Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data

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.

Potential Clinical Value of Biomarker-Guided Emergency Triage for Thoracic Aortic Dissection

“…At present, expensive imaging examinations and biomarkers with poor specificity create a barrier to quickly identifying such high-risk patients. In our previous studies, we attempted to provide clues for the prevention of TADs through simple, inexpensive, and repeatable examinations, such as screening morphological risk factors for TAD in plain chest computed tomography scans ( 61 , 62 ). And in this study, some potential biomarkers were obtained and great predictive efficacy was achieved.…”

Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic Dissection After Admission

“…The patients' BPs were measured approximately every 15-30 min by automated noninvasive BP monitors, and the demographic information and medical history of each patient was collected from his or her medical records. In this analysis, the presence of another acute AD in the past is considered as having an AD history, and complicated AD is defined as persistent pain, uncontrolled hypertension, early aortic expansion, malperfusion, and signs of rupture (18).…”

“…Thus, the underlying SBP process is a function over time intervals, and it was necessary to conduct functional data analysis (FDA) to first estimate the process before analysis (21,22). FDA is a nonparametric and continuous analysis technique proposed by Ramsay for functional data and has been shown to be an accurate estimation tool that can automatically adapt to the correct limit and recover the true underlying structure from discretely observed data in a wide variety of fields such as biomedical science, medicine, economics, finance, linguistics, psychology and sports (18,(23)(24)(25)(26)(27)(28). The complete estimation process is illustrated in the Supplementary File.…”

“…In addition, the thrombosis ratio of the false lumen was significantly lower in the high BPV group at the 6-month follow-up (86.4 vs. 69.7%) (19). Qiu et al showed the association between unit increases in BP and the incidence of in-hospital AEs in AD patients by FDA (18). In the current study, based on our multicenter data, we found that patients with a history of hypertension were more likely to have high SBPV, which was further associated with an increased incidence of AEs.…”

“…Second, the fixed threshold fails to take into account the dynamic nature of BP, which is critical in the development of cardiovascular events. For instance, earlier studies have suggested that the circadian rhythm of BP fluctuation has an impact on the occurrence of AD (14)(15)(16)(17) and that BP variations have strong predictive power for adverse events (AEs) in AD (18,19). There is, however, no clear correlation between the pattern of change in BP and the prognosis for AD.…”