Exploring the complexities of antimicrobial resistance forecasting
In a recent article published in the United States Centers for Disease Control and Prevention (CDC) Emerging Infectious Diseases, researchers highlight the need for prioritizing research on antimicrobial-resistant organism (AMRO) forecasting. Moreover, they discuss current challenges in AMRO forecasting and the potential of forecasting tools at the population and facility levels.
Study: Challenges in Forecasting Antimicrobial Resistance. Image Credit: Michael Design / Shutterstock.com
The growing threat of antimicrobial resistance
In 2019, about 4.95 million deaths were attributed to bacterial antimicrobial resistance, with most of these deaths due to Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa.
Despite the numerous advancements that have been made in developing predictive models for both viral and acute infectious diseases such as influenza, dengue, and the coronavirus disease 2019 (COVID-19), there remains a lack of forecasting models that have been implemented for predicting the severity of future AMROs. Improving the predictive intelligence on AMROs will allow researchers and public health officials to gain important insights into the potential emergence and spread of antimicrobial resistance within populations and healthcare facilities.
Current forecasting approaches
Various mathematical and statistical models have been used to combat antimicrobial resistance, with some of these models having potential applications as forecasting tools. Time series analyses, for example, have previously been used to determine the association between antibiotic use and the prevalence of antimicrobial resistance at the population level; however, this type of analysis is insufficient in its ability to predict antimicrobial prevalence.
Process-based mathematical models have also been used to study AMROs by stimulating competition between resistant and sensitive strains. Additionally, individual-level models have been developed by incorporating information from previous patients or contact with healthcare workers in order to devise potential transmission networks in healthcare facilities.
When developing antimicrobial resistance forecasting models, it is essential first to determine whether these tools will be applied to population- or facility-level scales. When applied at the population level, AMRO forecasting can predict how the infection will affect the general population for extended periods ranging from months to years.
At the population level, forecast targets may include the number of antimicrobial-resistant infections or the proportion of isolates that exhibit resistance. Collectively, this information could be used to determine the future burden of antimicrobial resistance within the population, including its impact on deaths, hospitalization, days of work lost, or direct and indirect economic costs.
Comparatively, at the facility level, the forecast target may instead be the number of antimicrobial infections detected within a single hospital or hospital system. With this information, healthcare facilities can preemptively allocate resources, including equipment, medications, staffing, and hospital space, if a surge in these infections arises.
Challenges in AMRO forecasting
Lacking a scientific understanding of the reasons for the spread of antimicrobial-resistant pathogens is the first and foremost issue with designing accurate forecasting models. To date, there remains a lack of understanding of how antibiotic use contributes to the development of antibiotic resistance and whether a single antibiotic drug has a more significant impact on the emergence of resistant species.
The extent to which competition between susceptible bacterial strains might impact the incidence of resistant strains also remains poorly understood. The coexistence of these strains over extended periods of time also remains unclear.
In acute viral infections, viral load can generally be linked to infectivity and disease phenotype, which can subsequently allow researchers to predict the severity of these infections. However, the correlation between pathogen load and clinical outcomes is less clear for bacterial or fungal infections. This is mainly due to the vast commensal bacterial population that resides within humans, many of which can be found in varying levels throughout the human body.
In some countries, surveillance systems gather data on changes in antimicrobial susceptibility; however, these systems do not track pathogens responsible for healthcare-related infections. Moreover, antimicrobial-resistant pathogen profiling is much more limited in low- and middle-income countries (LMICs). To date, despite surveillance at the population level, data to inform operationally beneficial forecasts of antimicrobial resistance remain inadequate.
In healthcare settings, since the surveillance for asymptomatic AMRO carriage is not of immediate clinical interest, it has hindered the estimation of overall AMRO prevalence, thereby leading to biased prediction targets for AMRO prediction models. It is also impractical to gather data on nonbiologic processes driving the transmission of resistant pathogens, such as patient interactions with healthcare staff. Overall, antimicrobial-resistant data from the facility level is scarce, missing, or of poor quality.
In the future, all stakeholders, such as healthcare practitioners, public health officials, and healthcare organizations, must identify specific requirements from antimicrobial resistance modeling to facilitate the generation of operational forecasts in real-world settings. In addition, computer scientists should design more efficient computational algorithms to calibrate antimicrobial resistance models to varied, multiscale data and more interpretable models to ensure that clinicians are more confident about these tools.
In healthcare settings, consistently collecting data on the testing and reporting of antimicrobial-resistant infections would standardize training and forecasting targets, the scale of the forecast horizon, and proper scoring rules, which, in turn, would ease evaluating forecast performance.
To conclude, there remains an urgent need to set appropriate expectations and establish good criteria for the successful implementation of AMRO prediction tools. However, with the collaborative efforts of all stakeholders, antimicrobial-resistant forecasting can successfully address real-world issues in public health and patient care.
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