Next Generation Sequencing in orthopaedic infections - Where is the road headed?

Abstract

Next-generation sequencing (NGS) has emerged as a game changer in the field of orthopaedic diagnostics, notably in the detection and management of infections associated with prosthetic joints and implants. This paper conducts an exhaustive examination of the pivotal role, outcomes, and prospective future uses of NGS in diagnosing orthopaedic infections. In comparison to conventional culture-based methods, NGS offers a marked improvement in sensitivity thereby facilitating prompt and comprehensive identification of pathogens. This encompasses the ability to detect polymicrobial infections, antibiotic-resistant strains, and previously imperceptible microorganisms. Furthermore, this article delves into the technology's contribution to advancing personalized medicine and promoting judicious antibiotic use. Nonetheless, the seamless integration of NGS into routine clinical practice is impeded by challenges such as substantial financial outlays, the requisite for specialized equipment and expertise, and the intricacy associated with data analysis. Notwithstanding these impediments, the potential for NGS to revolutionize orthopaedic diagnostics remains substantial, with ongoing advancements poised to address current limitations and broaden its scope within clinical applications.

Keywords: Antibiotic resistance; Bioinformatics; Molecular diagnostics; Next generation sequencing; Orthopaedic infections; Prosthetic joint infections.

Fig. 1

Workflow of Next Generation Sequencing in identification of organism responsible for infection. Created with Biorender.com.

Fig. 2

A learning health system for infectious diseases. Next-generation sequencing (NGS) technologies now permit routine genomic analysis of clinical microbiology specimens. When integrated with pathogen phenotypes derived from clinical metadata in electronic medical records (EMRs) and laboratory metadata, we can generate predictive models for pathogen transmission, outbreaks, drug resistance, virulence, and risk factors for infection or critical outcomes that are specific to the health system and its patient population. If management strategies are formulated from these predictions and sent to infectious disease (ID) physicians and hospital infection control, a continuous loop of data analysis, application, and model refinement is created. This image is an excerpt from publication by Pak et al. and its use is permitted under the Creative Commons Attribution 4.0 license.