Machine Learning Estimates Prognosis in Adult Congenital Heart Disease - Medical Bag

Machine learning algorithms using large datasets may be utilized to accurately estimate prognosis and guide therapy for patients with adult congenital heart disease, according to a study published in the European Heart Journal. The investigators of this large cohort, single-center study sought to examine the utility of machine learning algorithms as a prognostic model and to guide therapeutic decision-making in patients with adult congenital heart disease or pulmonary hypertension. The study sample included 10,019 adults under active follow-up at the Royal Brompton Hospital in London between 2000 and 2018. Patient data were retrospectively collected -- including clinical and demographic data, ECG parameters, cardiopulmonary exercise data, and laboratory markers -- and incorporated into deep learning algorithms. Specific deep learning models were then built for patient categorization into diagnostic subsets, disease complexity subsets, and by New York Heart Association (NYHA) class.