Sepsis poses a life-threatening infection complication, leading to 1.7 million hospitalizations and 350,000 deaths annually in the U.S. Timely diagnosis is crucial, as mortality risk rises by up to 8% per hour without effective treatment. Current diagnostic methods rely on culture growth, typically taking 2-3 days, prompting doctors to administer broad-spectrum antibiotics as a precaution. However, these may offer limited efficacy and pose potential toxicity.
At ASM Microbe, Day Zero Diagnostics introduced a groundbreaking approach in antimicrobial susceptibility testing using artificial intelligence (AI) (1✔ ✔Trusted Source
AI enables faster, more effective antibiotic treatment of sepsis
). Their Keynome® gAST system analyzes bacterial whole genomes from patient blood samples, bypassing the need for culture growth. Initial findings, based on studies involving samples from four Boston-area hospitals, show promising results.
Advancing Sepsis Diagnosis with Machine Learning
Unlike traditional methods that rely on known resistance genes, the machine learning algorithms autonomously identify drivers of resistance and susceptibility based on data from a continuously growing large-scale database of more than 75,000 bacterial genomes and 800,000 susceptibility test results (48,000 bacterial genomes and 450,000 susceptibility test results at the time of this study). This allows for rapid and accurate predictions of antimicrobial resistance, revolutionizing sepsis diagnosis and treatment.
“The result is a first-of-its-kind demonstration of comprehensive and high-accuracy antimicrobial susceptibility and resistance predictions on direct-from-blood clinical samples,” said Jason Wittenbach, Ph.D., Director of Data Science at Day Zero Diagnostics and lead author on the study. “This represents a critical demonstration of the feasibility of rapid machine learning-based diagnostics for antimicrobial resistance that could revolutionize treatment, reduce hospital stays and save lives.”
The researchers say that further study is needed, given the limited sample size, but the findings could contribute to significant advancements in patient outcomes amid the rising threat of antimicrobial resistance and the need for rapid diagnosis and treatment of sepsis.
Funding for this research was provided in part by the Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator (CARB-X).
Reference:
- AI enables faster, more effective antibiotic treatment of sepsis – (https://phys.org/news/2024-06-ai-enables-faster-effective-antibiotic.html)
Source-Eurekalert
