Coarctation duration and severity predict risk of hypertension precursors in a preclinical model and hypertensive status among patients.

Arash Ghorbannia, Hilda Jurkiewicz, Lith Nasif, Abdillahi Ahmed, Jennifer Co-Vu,Mehdi Maadooliat,Ronald K Woods,John F LaDisa

medRxiv : the preprint server for health sciences(2024)

引用 0|浏览1
暂无评分
摘要
Background:Coarctation of the aorta (CoA) often leads to hypertension (HTN) post-treatment. Evidence is lacking for the current >20 mmHg peak-to-peak blood pressure gradient (BPGpp) guideline, which can cause aortic thickening, stiffening and dysfunction. This study sought to find the BPGpp severity and duration that avoid persistent dysfunction in a preclinical model, and test if predictors translate to HTN status in CoA patients. Methods:Rabbits (N=75; 5-12/group) were exposed to mild, intermediate or severe CoA (≤12, 13-19, ≥20 mmHg BPGpp) for ~1, 3 or 22 weeks using dissolvable and permanent sutures with thickening, stiffening, contraction and endothelial function evaluated via multivariate regression. Relevance to CoA patients (N=239; age=0.01-46 years; median 3.7 months) was tested by retrospective review of predictors (preoperative BPGpp, surgical age, etc.) vs follow-up HTN status. Results:CoA duration and severity were predictive of aortic remodeling and active dysfunction in rabbits, and HTN in CoA patients. Interaction between patient age and BPGpp at surgery contributed significantly to HTN, similar to rabbits, suggesting preclinical findings translate to patients. Machine learning decision tree analysis uncovered that pre-operative BPGpp and surgical age predict risk of HTN along with residual post-operative BPGpp. Conclusions:These findings suggest the current BPGpp threshold determined decades ago is likely too high to prevent adverse coarctation-induced aortic remodeling. The results and decision tree analysis provide a foundation for revising CoA treatment guidelines considering the interaction between CoA severity and duration to limit the risk of HTN.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要