Key result
Patients with aortic dissection had significantly more slumped aortic centerlines compared to healthy controls, demonstrated by a lower average of slopes (0.74 vs 1.42).
Why the study?
Deviant increases in aortic dissection incidence and in-hospital mortality over the past 20 years highlighted the need for fresh prescreening and in-hospital treatment strategies.
Do image recognition and functional data analysis improve prescreening and treatment strategies for aortic dissection?
Cross-Sectional (n=519)
Yes
Do image recognition and functional data analysis improve prescreening and treatment strategies for aortic dissection?
Absolute Event Rate: 0.74% vs 1.42%
p-value: p=<0.001
A morphology-based prediction model using image recognition and a dynamic model for blood pressure and heart rate interventions can improve the prescreening and management of aortic dissection.
Slumped aortic centerlines may mark dissection vulnerability; hypothesis-generating and requires prospective validation before clinical use.
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.
No takes yet. Share an insight, caveat, or question.
Qiu et al. (2021) conducted a cross-sectional in Aortic dissection (n=519). Aortic dissection vs. Healthy controls was evaluated on Average of slopes of the aortic centerline (p=<0.001). Patients with aortic dissection had significantly more slumped aortic centerlines compared to healthy controls, demonstrated by a lower average of slopes (0.74 vs 1.42).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: