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Q&A: Preventing hospital outbreaks with surveillance technology (Includes interview)

New precision medicine solutions are capable of rapidly identifying and managing superbugs in healthcare facilities. These have the goal of cutting down on costly and dangerous misdiagnoses. One such solution comes from OpGen, a genomics and informatics company offering technology solutions in this space.

OpGen is currently rolling out an initiative with the New York State DoH and Merck to track these infections across state hospitals and recommend treatments based on precision medicine data, with the goal of reducing infection transmission and cutting down on antibiotic overuse.

To understand more about the technology, Digital Journal spoke with Evan Jones, CEO of OpGen.

Digital Journal: How big a problem are nosocomial infections?

Evan Jones: Nosocomial infections are a major challenge for the healthcare industry. The CDC recently estimated that each day, one in 31 patients is affected by a hospital-acquired infection. Even with more strict attention being paid to infection control practices, there are many factors present in hospital environments which make these types of infections common, particularly in high-risk populations.

Patients are more susceptible to infection after undergoing surgery, if they are using a medical device, such as a catheter, or if they have a preexisting condition, such as diabetes. And, of course, elderly patients living in nursing and long-term care facilities are at a higher risk of infection. In addition to patient suffering, these infections often lead to longer hospital stays and higher rates of readmittance, racking up billions of dollars in healthcare costs.

DJ: How frequently does misidentification of a bacterium occur?

Jones: While multiple bacteria may be present, identifying which bacteria is causing an infection can present challenges, it is not the most significant problem we face because rapid technologies are available to identify the pathogen. The problem is rooted in knowing which antibiotics will be effective to treat the infecting bacteria at the time of prescribing the first dose. For example, we may identify the bacteria as E. coli, but not all E. coli can be treated with the same antibiotics. In fact, in more than one-third of Urinary Tract Infection (UTI) cases, it has been shown that the infecting bacteria is resistant to front-line antibiotics. Unfortunately, traditional laboratory methods can take up to three days to identify the appropriate antibiotic therapy to treat the infection.

DJ: What are the implications of the wrong identification in relation to patient care?

Jones: First and foremost, patients who are initially treated with ineffective antibiotics often go through needless suffering as ineffective treatments are administered to no avail. Infection patients are also more likely to remain in the hospital for longer periods of time, which can be stressful, particularly for older high-risk patients and the individuals who manage their care. The longer a stay, the more nurses and doctors become involved in helping with patient care, which increases the likelihood of a miscommunication or mistake when it comes to keeping track of the patient’s symptoms and treatment courses. This process also significantly adds to hospital costs.

DJ: Does misidentification exacerbate the antimicrobial resistance issue?

Jones: Misidentification is certainly an issue, but the length of time required to determine the correct antibiotic can create an even larger challenge. Often doctors’ first course of action when diagnosing an infection is to treat with frontline antibiotics. However, if the pathogen is multi-drug resistant there’s a likely chance that their first round of treatment will be ineffective. Not only will the patient not improve, but the use of the front-line antibiotics can drive antibiotic resistance in the patient’s other bacteria. Should these other bacteria flourish to infection levels or be transmitted through the hospital; this can lead to proliferation of antibiotic resistance through the hospital leaving a reduced supply of effective drugs left in the pharmacy.

DJ: What is the OpGen solution to this issue?

Jones: OpGen combines advanced, rapid diagnostics with precision analytics to quickly identify and treat antibiotic resistance threats in healthcare institutions. The Acuitas AMR Gene Panel rapidly detects the DNA of common bacterial pathogens in complicated Urinary Tract Infections and antibiotic resistance genes for common antibiotics used to treat these cases. The Acuitas Lighthouse interprets the genetic data and accurately predicts antibiotic resistance to help guide the appropriate antibiotic therapy.

Healthcare professionals receive this information in less than three hours, compared to three days for traditional methods. Additionally, OpGen’s technology gathers infection data and information on patient outcomes to create a surveillance database of infection strains and treatment courses that can be tapped into by healthcare professionals across an entire facility or network. All results and treatment outcomes are stored in the database, and caregivers are alerted immediately if a patient’s results indicate the presence of an MDRO, so that the proper action can be taken and outbreaks can be prevented.

DJ: How does the technology work?

Jones: We have developed the Acuitas AMR Gene Panel, a PCR assay capable of detecting 47 resistance genes covering nine classes of antibiotics and the five most common bacterial pathogens in complicated UTI cases. Results, which are typically available in under three hours are uploaded to the cloud-based Acuitas Lighthouse software which predicts effective antibiotics and displays genetic resistance profiles for each bacterial organism tested.

The proprietary software can then track and trend these organisms either at the hospital-room level or across multiple healthcare institutions within a healthcare system. Lighthouse software analytics are able to profile and group similar antibiotic-resistant organisms with high resolution to instantly signal possible transmission events. Additionally, it can signal resistance patterns that might appear in specific profiles, which can alert the healthcare institution of an extreme MDRO threat.

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Written By

Dr. Tim Sandle is Digital Journal's Editor-at-Large for science news. Tim specializes in science, technology, environmental, business, and health journalism. He is additionally a practising microbiologist; and an author. He is also interested in history, politics and current affairs.

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