Advancing Diagnostics to Address Antibacterial Resistance: The Diagnostics and Devices Committee of the Antibacterial Resistance Leadership Group

Abstract

Diagnostics are a cornerstone of the practice of infectious diseases. However, various limitations frequently lead to unmet clinical needs. In most other domains, diagnostics focus on narrowly defined questions, provide readily interpretable answers, and use true gold standards for development. In contrast, infectious diseases diagnostics must contend with scores of potential pathogens, dozens of clinical syndromes, emerging pathogens, rapid evolution of existing pathogens and their associated resistance mechanisms, and the absence of gold standards in many situations. In spite of these challenges, the importance and value of diagnostics cannot be underestimated. Therefore, the Antibacterial Resistance Leadership Group has identified diagnostics as 1 of 4 major areas of emphasis. Herein, we provide an overview of that development, highlighting several examples where innovation in study design, content, and execution is advancing the field of infectious diseases diagnostics.

Keywords: antibacterial resistance; devices.; diagnostics.

Figure 1.

Overview of the development process for a host diagnostic biomarker. Beginning with a population that is dichotomized by susceptibility, diagnosis, or prognosis, biological samples are acquired. Omic measurements are run on these samples, which generates large quantities of data. Dimension reduction and statistical analyses generate a classifier or signature that distinguishes the desired characteristic from the original population. The classifier is then validated against a different population to test its generalizability. (Used with permission from Yang WE, et al. Host-based diagnostics for detection and prognosis of infectious diseases. In: Sails A, Tang YW, eds. Methods in Microbiology. Elsevier Ltd, 2015;42[13]:465–500.).

Figure 2.

Validation of the RADICAL (Rapid Diagnostics in Categorizing Acute Lung Infections) host response signature. A cohort of 273 patients encompassing bacterial acute respiratory infection (ARI) (black circles), viral ARI (dark gray circles), or noninfectious fever (white circles) was used to develop classifiers of each condition. Using leave-one-out cross-validation, 3 probabilities were determined for each patient: that of having a bacterial ARI, viral ARI, or noninfectious illness. The highest probability determined class assignment. Patients classified as having bacterial ARI appear to the left, viral ARI at the top, and noninfectious illness to the right. Closer proximity to the vertex indicates a higher probability of that condition. Overall accuracy was 87%.