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Smartphone-based diagnostic test accurately detects antimicrobial resistance
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A smartphone attachment designed by researchers at the University of California, Los Angeles that is used to detect antimicrobial resistance exceeded FDA standards for susceptibility testing, according to study findings recently published in Scientific Reports.
Bacterial pathogens associated with high mortality are becoming increasingly resistant to antibiotics, representing a “severe threat to global health”, Omai Garner, PhD, assistant professor of pathology and laboratory medicine in Health Sciences at UCLA, and colleagues wrote. Part of the challenge in addressing this threat is overcoming barriers to routine antimicrobial susceptibility testing, which often is not performed because of technological difficulties, high costs and a lack of professional training.
To improve susceptibility testing, Garner and colleagues have developed a 3D-printed smartphone attachment that can be used in resource-limited settings as a simple and cost-effective method to detect antimicrobial resistance. The device uses an array of light-emitting-diodes (LEDs) to illuminate a 96-well microtiter plate (MTP) that contains various antibiotic doses. The phone’s camera detects changes in light transmission of each well. The images are then sent to a local or remote server to automatically test for antimicrobial susceptibility, with results returned to the phone in about 1 minute.
The prototype was designed to work on a Nokia Lumia smartphone; however, the researchers reported that with slight modifications, the platform can be used on iOS- and Android-based smartphones.
Garner and colleagues validated the accuracy of the device by testing 78 clinical isolates of Klebsiella pneumoniae with “highly-resistant antimicrobial profiles” using MTPs prepared with 17 antibiotics. They reported that the device met the FDA-defined criteria for antimicrobial susceptibility testing, with a well-turbidity detection accuracy of 98.1%, a minimum-inhibitory-concentration accuracy of 95.1%, and a drug-susceptibility interpretation accuracy of 98.2%. No “very major errors” were reported, and few “major errors” (0.16%), such as susceptible pathogens misdiagnosed as resistant, and “minor errors” (0.65%), including indeterminate results, occurred.
“This mobile reader could eliminate the need for trained diagnosticians to perform antimicrobial susceptibility testing, reduce the cost barrier for routine testing, and assist in tracking of bacterial resistance globally,” Garner said in a press release.
Dino Di Carlo, PhD, professor of bioengineering at UCLA, added that “an additional advantage of this technology is the possibility of examining bacterial growth in the presence of a drug at an earlier time point than is currently read (about 24 hours). This could allow for a more rapid turnaround time of the results to the physician, which might help save lives.” – by Stephanie Viguers
Disclosure: One study author is the cofounder of Cellmic LLC, which aims to commercialize computational microscopy and diagnostic tools.
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