UNASSIGNED:心血管磁共振(CMR)是急性病毒性心肌炎中心肌炎症诊断和危险分层的首选测试。本研究的目的是评估来自北非的一组急性心肌炎患者的CMR炎症模式。亚洲,和中东使用无监督机器学习。
UNASSIGNED:共研究了169名种族和种族不同的成年人(≥18岁),CMR确诊为急性心肌炎。主要结果是心脏死亡的联合临床终点,心律失常,和扩张型心肌病.机器学习用于探索性分析以识别CMR炎症的模式。
未经评估:我们的队列是多样化的,其中25%来自北非,33%来自南亚,28%来自西亚/中东。12名患者达到联合临床终点-3名患有心律失常,8人患有扩张型心肌病,1人死亡。符合联合终点的患者前部(p=0.034)和间隔(p=0.042)晚期钆增强(LGE)增加。多变量逻辑回归,根据年龄调整,性别,BMI,发现来自南亚(p=0.041)和中东(p=0.043)的患者与外侧LGE独立相关。无监督机器学习和因子分析确定了两种不同的炎症CMR模式,一个LGE升高,另一个心肌T1/T2升高。
UNASSIGNED:我们发现前间隔炎症与恶化的预后相关。使用机器学习,我们在来自南亚的种族和种族不同的CMR患者中发现了两种急性心肌炎的心肌炎症模式。北非,和中东。
UNASSIGNED: Cardiovascular magnetic resonance (CMR) is the test of choice for diagnosis and risk stratification of myocardial inflammation in acute viral myocarditis. The objective of this study was to assess patterns of CMR inflammation in a cohort of acute myocarditis patients from Northern Africa, Asia, and the Middle East using unsupervised machine learning.
UNASSIGNED: A total of 169 racially and ethnically diverse adults ( ≥ 18 years of age) with CMR confirmed acute myocarditis were studied. The primary outcome was a combined clinical endpoint of cardiac death, arrhythmia, and dilated cardiomyopathy. Machine learning was used for exploratory analysis to identify patterns of CMR inflammation.
UNASSIGNED: Our cohort was diverse with 25% from Northern Africa, 33% from Southern Asia, and 28% from Western Asia/the Middle East. Twelve patients met the combined clinical endpoint - 3 had arrythmia, 8 had dilated cardiomyopathy, and 1 died. Patients who met the combined endpoint had increased anterior (p = 0.034) and septal (p = 0.042) late gadolinium enhancement (LGE). Multivariable logistic regression, adjusted for age, gender, and BMI, found that patients from Southern Asia (p = 0.041) and the Middle East (p = 0.043) were independently associated with lateral LGE. Unsupervised machine learning and factor analysis identified two distinct CMR patterns of inflammation, one with increased LGE and the other with increased myocardial T1/T2.
UNASSIGNED: We found that anteroseptal inflammation is associated with worsened outcomes. Using machine learning, we identified two patterns of myocardial inflammation in acute myocarditis from CMR in a racially and ethnically diverse group of patients from Southern Asia, Northern Africa, and the Middle East.