Machine learning-based tool to assess risk of hemodynamically significant PDA in extremely premature infants

We report the first machine learning-based risk assessment tool for the prediction of a hemodynamically significant PDA. Several characteristics differ significantly between patients with spontaneous closure of the arterial duct and those with PDA, but a comprehensive and accurate assessment to predict hemodynamic significance is missing. The simple pen-and-paper score enables us to allocate risk for hsPDA using an easy-to-use pen-and-paper score with high accuracy. Risk assessment at an early stage might help guide targeted surveillance, prophylactic strategies, or individualized treatment decisions in extremely preterm infants.