Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood—while maintaining, if not improving—the current assay’s sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients.

Background: Multidrug-resistant bacteria are among the most urgent global public health threats. Rapid determination of antimicrobial resistance in a single bacterium is a major clinical unmet need in the diagnosis of bacterial infections. Methods: By capturing dynamic single-cell morphological features of over twenty-eight thousand cells, we evaluated strategies based on time and concentration differentials for classifying the susceptibility of Klebsiella pneumoniae to meropenem and predicting their minimum inhibitory concentrations (MIC). Findings: The classifiers achieved as high as 97% accuracy in 20 minutes (two-fifths of the doubling time) and reached over 99% accuracy within 50 minutes (one doubling time) in predicting the antimicrobial response. A regression model based on the concentration differential of individual cells predicted the MIC with >97% categorical agreement and 100% essential agreement. When tested against cells from an unseen strain, the regressor achieved a categorical agreement of 91.9% with a very major error of 0.1%. Interpretation: We report morphometric antimicrobial susceptibility testing (MorphoAST), an image-based machine learning workflow, for rapid determination of antimicrobial susceptibility by single-cell morphological analysis in a sub-doubling time. Our approach has the ability to predict bacterial antimicrobial responsiveness in a fraction of the organisms doubling time. This innovation will have significant implications for the future management of bacterial infections. Funding: This work was supported in part by NIH NIAID (R01AI153133).