Antibiotic resistance: a digital platform to choose the most effective therapy
The project is led by Kelyon with the IDI in Rome: a machine learning-based antibiogram predicts resistance and identifies patients at risk
Antibiotic resistance is now more than a threat, as antibiotic-resistant bacteria are among the most serious dangers, especially in hospitals. According to the Italian Medicines Agency, antibiotic resistance causes 12,000 deaths a year in our country, burdening the National Health Service’s coffers with 2.4 billion in hospital beds.
According to the WHO, if the trend is not reversed, antibiotic resistance will be the leading cause of death in 2050 in Italy and Europe.
In fact, the increasing spread of multi-resistant bacteria threatens the sustainability of healthcare systems and makes the development of new, more effective antibiotics, but also the adoption of advanced tools for the monitoring and proper management of infections and resistance, urgent.
The project also involved the University of Salerno
Kelyon, with the support of the Istituto Dermopatico dell’Immacolata in Rome, as part of a research project conducted with the University of Salerno, has developed AntiMO, an advanced digital platform designed to support doctors and healthcare facilities in the fight against antibiotic resistance.
“With AntiMO we wanted to put technology at the service of medicine, offering a concrete tool to support clinicians and counter one of the most urgent challenges of modern healthcare,” said Gaetano Cafiero, CEO of Kelyon.
Decision support for doctors on choice of therapy
‘The platform,’ says IDI-Irccs CEO Alessandro Zurzolo, ‘is currently being integrated into the Institute’s IT system and is able to provide decision-making support to doctors in choosing the most effective antibiotics and in the continuous monitoring of antibiotic resistance.
AntiMO integrates with hospital diagnostic systems from a precision medicine approach, and – using artificial intelligence algorithms and predictive models – identifies patients at risk of antibiotic resistance, suggests optimisation of antibiotic prescriptions and monitors resistance trends.
The platform then visualises up-to-date data on the spread of resistance in the hospital environment in real time, making it possible to identify the areas and departments at greatest risk and plan targeted prevention strategies.
The heart of the system is a digital antibiogram
AntiMO also integrates the AWaRe guidelines of the WHO: it monitors prescription compliance and promotes therapeutic appropriateness, reducing the misuse of antibiotics by connecting to diagnostic systems and data storage platforms, facilitating the early identification of infectious microorganisms. “At the heart of the predictive system,” the researchers explain, “is a digital antibiogram based on machine learning, which predicts antibiotic resistance many hours in advance of the antibiogram obtained by the laboratory and displays the results according to WHO classification. The benefits include faster and more appropriate treatments, reduced complications, dissemination of best clinical practices, reduced healthcare costs, and improved efficiency of hospital resources’.
