Application of metagenomic sequencing of drainage fluid in rapid and accurate diagnosis of postoperative intra-abdominal infection: a diagnostic study. International journal of surgery (London, England) BACKGROUND:Postoperative intra-abdominal infection (PIAI) is one of the most serious complications of abdominal surgery, increasing the risk of postoperative morbidity and mortality and prolonging hospital stay. Rapid diagnosis of PIAI is of great clinical value. Unfortunately, the current diagnostic methods of PIAI are not fast and accurate enough. METHODS:The authors performed an exploratory study to establish a rapid and accurate diagnostic method of PIAI. The authors explored the turnaround time and accuracy of metagenomic next-generation sequencing (mNGS) in diagnosing PIAI. Patients who underwent elective abdominal surgery and routine abdominal drainage with suspected PIAI were enroled in the study. The fresh midstream abdominal drainage fluid was collected for mNGS and culturing. RESULTS:The authors found that the median sample-to-answer turnaround time of mNGS was dramatically decreased than that of culture-based methods (<24 h vs. 59.5-111 h). The detection coverage of mNGS was much broader than culture-based methods. The authors found 26 species from 15 genera could only be detected by mNGS. The accuracy of mNGS was not inferior to culture-based methods in the 8 most common pathogens detected from abdominal drainage fluid (sensitivity ranged from 75 to 100%, specificity ranged from 83.3 to 100%, and kappa values were higher than 0.5). Moreover, the composition of the microbial spectrum established by mNGS varied between upper and lower gastrointestinal surgery, enhancing the understanding of PIAI pathogenesis. CONCLUSION:This study preliminarily revealed the clinical value of mNGS in the rapid diagnosis of PIAI and provided a rationale for further research. 10.1097/JS9.0000000000000500

Evaluation of metagenomic and pathogen-targeted next-generation sequencing for diagnosis of meningitis and encephalitis in adults: A multicenter prospective observational cohort study in China. The Journal of infection BACKGROUND:Next-generation sequencing (NGS) might aid in the identification of causal pathogens. However, the optimal approaches applied to cerebrospinal fluid (CSF) for detection are unclear, and studies evaluating the application of different NGS workflows for the diagnosis of intracranial infections are limited. METHODS:In this multicenter, prospective observational cohort study, we described the diagnostic efficacy of pathogen-targeted NGS (ptNGS) and metagenomic NGS (mNGS) compared to that of composite microbiologic assays, for infectious meningitis/encephalitis (M/E). RESULTS:In total, 152 patients diagnosed with clinically suspected M/E at four tertiary hospitals were enrolled; ptNGS and mNGS were used in parallel for pathogen detection in CSF. Among the 89 patients who were diagnosed with definite infectious M/E, 57 and 39 patients had causal microbial detection via ptNGS and mNGS, respectively. The overall accuracy of ptNGS was 65.1%, with a positive percent agreement (PPA) of 64% and a negative percent agreement (NPA) of 66.7%; and the overall accuracy of mNGS was 47.4%, with a PPA of 43.8% and an NPA of 52.4% after discrepancy analysis. There was a significant difference in the detection efficiency between these two methods both for PPA (sensitivity) and overall accuracy for pathogen detection (P < 0.05). CONCLUSIONS:NGS tests have provided new information in addition to conventional microbiologic tests. ptNGS seems to have superior performance over mNGS for common causative pathogen detection in CSF for infectious M/E. 10.1016/j.jinf.2024.106143

Metagenomic next-generation sequencing, instead of procalcitonin, could guide antibiotic usage in patients with febrile acute necrotizing pancreatitis: a multicenter, prospective cohort study. International journal of surgery (London, England) BACKGROUNDS:The effectiveness of procalcitonin-based algorithms in guiding antibiotic usage for febrile acute necrotizing pancreatitis (ANP) remains controversial. Metagenomic next-generation sequencing (mNGS) has been applied to diagnose infectious diseases. The authors aimed to evaluate the effectiveness of blood mNGS in guiding antibiotic stewardship for febrile ANP. MATERIALS AND METHODS:The prospective multicenter clinical trial was conducted at seven hospitals in China. Blood samples were collected during fever (T ≥38.5°C) from ANP patients. The effectiveness of blood mNGS, procalcitonin, and blood culture in diagnosing pancreatic infection was evaluated and compared. Additionally, the real-world utilization of antibiotics and the potential mNGS-guided antimicrobial strategy in febrile ANP were also analyzed. RESULTS:From May 2023 to October 2023, a total of 78 patients with febrile ANP were enrolled and 30 patients (38.5%) were confirmed infected pancreatic necrosis (IPN). Compared with procalcitonin and blood culture, mNGS showed a significantly higher sensitivity rate (86.7% vs. 56.7% vs. 26.7%, P <0.001). Moreover, mNGS outperformed procalcitonin (89.5 vs. 61.4%, P <0.01) and blood culture (89.5 vs. 69.0%, P <0.01) in terms of negative predictive value. Blood mNGS exhibited the highest accuracy (85.7%) in diagnosing IPN and sterile pancreatic necrosis, significantly superior to both procalcitonin (65.7%) and blood culture (61.4%). In the multivariate analysis, positive blood mNGS (OR=60.2, P <0.001) and lower fibrinogen level (OR=2.0, P <0.05) were identified as independent predictors associated with IPN, whereas procalcitonin was not associated with IPN, but with increased mortality (Odds ratio=11.7, P =0.006). Overall, the rate of correct use of antibiotics in the cohort was only 18.6% (13/70) and would be improved to 81.4% (57/70) if adjusted according to the mNGS results. CONCLUSION:Blood mNGS represents important progress in the early diagnosis of IPN, with particular importance in guiding antibiotic usage for patients with febrile ANP. 10.1097/JS9.0000000000001162

