3 Ways Bioinformatics Can Change the Fight Against AMR
healthcaretechoutlook

3 Ways Bioinformatics Can Change the Fight Against AMR

Oliver Schacht, PhD, CEO of OpGen

Oliver Schacht, PhD, CEO of OpGen

Technology solutions and platforms that facilitate detection of drug-resistant infections are proving to be incredibly valuable tools in the global fight against antimicrobial resistance (AMR). The WHO placed AMR on its list of top 10 global public health threats in October 2020. And rightfully so – “superbugs,” as they’ve come to be called, claim 35,000 lives in the U.S. annually and cause 2.8 million infections. These infections contribute to increasing costs as well as higher morbidity and mortality across healthcare systems. Despite the heightened awareness about AMR and initiatives focused on antimicrobial stewardship to curb unnecessary antibiotic use, aggressive administration of broad-spectrum antibiotics, especially in the ICU setting, is common practice. In scenarios where the causative pathogen and whether it is drug-susceptible or drug-resistant are unknown, clinicians rely on empirical use of antibiotics. And now, the ongoing COVID-19 pandemic has further increased prescribing broad-spectrum antibiotics, with reports that antibiotics were administered in 72% of COVID-19 cases, even when not clinically indicated in some cases. This trajectory is dangerous, and it could exacerbate the global spread of AMR, leading to more deaths from once treatable infections as the world runs out of antibiotics that still work against bacteria that have developed resistance.

Healthcare systems are adopting platforms that encourage smarter antibiotic use and deliver rapid treatment insights at a faster rate in recent years. Cloud-based solutions and data-sharing platforms offer new ways for these facilities and their staff members to collaborate in the fight against AMR and other infectious diseases. These technologies are expected to impact outcomes in several important ways.

Antibiotic Susceptibility & Genetic Resistance Prediction

Healthcare workers strive to make treatment decisions as quickly as possible to alleviate patient suffering and move toward positive outcomes. There are now more tools available than ever before to aid in faster, more reliable decision making, including rapid molecular diagnostic testing. Test results can be delivered in just hours, directly from patient samples. This is a tremendous improvement over conventional culture methods, which still take days to report results.

Also, increasingly, whole genome sequencing (WGS) is being deployed routinely in many clinical microbiology laboratories. These technologies expand the laboratory’s capabilities to provide clinicians with accurate pathogen identification and antibiotic resistance detection. What’s more, automated interpretive tools are becoming increasingly refined, with studies showcasing next generation sequencing (NGS) workflows capable of identifying  antibiotic resistance markers with >95% sensitivity and >99% specificity and identifying pathogens correctly with 100% sensitivity and specificity. Results such as these are incredibly promising and speak to a broader need for widespread access to AI-powered algorithms, bioinformatics and machine-learned models that can identify resistance markers and predict antimicrobial susceptibility (AST).

Identifying AMR Threats & Tracking Outbreaks

With cloud-based software, healthcare workers can quickly and easily analyze genetic information to identify novel AMR markers and threats. Tens of thousands of clinical isolates have been compiled over many years and in some cases even several decades to form databases of genotypic and phenotypic profiles. These collections are kept up to date regularly with AST information on hundreds of antibiotics, NGS data and pathogen strains.

Backed by the power of machine learning algorithms, healthcare workers can draw on these databases to identify AMR threats in their patient populations as well as receive insights on effective treatment courses for specific pathogens. The ability to take confident, decisive action against known and novel pathogens especially in light of certain resistance markers is crucial for healthcare workers grappling with infectious disease outbreaks.

Taking it a step further, data from treatment outcomes, individual diagnostic tests and genetic analysis can be shared across facilities via digital surveillance infrastructures. Broader collaborative efforts are crucial to advance our understanding of infectious diseases through more frequent, seamless exchange of patient and AMR data.

Empowering Confident Decision Making

Moving swiftly and confidently toward a treatment decision for infectious diseases is paramount, not only for improved patient outcomes but also to enable more judicious use of available antibiotics. Digital health platforms utilizing NGS for pathogen and resistance marker characterization can be joined with AI-powered reporting systems for more informed decision making. Through a cloud-based clinical decision system, AMR markers can be linked to treatment responses to guide healthcare workers with their review of patient results and provide actionable insights for more effective treatment decision.

Responsible decision making at the patient level goes hand-in-hand with improved antibiotic stewardship. Broader access to pathogen and resistance marker data available in cloud-based reporting systems enables doctors to administer the most appropriate antibiotic for a patient’s condition, reduces overuse and misuse of antibiotics, limits AMR and preserves patient microflora.

To address the threat of AMR more comprehensively and effectively, digital health tools are available today which can help healthcare workers preserve existing antibiotics as effective weapons through guided decision-making and gather pathogen and AMR marker information on a broader scale. With healthcare workers grappling with AMR and infectious disease outbreaks that threaten public health globally, now is the time to invest in these technologies and encourage widespread adoption of bioinformatics solutions. 

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