Innovative Methods Could Help Predict Flu Outbreaks and Prevent the Spread of Antibiotic Resistance


Researchers have discovered new methods that could improve treatment for infectious diseases by enabling earlier detection of influenza outbreaks and curtailing inappropriate antibiotic usage. The findings were presented today at the 69th AACC Annual Scientific Meeting & Clinical Lab Expo in San Diego.

Two major issues in the field of infectious diseases today are the threat of a global flu pandemic and the spread of antibiotic resistance. In an outbreak, earlier detection of influenza or any infectious disease activity could improve the ability of health agencies to respond appropriately.  Earlier detection of disease activity might trigger new or enhanced surveillance. One of the primary causes of antibiotic resistance is unnecessary antibiotic use, which happens because providers often have difficulty determining whether an infection is bacterial or viral in nature. A test to quickly distinguish between bacterial and viral causes of fever could help providers select the appropriate treatment.

Using data on test reimbursement from the Centers for Medicare and Medicaid Services (CMS), a team led by Rex Astles, PhD, coordinator at the CDC Division of Laboratory Systems in Atlanta, investigated whether it was possible to detect influenza outbreaks early. The team designed an algorithm using Medicare outpatient claims from 2007 to 2012 to calculate predicted weekly volumes for flu tests (V_test) and flu diagnoses (V_dx). Increases in predicted volume that surpassed a certain standard deviation and that were followed by a similar drop in volume were categorized as influenza episodes. The team compared the detected flu episodes with those identified using positive rapid flu test volumes from the CDC National Respiratory and Enteric Virus Surveillance System (NREVSS).

In preliminary results, across the ten representative states they examined, researchers detected a total of 67 influenza episodes using NREVSS data. The predictive models using V_test and V_dx data performed almost equally well, respectively catching 64 and 60 out of 67 NREVSS-confirmed episodes. Significantly, the team found that using this reimbursement data identified increases in influenza activity on average three weeks earlier than using NREVSS data.

“Our work showed that it is theoretically possible to use the simple fact that a test was ordered and performed as a means of detecting early respiratory disease activity,” Astles said, and cautioned that this approach could not replace, but might possibly augment, traditional influenza surveillance. Also, it has yet to be demonstrated using real-time Medicare data. He added: “We hope that this approach can be tried by other healthcare systems with large administrative data sets because it doesn’t require knowledge of the test results, allowing the use of various indicators for disease activity. This could be especially helpful for other diseases that have less effective surveillance systems. Comprehensive surveillance has many purposes, only one of which is detection of apparent disease activity. This detection could be a signal that more intensive surveillance, including testing patient specimens to identify the circulating viral or bacterial strain, should be initiated.”

In the second study, researchers led by Eran Eden, PhD, of MeMed Diagnostics in Tirat Carmel, Israel, evaluated the efficacy of their host-immune based ImmunoXpert assay in 233 pediatric patients who presented at the emergency department with fever. To classify an infection as bacterial or viral, the assay measured the serum levels of three protein biomarkers: TRAIL, IP-10, and C-reactive protein. These results were then compared to diagnoses made by a physician panel along with multiplex polymerase chain reaction (PCR).

Eden’s team found that ImmunoXpert correctly classified 90% of bacterial cases and 91% of viral cases compared to the expert and PCR diagnoses. The test’s accuracy could enable healthcare providers to quickly characterize pediatric infections and avoid inappropriate antibiotic use.

“We found that this test was superior to the traditional tests that are out there, and this was independently validated in a double blind manner,” Eden said. “We saw a consistent response and a high performance across a variety of viruses, indicating that this test could be useful in future epidemics. Our technology opens the application for many indications where it’s harder to apply more traditional microbiology tests.”

 Source: American Association for Clinical Chemistry (AACC)

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