Novel Clinical mNGS-Based Machine Learning Model for Rapid Antimicrobial Susceptibility Testing of Acinetobacter baumannii. Journal of clinical microbiology Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data. The key genetic characteristics associated with antimicrobial resistance (AMR) were selected through a least absolute shrinkage and selection operator (LASSO) regression model based on 1,942 A. baumannii genomes. The mNGS-AST prediction model was accordingly established, validated, and optimized using read simulation sequences of clinical isolates. Clinical specimens were collected to evaluate the performance of the model retrospectively and prospectively. We identified 20, 31, 24, and 3 AMR signatures of A. baumannii for imipenem, ceftazidime, cefepime, and ciprofloxacin, respectively. Four mNGS-AST models had a positive predictive value (PPV) greater than 0.97 for 230 retrospective samples, with negative predictive values (NPVs) of 100% (imipenem), 86.67% (ceftazidime), 86.67% (cefepime), and 90.91% (ciprofloxacin). Our method classified antibacterial phenotypes with an accuracy of 97.65% for imipenem, 96.57% for ceftazidime, 97.64% for cefepime, and 98.36% for ciprofloxacin. The average reporting time of mNGS-based AST was 19.1 h, in contrast to the 63.3 h for culture-based AST, thus yielding a significant reduction of 44.3 h. mNGS-AST prediction results coincided 100% with the phenotypic AST results when testing 50 prospective samples. The mNGS-based model could be used as a rapid genotypic AST approach to identify A. baumannii and predict resistance and susceptibility to antibacterials and could be applicable to other pathogens and facilitate rational antimicrobial usage. 10.1128/jcm.01805-22

Metagenomics next-generation sequencing (mNGS) reveals emerging infection induced by Klebsiella pneumoniaeniae. International journal of antimicrobial agents OBJECTIVES:The increasing emergence of hypervirulent Klebsiella pneumoniae (hv-Kp) and carbapenem-resistant K. pneumoniae (CR-Kp) is a serious and substantial public health problem. The use of the last resort antimicrobials, tigecycline and polymyxin to combat infections is complicated by the expanding repertoire of newly-identified CR-hvKp. The transmission and co-occurrence of the corresponding antimicrobial resistance and virulence determinants are largely unknown. The aim of this study was to investigate the dissemination and dynamics of CR-Kp and its antibiotic resistance in a hospitalised patient. METHODS:Metagenomic next-generation sequencing (mNGS) was conducted for different specimens collected from an elderly male hospitalised patient. CR-Kp strains were examined using antibiotic susceptibility and string testing. Antimicrobial and virulence genes were annotated using whole-genome sequencing (WGS). RESULTS:A clinical case of a patient infected with a variety of CR-Kp isolates was reported. The co-occurrence of KPC-2 and NDM-1 in the patient was revealed. The CR-Kp isolates, such as BALF2, and Sputum T1 and T3, were classified into ST11 and ST147, respectively. The genetic signature (iuc operon) of hypervirulence was identified in strain T1, although string testing indicated its intermediate virulence. CONCLUSIONS:In this study, multiple infections of CR-Kp isolates were revealed by mNGS, and their dissemination was attributed to plasmid variations, mgrB inactivation and integrative conjugative elements (ICEs). Furthermore, the finding indicated one likely convergence to form CR-hvKp, different from acquisition of carbapenem-resistance determinants in hvKp. A combination of mNGS and WGS is beneficial for clinical diagnosis and anti-infection therapy, and facilitates a better understanding of genetic variants conferring antimicrobial and virulence properties. 10.1016/j.ijantimicag.2023.107056