Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection
Background: Extended aortic arch repair (EAR) is increasingly adopted for treating acute type A aortic dissection (ATAAD). However, existing prediction models may not be suitable for assessing the in-hospital death risk in ATAAD patients undergoing EAR. This study aims to develop...
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| Format: | Article |
| Language: | English |
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IMR Press
2025-04-01
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| Series: | Reviews in Cardiovascular Medicine |
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| Online Access: | https://www.imrpress.com/journal/RCM/26/4/10.31083/RCM26943 |
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| author | Qiyi Chen Yulin Wang Yixiao Zhang Fangyu Liu Kejie Shao Hao Lai Chunsheng Wang Qiang Ji |
| author_facet | Qiyi Chen Yulin Wang Yixiao Zhang Fangyu Liu Kejie Shao Hao Lai Chunsheng Wang Qiang Ji |
| author_sort | Qiyi Chen |
| collection | DOAJ |
| description | Background: Extended aortic arch repair (EAR) is increasingly adopted for treating acute type A aortic dissection (ATAAD). However, existing prediction models may not be suitable for assessing the in-hospital death risk in ATAAD patients undergoing EAR. This study aims to develop a comprehensive risk prediction model for in-hospital death following EAR based on patient’s preoperative status and surgical data, which may contribute to identification of high-risk individuals and improve outcomes following EAR. Methods: We reviewed clinical records of consecutive adult ATAAD patients undergoing EAR at our institute between January 2015 and December 2022. Utilizing data from 925 ATAAD patients undergoing EAR, we employed multivariable logistic regression and machine learning techniques, respectively, to develop nomograms for in-hospital mortality. Employed machine learning techniques included simple decision tree, random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM). Results: The nomogram based on SVM outperformed others, achieving a mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.842 on training dataset and a mean AUC of 0.782 on testing dataset, accompanied by a Brier score of 0.058. Key risk factors included cerebral malperfusion, mesenteric malperfusion, preoperative critical station, Marfan syndrome, platelet count, D-dimer, coronary artery bypass grafting, and cardiopulmonary bypass time. A web-based application was developed for clinical use. Conclusions: We develop a novel nomogram risk prediction model based on SVM algorithm for in-hospital death following extended aortic arch repair for ATAAD with good discrimination and accuracy. Clinical Trial Registration: Registration number ChiCTR2200066414, https://www.chictr.org.cn/showproj.html?proj=187074. |
| format | Article |
| id | doaj-art-66a2fcfc7abb4601be862ac30bf09aa6 |
| institution | OA Journals |
| issn | 1530-6550 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj-art-66a2fcfc7abb4601be862ac30bf09aa62025-08-20T02:28:55ZengIMR PressReviews in Cardiovascular Medicine1530-65502025-04-012642694310.31083/RCM26943S1530-6550(24)01733-2Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic DissectionQiyi Chen0Yulin Wang1Yixiao Zhang2Fangyu Liu3Kejie Shao4Hao Lai5Chunsheng Wang6Qiang Ji7Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaDepartment of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaDepartment of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaDepartment of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaDepartment of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaDepartment of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaShanghai Municipal Institute for Cardiovascular Diseases, 200032 Shanghai, ChinaDepartment of Cardiovascular Surgery, Zhongshan Hospital Fudan University, 200032 Shanghai, ChinaBackground: Extended aortic arch repair (EAR) is increasingly adopted for treating acute type A aortic dissection (ATAAD). However, existing prediction models may not be suitable for assessing the in-hospital death risk in ATAAD patients undergoing EAR. This study aims to develop a comprehensive risk prediction model for in-hospital death following EAR based on patient’s preoperative status and surgical data, which may contribute to identification of high-risk individuals and improve outcomes following EAR. Methods: We reviewed clinical records of consecutive adult ATAAD patients undergoing EAR at our institute between January 2015 and December 2022. Utilizing data from 925 ATAAD patients undergoing EAR, we employed multivariable logistic regression and machine learning techniques, respectively, to develop nomograms for in-hospital mortality. Employed machine learning techniques included simple decision tree, random forest (RF), eXtreme Gradient Boosting (XGBoost), and support vector machine (SVM). Results: The nomogram based on SVM outperformed others, achieving a mean area under the receiver operating characteristic (ROC) curve (AUC) of 0.842 on training dataset and a mean AUC of 0.782 on testing dataset, accompanied by a Brier score of 0.058. Key risk factors included cerebral malperfusion, mesenteric malperfusion, preoperative critical station, Marfan syndrome, platelet count, D-dimer, coronary artery bypass grafting, and cardiopulmonary bypass time. A web-based application was developed for clinical use. Conclusions: We develop a novel nomogram risk prediction model based on SVM algorithm for in-hospital death following extended aortic arch repair for ATAAD with good discrimination and accuracy. Clinical Trial Registration: Registration number ChiCTR2200066414, https://www.chictr.org.cn/showproj.html?proj=187074.https://www.imrpress.com/journal/RCM/26/4/10.31083/RCM26943acute type a aortic dissectionextended aortic arch repairprediction modelmachine learningnomogram |
| spellingShingle | Qiyi Chen Yulin Wang Yixiao Zhang Fangyu Liu Kejie Shao Hao Lai Chunsheng Wang Qiang Ji Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection Reviews in Cardiovascular Medicine acute type a aortic dissection extended aortic arch repair prediction model machine learning nomogram |
| title | Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection |
| title_full | Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection |
| title_fullStr | Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection |
| title_full_unstemmed | Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection |
| title_short | Development and Validation of a Novel Nomogram Risk Prediction Model for In-Hospital Death Following Extended Aortic Arch Repair for Acute Type A Aortic Dissection |
| title_sort | development and validation of a novel nomogram risk prediction model for in hospital death following extended aortic arch repair for acute type a aortic dissection |
| topic | acute type a aortic dissection extended aortic arch repair prediction model machine learning nomogram |
| url | https://www.imrpress.com/journal/RCM/26/4/10.31083/RCM26943 |
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