Classification tree obtained by artificial intelligence for the prediction of heart failure after acute coronary syndromes. / Árboles de clasificación obtenidos mediante inteligencia artificial para la predicción de insuficiencia cardiaca tras el síndrome coronario agudo.

BACKGROUND: Coronary heart disease is the leading cause of heart failure (HF), and tools are needed to identify patients with a higher probability of developing HF after an acute coronary syndrome (ACS). Artificial intelligence (AI) has proven to be useful in identifying variables related to the development of cardiovascular complications. METHODS: We included all consecutive patients discharged after ACS in two Spanish centers between 2006 and 2017. Clinical data were collected and patients were followed up for a median of 53months. Decision tree models were created by the model-based recursive partitioning algorithm. RESULTS: The cohort consisted of 7,097 patients with a median follow-up of 53months (interquartile range: 18-77). The readmission rate for HF was 13.6% (964 patients). Eight relevant variables were identified to predict HF hospitalization time: HF at index hospitalization, diabetes, atrial fibrillation, glomerular filtration rate, age, Charlson index, hemoglobin, and left ventricular ejection fraction. The decision tree model provided 15 clinical risk patterns with significantly different HF readmission rates. CONCLUSIONS: The decision tree model, obtained by AI, identified 8 leading variables capable of predicting HF and generated 15 differentiated clinical patterns with respect to the probability of being hospitalized for HF. An electronic application was created and made available for free.

Chemical and technological properties of bologna-type sausages with added black quinoa wet-milling coproducts as binder replacer.

The objective of this study was to evaluate different strategies for adding 3% black quinoa (either as whole seeds or as a fiber-rich fraction of quinoa from its wet-milling process) to bologna-type sausage. This addition was evaluated in terms of its influence on nutritional composition and technological properties (emulsion stability, pH, water activity, color changes, textural properties, residual nitrite level and lipid oxidation). Both strategies resulted in commercially feasible sausages with increased nutritive properties (dietary fiber) and with some modifications in their technological properties. Compared with control sausages, they showed better emulsion stability, lower water activity and lipid oxidation values (interesting properties for sausages shelf-life). Color changes were more evident when the fiber-rich fraction was added. The residual nitrite level increased with the addition of quinoa so that it would be necessary to incorporate less nitrites, or it might even be unnecessary, contributing to the production of more natural products.