SMART-CDSS An artificial intelligence system for antimicrobial prescription support
Creators: name: Pillonetto, Marcelo
name: Kalil, Aline
name: Becker, Guilherme
name: Giamberardino, Ana Luisa
name: Teixeira, Beatriz
name: Bergamo, Ricardo
name: Madeira, Humberto
name: Dias, Viviane
name: Miorando, Rogerio
name: Giglio, Ricardo
Publisher: name: Gates Open Research
Year: 2020

description: The antimicrobial prescription has become a significant challenge in the last decades. The overuse and misuse of antimicrobial drugs have led antimicrobial resistance (AMR) to be considered a global public health issue, with ten million deaths/yearly predicted by 2050. Antimicrobial Susceptibility Test (AST) is a crucial tool for making a correct antimicrobial prescription, especially for inpatients. Unfortunately, this test is available 48 to 72 hours after the sample collection. Despite many Antimicrobial Stewardship Programs (ASP) implemented in hospitals worldwide, COVID-19 pandemics dramatically raised the antimicrobial prescription without further evidence of its needs. These unnecessary prescriptions push AMR even further to an eminent shortage of drugs, with many bacteria resistant to all antibiotics available. Considering this scenario, we develop a tool that uses an algorithm based on antimicrobial Pharmacokinetics and Pharmacodynamics, epidemiology of AST, and patient demography to build up the so-called SMART-CDSS. Its purpose is to serve as a support decision tool in the antimicrobial prescription from the general practitioner to the Infectious Diseases Physician, gathering AI/machine learning with an Evidence-Based Medicine approach. MVP App was developed for iOS and Android. We are actually in the validation process with the MVP by a high-skilled infectious disease practitioner team. Up to now, the System requires and analyses the following inputs: outpatient/inpatient, sex (pregnancy or breastfeeding for females), age group, allergy to antibiotics, presence of comorbidities, calculation and core of sepsis risk and renal function, oral/parenteral route, anatomic site of the infection (urinary tract, respiratory tract or bloodstream infection) and microbiology results if available (gram stain, bacterial group, bacterial species). Based on these inputs, the algorithm builds up a calculation process that considers a National Surveillance Database (BrGLASS) - composed of more than 38,000 bacterial isolates - and its AST, which works as a backbone selecting the most predictable agent for that inputted data. The outcome of the System is a ranking of the drugs most likely to succeed. It includes a calculated probability of success; based on the System algorithm's pharmacological, demographical, microbiological, and epidemiological data. Our next steps are: 1- To improve the algorithm and the back-end/front-end of the MVP, after the clinical experts' validation; 2- To extend the microorganisms database, 3- Inclusion of anatomical sites: skin and soft tissue, central nervous system, gastrointestinal and head/neck infections, and 4- To customize the epidemiology database in levels (i.e., one specific clinic or hospital; city level, state level up to country level). <br><br>We believe that SMART-CDSS can improve medical decisions in antimicrobial prescription and significantly reduce the selective pressure towards antimicrobial resistance.