Machine learning to support decision-making for cardiac surgery during the acute phase of infective endocarditis

Rapid identification of patients at high risk of death may trigger additional therapeutic interventions, which in turn may change the course of the disease and improve prognosis. For infective endocarditis (IE), inhospital mortality rate remains high—from 15% to 30%—despite major achievements over the last 20 years in diagnostic tools (imaging, microbiology) and cardiac surgery.1 Prognostic factors include, in addition to the usual suspects (eg, age, comorbidities, heart failure), variables specific to the pathophysiology of IE: periannular complication, Staphylococcus aureus infection or persistence of positive blood cultures >72 hours after initiation of appropriate antibiotic treatment. The recommendation is to early transfer these patients to referral centres, where an endocarditis team will adjust their management and evaluate the indication of, and—in selected cases—the best timing for, cardiac surgery.2

The endocarditis team is a great innovation strongly supported by recent guidelines from America and Europe: The American 2015 guidelines