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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: 1.3] [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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Ullah S, Finch CF. Applications of functional data analysis: A systematic review. BMC Med Res Methodol 2013; 13:43. [PMID: 23510439 PMCID: PMC3626842 DOI: 10.1186/1471-2288-13-43] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 03/04/2013] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods. METHODS A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995-2010. Papers reporting methodological considerations only were excluded, as were non-English articles. RESULTS In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data. CONCLUSIONS Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.
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Affiliation(s)
- Shahid Ullah
- Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Faculty of Health Sciences, Flinders University, Adelaide, SA, 5001, Australia
| | - Caroline F Finch
- Centre for Healthy and Safe Sports (CHASS), University of Ballarat, SMB Campus, Ballarat, VIC, 3353, Australia
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