PMID- 35722130
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2297-055X (Print)
IS  - 2297-055X (Electronic)
IS  - 2297-055X (Linking)
VI  - 9
DP  - 2022
TI  - Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic 
      Dissection After Admission.
PG  - 832770
LID - 10.3389/fcvm.2022.832770 [doi]
LID - 832770
AB  - OBJECTIVES: Hypertension 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. METHODS: This 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. RESULTS: A 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). CONCLUSIONS: This 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.
CI  - Copyright (c) 2022 Wu, Li, Qiu, Liu, Liu, Li, Wang, Chen and Lu.
FAU - Wu, Zhaoyu
AU  - Wu Z
AD  - Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's 
      Hospital, Shanghai Jiao Tong University, Shanghai, China.
FAU - Li, Yixuan
AU  - Li Y
AD  - Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.
AD  - Department of Economics, University of Waterloo, Waterloo, ON, Canada.
AD  - Stoppingtime (Shanghai) BigData & Technology Co., Ltd., Shanghai, China.
FAU - Qiu, Peng
AU  - Qiu P
AD  - Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's 
      Hospital, Shanghai Jiao Tong University, Shanghai, China.
AD  - Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.
FAU - Liu, Haichun
AU  - Liu H
AD  - Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
AD  - Ningbo Artificial Intelligent Institute, Shanghai Jiao Tong University, Ningbo, 
      China.
FAU - Liu, Kai
AU  - Liu K
AD  - Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.
AD  - School of Mathematical and Computational Sciences, University of Prince Edward 
      Island, Charlottetown, PE, Canada.
AD  - Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
FAU - Li, Weimin
AU  - Li W
AD  - Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's 
      Hospital, Shanghai Jiao Tong University, Shanghai, China.
FAU - Wang, Ruihua
AU  - Wang R
AD  - Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's 
      Hospital, Shanghai Jiao Tong University, Shanghai, China.
FAU - Chen, Tao
AU  - Chen T
AD  - Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.
AD  - Department of Economics, University of Waterloo, Waterloo, ON, Canada.
AD  - Senior Research Fellow of Labor and Worklife Program, Harvard University, 
      Cambridge, MA, United States.
FAU - Lu, Xinwu
AU  - Lu X
AD  - Department of Vascular Surgery, School of Medicine, Shanghai Ninth People's 
      Hospital, Shanghai Jiao Tong University, Shanghai, China.
LA  - eng
PT  - Journal Article
DEP - 20220603
PL  - Switzerland
TA  - Front Cardiovasc Med
JT  - Frontiers in cardiovascular medicine
JID - 101653388
PMC - PMC9204146
OTO - NOTNLM
OT  - adverse events
OT  - aortic dissection
OT  - blood pressure
OT  - classification
OT  - functional data analysis
COIS- YL was employed by Stoppingtime (Shanghai) BigData & Technology Co., Ltd., 
      Shanghai, China. The remaining authors declare that the research was conducted in 
      the absence of any commercial or financial relationships that could be construed 
      as a potential conflict of interest.
EDAT- 2022/06/21 06:00
MHDA- 2022/06/21 06:01
PMCR- 2022/01/01
CRDT- 2022/06/20 04:04
PHST- 2021/12/10 00:00 [received]
PHST- 2022/05/09 00:00 [accepted]
PHST- 2022/06/20 04:04 [entrez]
PHST- 2022/06/21 06:00 [pubmed]
PHST- 2022/06/21 06:01 [medline]
PHST- 2022/01/01 00:00 [pmc-release]
AID - 10.3389/fcvm.2022.832770 [doi]
PST - epublish
SO  - Front Cardiovasc Med. 2022 Jun 3;9:832770. doi: 10.3389/fcvm.2022.832770. 
      eCollection 2022.