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Wu Z, Li Y, Qiu P, Liu H, Liu K, Li W, Wang R, Chen T, Lu X. Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic Dissection After Admission. Front Cardiovasc Med 2022; 9:832770. [PMID: 35722130 PMCID: PMC9204146 DOI: 10.3389/fcvm.2022.832770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesHypertension is a predominant risk factor for aortic dissection (AD), and blood pressure (BP) control plays a vital role in the management of AD. However, the correlation between BP change and the prognosis for AD remains unclear. This study aims to demonstrate the impact of BP change patterns on AD prognosis.MethodsThis retrospective study included AD patients at two institutions (Shanghai Ninth People's Hospital Affiliated with Shanghai Jiao Tong University School of Medicine and the Vascular Department of the First Affiliated Hospital of Anhui Medical University) between 2004 and 2018. The systolic BP (SBP) change patterns of these patients were analyzed by functional data analysis (FDA). The relationship between BP change patterns and the risk of adverse events (AEs) was assessed using survival analysis.ResultsA total of 458 patients with AD were eligible for analysis. The logistic regression analysis indicated that compared with that in patients with low SBP variation (SBPV), the incidence of AEs in patients with high SBPV was significantly higher (35.84 vs. 20.35%, OR 2.19, P < 0.001). The patients were divided into four categories (accelerating rise, accelerating drop, decelerating rise, and decelerating drop) based on their SBP patterns after FDA fitting. The results of Kaplan–Meier analysis showed that at the 15- and 20-min time points, the incidence of AEs in the decelerating-drop group was significantly lower than that in the accelerating-rise group (OR 0.19, P = 0.031 and OR 0.23, P = 0.050). However, at the 25- and 30-min time points, the difference between these four groups was not significant (OR 0.26, P = 0.08 and OR 0.29, P = 0.10).ConclusionsThis study classified AD patients into four groups according to the SBP change patterns the first 30 min following admission, of which those with accelerating rises in SBP are at the highest risk of AEs, while those with decelerating drops have the best prognosis in the first 24 h after admission. Clinical practitioners may benefit from analyzing patterns of in-hospital SBP.
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Affiliation(s)
- Zhaoyu Wu
- Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Yixuan Li
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
- Department of Economics, University of Waterloo, Waterloo, ON, Canada
- Stoppingtime (Shanghai) BigData & Technology Co., Ltd., Shanghai, China
| | - Peng Qiu
- Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
| | - Haichun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Ningbo Artificial Intelligent Institute, Shanghai Jiao Tong University, Ningbo, China
| | - Kai Liu
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
- School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, PE, Canada
- Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Weimin Li
- Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Weimin Li
| | - Ruihua Wang
- Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
- Ruihua Wang
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
- Department of Economics, University of Waterloo, Waterloo, ON, Canada
- Senior Research Fellow of Labor and Worklife Program, Harvard University, Cambridge, MA, United States
- Tao Chen
| | - Xinwu Lu
- Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
- Xinwu Lu
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Qiu P, Li Y, Liu K, Qin J, Ye K, Chen T, Lu X. Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data. BioData Min 2021; 14:24. [PMID: 33794946 PMCID: PMC8015064 DOI: 10.1186/s13040-021-00249-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/14/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies. METHODS Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate. RESULTS Compared with the healthy control group, the aortic centerline is significantly more slumped for the AD group. Further, controlling patients' blood pressure and heart rate according to the likelihood of adverse events can offer the highest possible survival probability. CONCLUSIONS The degree of slumpness is introduced to depict aortic morphological changes comprehensively. The morphology-based prediction model is associated with an improvement in the predictive accuracy of the prescreening of AD. The dynamic model reveals that blood pressure and heart rate variations have a strong predictive power for adverse events, confirming this model's ability to improve AD management.
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Affiliation(s)
- Peng Qiu
- Department of Vascular Surgery, Shanghai Ninth People’s Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Big Data Research Lab, University of Waterloo, Waterloo, Canada
| | - Yixuan Li
- Big Data Research Lab, University of Waterloo, Waterloo, Canada
- Department of Economics, University of Waterloo, Waterloo, Canada
- Stoppingtime (Shanghai) BigData & Technology Co. Ltd., Shanghai, China
| | - Kai Liu
- Big Data Research Lab, University of Waterloo, Waterloo, Canada
- School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, Canada
| | - Jinbao Qin
- Department of Vascular Surgery, Shanghai Ninth People’s Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kaichuang Ye
- Department of Vascular Surgery, Shanghai Ninth People’s Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, Canada
- Department of Economics, University of Waterloo, Waterloo, Canada
- Senior Research Fellow of Labor and Worklife Program, Harvard University, Cambridge, USA
| | - Xinwu Lu
- Department of Vascular Surgery, Shanghai Ninth People’s Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Pang H, Chen Y, He X, Tan X, Wang J, Yao Q, Liu X. Twelve-Month Computed Tomography Follow-Up after Thoracic Endovascular Repair for Acute Complicated Aortic Dissection. Ann Vasc Surg 2020; 71:444-450. [PMID: 32891743 DOI: 10.1016/j.avsg.2020.08.125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/25/2020] [Accepted: 08/05/2020] [Indexed: 01/16/2023]
Abstract
BACKGROUND To explore the impact of thoracic endovascular aortic repair (TEVAR) on aortic remodeling (AR) and the relationship between AR and complications after TEVAR. METHODS A total of 56 patients (2 type IIIA aortic dissection [AD] and 54 type IIIB AD) with complicated acute type B aortic dissection suitable for TEVAR were prospectively enrolled. There were 44 men (78%) and 12 women (22%) with an average age of 54 ± 13.8 years. Aortic enhanced computed tomography (CT) was performed pre-TEVAR and 3, 6, and 12 months postoperatively. The morphological changes in AR, namely aortic volume and false lumen thrombosis, were obtained by analyzing the CT data. The effect of TEVAR on AR was determined by the morphological changes in the aorta. The relationship between AR index, false lumen thrombosis, and complications was analyzed. RESULTS The volume of the thoracic aortic true lumen gradually increased post-TEVAR, whereas the volume of the thoracic aortic false lumen gradually decreased. The volume of abdominal aortic total lumen and false lumen increased 6 months postoperatively. The AR index increased significantly 3 months postoperatively, which was negatively correlated with complications and mortality. The thoracic and abdominal aortic false lumen thrombosis developed gradually after TEVAR, and the degree of thoracic aortic false lumen thrombosis was negatively correlated with complications and mortality. CONCLUSIONS TEVAR promotes AR. AR index and the degree of thoracic aortic false lumen thrombosis can serve as predictors of complications and mortality.
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Affiliation(s)
- Huajin Pang
- Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Yong Chen
- Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaofeng He
- Division of Vascular and Interventional Radiology, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiangliang Tan
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Junling Wang
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qianqian Yao
- Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xuehan Liu
- Department of Statistics, Huazhong University of Science and Technology, Wuhan, China
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