We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

Phenotypic Test Identifies Antibiotic-Resistant Bacterial Infections

By LabMedica International staff writers
Posted on 04 Dec 2018
Image: The BD Phoenix identification and susceptibility combo panels (Photo courtesy of Becton, Dickinson and Company).
Image: The BD Phoenix identification and susceptibility combo panels (Photo courtesy of Becton, Dickinson and Company).
Carbapenemase-producing organisms (CPO) are gram-negative bacteria that are resistant to the carbapenem class of antibiotics, which are commonly used for severe or high-risk bacterial infections. Mortality related to CPO infection is quite high around the world, with reported rates from 22% to 72%.

A CPO detect test, which offers results in less than 36 hours, is expected to replace time-consuming, manual detection that can take up to 96 hours for results. The CPO detection test will be offered as part of the BD Phoenix gram-negative panels, which already use susceptibility testing to expose bacteria from a specific patient's infection to a variety of potential treatments or candidates to gauge how they respond.

The BD Phoenix CPO detect test has gained US Food and Drug Administration clearance. The test may help hospitals contain the spread of antimicrobial resistance (AMR) by shortening the time it takes to detect CPOs, thereby enabling the earlier implementation of infection control procedures and the initiation of appropriate antibiotic therapies designed for treating these infections.

Kenneth Thomson, PhD, clinical professor of Pathology and Laboratory Medicine at the University of Louisville School of Medicine, said, “The BD Phoenix CPO detect test is a completely new type of phenotypic test, and its range of automation and diagnostic capabilities is unmatched by all other currently marketed tests. It represents a significant advance in meeting an important clinical need for rapid detection of CPOs.”

Steve Conly, vice president and general manager of Microbiology for BD Diagnostic Systems, said, “The BD Phoenix CPO detect test gives laboratories an accurate and cost-effective method to rapidly identify CPOs and support patient management. Along with the BD Phoenix M50 instrument, this automated first-to-market, phenotypic test to detect CPOs, using the BD Phoenix system, expands the BD portfolio of solutions for identification and antimicrobial susceptibility testing and is another example of the company's commitment to providing solutions that help addressing the global burden of antimicrobial resistance.”

Related Links:
University of Louisville School of Medicine

Platinum Member
Automated Coagulation Analyzer
Hemolumi H6
Gold Member
Blood-Based Protein Biomarker Solution for Alzheimer's Disease
BG-DTi2000.
New
Gastrointestinal Panel
Xpert® GI Panel
New
Drug Testing Assays
Atellica DT 250 Analyzer

Channels

Clinical Chemistry

view channel
Image: Tracking blood test trends alongside unexplained weight loss may help identify patients at increased cancer risk and support earlier investigation (Image Credit: 123RF)

Blood Test Patterns Improve Cancer Risk Assessment in Primary Care

Unexplained weight loss is a common but nonspecific presentation in primary care that can precede several types of cancer, making referral decisions difficult. Routine blood tests may produce borderline... Read more

Molecular Diagnostics

view channel
Image: The Avantect Pancreatic Cancer Test combines epigenomic, genomic, and glycan biomarkers with machine learning to detect pancreatic cancer-associated signals in blood (Photo courtesy of ClearNote Health)

Multiomic Blood Test Supports Noninvasive Monitoring and Subtyping in Pancreatic Cancer

Pancreatic ductal adenocarcinoma remains difficult to detect early and monitor during treatment, particularly in patients with homologous recombination defects. Clinicians also have limited noninvasive... Read more

Immunology

view channel
Image: Although many people harbor latent Epstein-Barr virus (EBV), growing evidence has linked the virus to MS pathobiology (Image Credit: Adobe Stock)

Blood EBV Activity Biomarkers May Predict Multiple Sclerosis Relapse Months Ahead

Predicting relapse in multiple sclerosis (MS) remains difficult, limiting opportunities for timely intervention and monitoring. Although many people harbor latent Epstein-Barr virus (EBV), growing evidence... Read more

Pathology

view channel
Image: The model combines digitized tumor histopathology, clinical variables, and a 42-gene molecular panel using AI to generate a unified recurrence risk prediction (Image Credit: Shutterstock)

Multimodal AI Improves Breast Cancer Recurrence Risk Prediction Beyond Standard Genomic Testing

Predicting which patients with early-stage breast cancer will develop distant recurrence remains difficult, complicating decisions about long-term therapy and surveillance. Widely used genomic assays are... Read more

Technology

view channel
Image: ADLM recommends that emerging AI tools follow the same professional oversight, quality, validation, and monitoring standards as traditional clinical testing within CLIA’s existing framework (Image Credit: Adobe Stock)

ADLM Calls for CLIA Updates to Support Safe AI Use in Laboratory Medicine

Clinical laboratories increasingly use artificial intelligence to verify, interpret, and report results, but safeguards under the Clinical Laboratory Improvement Amendments (CLIA) were designed in 1992.... Read more

Industry

view channel
Image Credit: Adobe Stock

Mayo Clinic and Thermo Fisher Launch Multi-Omics Venture to Identify Early Disease Signals

Many diseases begin developing years before symptoms emerge, making early detection difficult for healthcare systems and clinical laboratories. Linking molecular changes with longitudinal health data could... Read more