Artificial intelligence (AI) can now deliver same-day evaluations of antimicrobial resistance for patients in intensive care, which is vital for preventing severe cases of sepsis.
Antimicrobial resistance refers to the ability of microorganisms to defend themselves against treatment, creating a significant challenge for healthcare systems worldwide. This issue is estimated to lead to around 1.2 million fatalities each year, with the NHS alone incurring costs of at least £180 million annually.
Bloodstream infections can become resistant to antibiotics and result in the critical condition of sepsis. Once a patient develops sepsis, they are at a high risk of swiftly experiencing organ failure, shock, or even death.
Factors such as previous antibiotic use, genetic makeup, and diet—which can influence their microbiome—mean that not all patients exhibit the same level of antimicrobial resistance.
Researchers are now using AI technology to evaluate antimicrobial resistance in patients within intensive care units (ICUs) and to detect infections in the bloodstream that may cause sepsis.
A collaboration between scientists from King’s College London and clinicians at Guy’s and St Thomas’ NHS Foundation Trust has led to this interdisciplinary study, aiming to enhance outcomes for critically ill patients.
Making strides in this field, the team demonstrated how AI and machine learning can enable same-day assessment for ICU patients, particularly in settings with constrained resources. This technology also proves to be much more cost-effective than traditional manual testing methods.
Current evaluations for ICU patients require extensive laboratory testing that can take up to five days because bacteria must be cultured. This delay severely impacts patient care, particularly for those in fragile states due to serious health conditions.
Access to faster evaluations would empower clinicians to make timely, well-informed decisions regarding patient treatment, including the administration of antibiotics. Correct antibiotic usage is strongly linked to better patient results.
Davide Ferrari, the study’s first author from King’s College London, stated: “Our research reinforces the positive impact of AI in healthcare, especially concerning antimicrobial resistance and bloodstream infections. This is particularly timely as the NHS invests in shared data resources to enhance collaborative and efficient patient care.”
“Our employment of machine learning introduces a fresh approach to combat the significant issue of antimicrobial resistance. We hope this AI tool will assist clinicians in making vital decisions, particularly for ICU patients.”
Dr. Lindsey Edwards, a microbiology specialist at King’s College London, noted: “One crucial strategy to mitigate the serious threat of antimicrobial resistance is to safeguard our existing antibiotics alongside the pressing need for rapid diagnostic methods. Often, patients with drug-resistant infections arrive at the ICU in critical condition, and there may not be enough time for traditional diagnostics to reveal the nature of their infection. This puts clinicians in a tough position where they must administer a broad-spectrum antibiotic without definite knowledge of the specific infection to save the patient’s life.
“However, using broad-spectrum antibiotics can eliminate many beneficial microbes in the patient’s microbiome without effectively targeting the harmful pathogen, possibly making the pathogen more resistant to treatment.”
“The implications of our findings are incredibly encouraging; employing AI to accelerate infection diagnostics could greatly enhance patient survival rates and treatment outcomes while preserving existing antibiotics and curbing the rise of further antimicrobial resistance.”
The study analyzed data from 1,142 patients at Guy’s and St Thomas’ NHS Foundation Trust, paving the way for further research involving data from over 20,000 individuals. There is optimism that a more sophisticated approach, especially in a multi-hospital context using Federated Machine Learning technology, could meet the necessary regulatory standards for deploying this AI strategy in frontline NHS settings.
Professor Yanzhong Wang, a population health expert at King’s College London, remarked: “The simplicity and scalability of this innovative machine learning approach highlight its potential for broad implementation, providing a sustainable solution to these pressing healthcare challenges and ultimately enhancing patient outcomes.”
