Comparative Evaluation of Sequencing Technologies for Detecting Antimicrobial Resistance in Bloodstream Infections

Bloodstream infections (BSIs) pose a significant global health challenge, particularly due to the increasing prevalence of antimicrobial resistance (AMR). Timely and accurate identification of pathogens and resistance determinants is critical for guiding appropriate therapy and improving patient outcomes. Traditional culture-based diagnostics are limited by prolonged turnaround times and reduced sensitivity, especially in culture-negative or polymicrobial infections. This review systematically examined current and emerging sequencing technologies for AMR detection in BSIs, including whole-genome sequencing (WGS), targeted next-generation sequencing (tNGS), metagenomic next-generation sequencing (mNGS), and long-read sequencing platforms (Oxford Nanopore, PacBio). We compared their clinical performance using key metrics such as diagnostic sensitivity, turnaround time, and cost, highlighting contexts in which each technology is most effective. For example, tNGS can achieve the rapid detection of known resistance genes within 8–24 h, while WGS provides comprehensive genome-wide resistance profiling over 24–48 h. mNGS offers broader detection, including rare or unexpected pathogens, although at higher cost and longer processing times. Our analysis identifies specific strengths and limitations of each approach, supporting the use of context-specific strategies, such as combining rapid targeted sequencing for common pathogens with broader metagenomic approaches for complex cases, to improve diagnostic yield and guide antimicrobial therapy. Quantitative comparisons indicate that sequencing technologies can complement conventional methods, particularly in cases where culture-based approaches fail. In conclusion, sequencing-based diagnostics offer measurable improvements in sensitivity and speed over traditional methods for AMR detection in BSIs. Future work should focus on optimizing workflows, integrating sequencing data into clinical decision-making, and validating approaches in prospective studies.