Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery
BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient...
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2021.728521/full |
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| author | Jinzhang Li Jinzhang Li Jinzhang Li Ming Gong Ming Gong Yashutosh Joshi Lizhong Sun Lianjun Huang Ruixin Fan Tianxiang Gu Zonggang Zhang Chengwei Zou Guowei Zhang Ximing Qian Chenhui Qiao Yu Chen Wenjian Jiang Wenjian Jiang Wenjian Jiang Hongjia Zhang Hongjia Zhang Hongjia Zhang |
| author_facet | Jinzhang Li Jinzhang Li Jinzhang Li Ming Gong Ming Gong Yashutosh Joshi Lizhong Sun Lianjun Huang Ruixin Fan Tianxiang Gu Zonggang Zhang Chengwei Zou Guowei Zhang Ximing Qian Chenhui Qiao Yu Chen Wenjian Jiang Wenjian Jiang Wenjian Jiang Hongjia Zhang Hongjia Zhang Hongjia Zhang |
| author_sort | Jinzhang Li |
| collection | DOAJ |
| description | BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p < 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures. |
| first_indexed | 2024-12-24T01:52:51Z |
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| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-12-24T01:52:51Z |
| publishDate | 2022-01-01 |
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| series | Frontiers in Medicine |
| spelling | doaj.art-6f70a4eef0c74f62a05d3cfc5fcc30402022-12-21T17:21:41ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-01-01810.3389/fmed.2021.728521728521Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome SurgeryJinzhang Li0Jinzhang Li1Jinzhang Li2Ming Gong3Ming Gong4Yashutosh Joshi5Lizhong Sun6Lianjun Huang7Ruixin Fan8Tianxiang Gu9Zonggang Zhang10Chengwei Zou11Guowei Zhang12Ximing Qian13Chenhui Qiao14Yu Chen15Wenjian Jiang16Wenjian Jiang17Wenjian Jiang18Hongjia Zhang19Hongjia Zhang20Hongjia Zhang21Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, ChinaDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, ChinaBeijing Lab for Cardiovascular Precision Medicine, Beijing, ChinaDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, ChinaBeijing Lab for Cardiovascular Precision Medicine, Beijing, ChinaDepartment of Cardiothoracic Surgery, St Vincent's Hospital, Sydney, NSW, AustraliaDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, ChinaDepartment of Interference Diagnosis and Treatment, Beijing Anzhen Hospital, Capital Medical University, Beijing, ChinaDepartment of Cardiovascular Surgery, Guangdong Provincial People's Hospital, Guangzhou, ChinaDepartment of Cardiac Surgery, First Affiliated Hospital, China Medical University, Shenyang, ChinaDepartment of Cardiac Surgery, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, ChinaDepartment of Cardiovascular Surgery, Shandong Provincial Hospital Affiliated With Shandong First Medical University, Jinan, China0Department of Cardiovascular Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, China1Department of Cardiac Surgery, School of Medicine, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, China2Department of Cardiovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China3Department of Cardiac Surgery, Peking University People's Hospital, Beijing, ChinaDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, ChinaBeijing Lab for Cardiovascular Precision Medicine, Beijing, ChinaDepartment of Cardiothoracic Surgery, St Vincent's Hospital, Sydney, NSW, AustraliaDepartment of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, ChinaDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, ChinaBeijing Lab for Cardiovascular Precision Medicine, Beijing, ChinaBackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p < 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.https://www.frontiersin.org/articles/10.3389/fmed.2021.728521/fullmachine learningacute renal failureacute aortic syndromeprediction modeleXtreme Gradient Boosting |
| spellingShingle | Jinzhang Li Jinzhang Li Jinzhang Li Ming Gong Ming Gong Yashutosh Joshi Lizhong Sun Lianjun Huang Ruixin Fan Tianxiang Gu Zonggang Zhang Chengwei Zou Guowei Zhang Ximing Qian Chenhui Qiao Yu Chen Wenjian Jiang Wenjian Jiang Wenjian Jiang Hongjia Zhang Hongjia Zhang Hongjia Zhang Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery Frontiers in Medicine machine learning acute renal failure acute aortic syndrome prediction model eXtreme Gradient Boosting |
| title | Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery |
| title_full | Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery |
| title_fullStr | Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery |
| title_full_unstemmed | Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery |
| title_short | Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery |
| title_sort | machine learning prediction model for acute renal failure after acute aortic syndrome surgery |
| topic | machine learning acute renal failure acute aortic syndrome prediction model eXtreme Gradient Boosting |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2021.728521/full |
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