin Zhang Shuang Gao Xinyuan Wang Yanfei Wang Lin Che Yan Xu |
| author_facet | Lingyu Xu Siqi Jiang Chenyu Li Xue Gao Chen Guan Tianyang Li Ningxin Zhang Shuang Gao Xinyuan Wang Yanfei Wang Lin Che Yan Xu |
| author_sort | Lingyu Xu |
| collection | DOAJ |
| description | Background Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.Methods Data from 2,346 hospitalized pediatric patients, collected between January 2020 and January 2023, were divided into an 85% training set and a 15% test set. Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. SHAP was used to interpret the model, and an online prediction tool was developed with Streamlit to predict AKI and AKD.Results The incidence of AKI and AKD were 14.90% and 16.26%, respectively. Patients with AKD combined with AKI had the highest mortality rate, at 6.94%, when analyzed by renal function trajectories. The LightGBM algorithm showed superior predictive performance for both AKI and AKD (AUROC: 0.813, 0.744). SHAP identified top predictors for AKI as serum creatinine, white blood cell count, neutrophil count, and lactate dehydrogenase, while key predictors for AKD included proton pump inhibitor, blood glucose, hemoglobin, and AKI grade.Conclusion The high incidence of AKI and AKD among hospitalized children warrants attention. Renal function trajectories are strongly associated with prognosis. Supported by a web-based tool, machine learning models can effectively predict AKI and AKD, facilitating early identification of high-risk pediatric patients and potentially improving outcomes. |
| format | Article |
| id | doaj-art-1bbff88910f14c8d9db6e68b58e79eda |
| institution | OA Journals |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-1bbff88910f14c8d9db6e68b58e79eda2025-08-20T02:29:58ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146210.1080/0886022X.2024.2438858Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approachLingyu Xu0Siqi Jiang1Chenyu Li2Xue Gao3Chen Guan4Tianyang Li5Ningxin Zhang6Shuang Gao7Xinyuan Wang8Yanfei Wang9Lin Che10Yan Xu11Department of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaOcean University of China, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Nephrology, the Affiliated Hospital of Qingdao University, Qingdao, ChinaBackground Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.Methods Data from 2,346 hospitalized pediatric patients, collected between January 2020 and January 2023, were divided into an 85% training set and a 15% test set. Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. SHAP was used to interpret the model, and an online prediction tool was developed with Streamlit to predict AKI and AKD.Results The incidence of AKI and AKD were 14.90% and 16.26%, respectively. Patients with AKD combined with AKI had the highest mortality rate, at 6.94%, when analyzed by renal function trajectories. The LightGBM algorithm showed superior predictive performance for both AKI and AKD (AUROC: 0.813, 0.744). SHAP identified top predictors for AKI as serum creatinine, white blood cell count, neutrophil count, and lactate dehydrogenase, while key predictors for AKD included proton pump inhibitor, blood glucose, hemoglobin, and AKI grade.Conclusion The high incidence of AKI and AKD among hospitalized children warrants attention. Renal function trajectories are strongly associated with prognosis. Supported by a web-based tool, machine learning models can effectively predict AKI and AKD, facilitating early identification of high-risk pediatric patients and potentially improving outcomes.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2438858Pediatricacute kidney injuryacute kidney diseasemachine learningprediction modelrenal function trajectory |
| spellingShingle | Lingyu Xu Siqi Jiang Chenyu Li Xue Gao Chen Guan Tianyang Li Ningxin Zhang Shuang Gao Xinyuan Wang Yanfei Wang Lin Che Yan Xu Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach Renal Failure Pediatric acute kidney injury acute kidney disease machine learning prediction model renal function trajectory |
| title | Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach |
| title_full | Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach |
| title_fullStr | Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach |
| title_full_unstemmed | Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach |
| title_short | Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach |
| title_sort | acute kidney disease in hospitalized pediatric patients risk prediction based on an artificial intelligence approach |
| topic | Pediatric acute kidney injury acute kidney disease machine learning prediction model renal function trajectory |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2438858 |
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