Rapidly predicting vancomycin resistance ofEnterococcus faeciumthrough MALDI-TOF MS spectrum obtained in real-world clinical microbiology laboratory

Abstract

50Enterococcus faecium is one of the leading pathogens in the world. In this study, we proposed 51 a strategy to rapidly and accurately distinguish vancomycin-resistant Enterococcus faecium 52 (VREfm) and vancomycin-susceptible E. faecium (VSEfm) to help doctors correctly determine 53 the use of vancomycin by a machine learning (ML)-based algorithm. A predictive model was 54 developed and validated to distinguish VREfm and VSEfm by analyzing MALDI-TOF MS 55 spectra of unique E. faecium isolates from different specimen types. Firstly, 5717 mass spectra, 56 including 2795 VREfm and 2922 VSEfm, were used to develop the algorithm. And 2280 mass 57 spectra of isolates, namely 1222 VREfm and 1058 VSEfm, were used to externally validate the 58 algorithm. The random forest-based algorithm demonstrated good classification performances 59 for overall specimens, whose mean AUROC in 5-fold cross validation, time-wise validation, 60 and external validation was all greater than 0.84. For the detection of VREfm in blood, sterile 61 body fluid, urinary tract, and wound, the AUROC in external validation was also greater than 62 0.84. The predictions with algorithms were significantly more accurate than empirical 63 antibiotic use. The accuracy of antibiotics administration could be improved by 30%. And the 64 algorithm could provide rapid antibiotic susceptibility results at least 24 hours ahead of routine 65 laboratory tests. The turn-around-time of antibiotic susceptibility could be reduced by 50%. In 66 conclusion, a ML algorithm using MALDI-TOF MS spectra obtained in routine workflow 67 accurately differentiated VREfm from VSEfm, especially in blood and sterile body fluid, which 68 can be applied to facilitate the clinical testing process due to its accuracy, generalizability, and 69 rapidness. 70 71 3 Introduction 72 Enterococcus spp. is one of the leading pathogens in healthcare-associated infection. 1 73 Enterococcal infection could cause urinary tract infection, blood stream infection, and even 74 mortality. 2 Until recently, vancomycin was virtually the only drug that could be consistently 75 relied on for treating multidrug-resistant enterococcal infections 3,4 . Vancomycin-resistant 76 Enterococcus (VRE) has led to heavy burden on healthcare worldwide since its first-time 77 isolation. 5,6 Enterococcus faecalis and E. faecium are the 2 most commonly isolated 78 Enterococcus spp. in clinical practice. 1 VRE faecium (VREfm) has received considerably more 79 attention than VRE faecalis (VREfs) because most of the clinically isolated VRE is E. faecium 80 in the recent decades 4,7 and VREfm causes more severe infection than VREfs 8,9 . Early detection 81 of vancomycin resistance is essential for successfully treating VREfm infection. 10 Vancomycin 82 could be discontinued, and antimicrobial agents could be replaced with other antibiotics (eg, 83 linezolid and daptomycin) based on the laboratory results of vancomycin resistance 11,12 . 84 Patients' prognosis could be improved and further drug resistance development ...

Improving Multi-Tumor Biomarker Health Check-Up Tests with Machine Learning Algorithms

“…We tried several ML algorithms and found that simple/linear algorithms perform well in classification problems within the medical field. We also found the same phenomenon in other classification studies [ 16 , 17 , 18 , 30 , 31 , 32 , 33 , 34 ]. We do not completely understand the reason behind this fact but hypothesize that the phenomenon of “simple/linear algorithms perform well” in the study could be attributed to the representativeness of the features (i.e., serum protein tumor markers).…”

Extensive validation and prospective observation of the impact of an AI-based rapid antibiotics susceptibility prediction platform in multiple medical centers

“…6,14 MALDI-TOF spectra-based AST for other species of ESKAPE, such as Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa, has also been reported recently. 14,15 In these studies, machine learning (ML) algorithms were adopted for the analysis of the complex data of MS spectra. 6,14,15 Meanwhile, large data were used for training and validating the ML models.…”

“…6,14,15 Meanwhile, large data were used for training and validating the ML models. 6,14,15 However, application of the MALDI-TOF spectra-based rapid AST has not yet been supported by adequate evidence from its implementation in real-world cases. 16 Individualized clinical ML models offer the promise of both reducing broad-spectrum antibiotic use and preserving/improving adequacy of treatment, but few have been validated in the clinical setting.…”

“…Thus, providing rapid AST from routine MALDI-TOF spectra is promising because it is potentially cost-effective and easy to integrate into current workflow of clinical microbiology laboratory. 6,14,15 Based on the approach, accurate prediction of oxacillin resistance for Staphylococcus aureus has been reported by two independent teams. 6,14 MALDI-TOF spectra-based AST for other species of ESKAPE, such as Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa, has also been reported recently.…”

“…14,15 In these studies, machine learning (ML) algorithms were adopted for the analysis of the complex data of MS spectra. 6,14,15 Meanwhile, large data were used for training and validating the ML models. 6,14,15 However, application of the MALDI-TOF spectra-based rapid AST has not yet been supported by adequate evidence from its implementation in real-world cases.…”