Background-Despite successful surgical correction, morbidity of patients with coarctation of the aorta is increased. It is well known that these patients have impaired elastic properties of the prestenotic arteries. To find out whether these abnormalities are primarily present or develop later, we studied 17 newborns before and early after surgical repair. Methods and Results-Aortic wall stiffness index and distensibility were calculated using ascending and abdominal aortic diameters determined by M-mode echocardiography and noninvasive estimation of aortic pulse pressure in the right arm and leg.

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Six machine learning techniques have been investigated for their classification accuracy focusing on two metabolic disorders, phenylketo nuria (PKU) and medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Logistic regression analysis led to superior classification rules (sensitivity >96.8%, specificity >99.98%) compared to all investigated algorithms. Including novel constellations of metabolites into the models, the positive predictive value could be strongly increased (PKU 71.9% versus 16.2%, MCADD 88.4% versus 54.6% compared to the established diagnostic markers). Our results clearly prove that the mined data confirm the known and indicate some novel metabolic patterns which may contribute to a better understanding of newborn metabolism.

In young patients with Marfan syndrome, a computerized image-analyzing technique revealed decreased aortic elastic properties expressed by parameters showing high diagnostic power. A multiple logistic regression model including merely aortic parameters can serve as useful predictor for Marfan syndrome.