Yield of clinical metagenomics: insights from real-world practice for tissue infections

Abstract

Background: While metagenomic next-generation sequencing (mNGS) has been acknowledged as a valuable diagnostic tool for infections, its clinical validity and impact on patient management when using fresh tissue samples remains uncertain.

Methods: We conducted a retrospective cross-sectional study involving patients who underwent tissue mNGS at a tertiary hospital in China from February 2021 to February 2024, aiming to assess its ability to detect plausible pathogens and its clinical validity and impact.

Findings: A total of 520 mNGS results from 508 patients were analysed, detecting plausible pathogens in 302 (58.1%) tests, including 260 single-pathogen and 42 (13.9%) multi-pathogen results. Rare pathogens, such as Balamuthia mandrillaris, Bartonella henselae, and Sporothrix globosa, were identified. Of the multi-pathogen results, 22 were with predominance of anaerobes. mNGS showed higher positivity in cases with high initial suspicion of infection than those used for ruling out infection (PR 1.961, 95% CI: 1.604-2.394) and in patients living with HIV (PR 1.312, 95% CI: 1.047-1.643) or solid organ transplant recipients (PR 1.346, 95% CI: 1.103-1.643) compared to immunocompetent individuals. Sensitivity and specificity for diagnosing confirmed/probable infections were 85.0% (95% CI: 76.7%-93.3%) and 93.7% (95% CI: 86.8%-100.0%), respectively. mNGS influenced clinical management in 258 (49.6%) cases by identifying new infections and in 112 (21.5%) by excluding infections. It prompted initiation (20.2%), modification (23.1%), or discontinuation (6.3%) of antimicrobial therapy.

Interpretation: mNGS demonstrates high diagnostic accuracy for tissue infections. Its impact on clinical management highlights the need to integrate it into current diagnostic practices.

Funding: National Natural Science Foundation of China (No. 82472371), "Leading Geese" Research and Development Plan of Zhejiang Province (No. 2024C03218), and Pudong New Area Joint Project (PW2021D-09).

Keywords: Biopsy; Metagenomic; Next-generation sequencing; Tissue infection; mNGS.

Fig. 1

Flowchart of samples included in tissue mNGS analysis. This figure shows the sample inclusion criteria, diagnostic grouping, detection of plausible pathogens, and clinical validity of tissue mNGS results. In this figure, “Clinically relevant” means that, in the infection group (including both confirmed and probable infections), mNGS detected a plausible pathogen; or, in the non-infection group (unlikely infection), the mNGS result was negative. “Clinically irrelevant” means that, in the infection group (both confirmed and suspected infections), mNGS did not detect a plausible pathogen; or, in the non-infection group (unlikely infection), mNGS detected microorganisms (positive result, with potential for colonisation or contamination).

Fig. 2

Distribution of 520 fresh tissue samples for mNGS testing. (a) distribution of tissue lesion sites and tissue types. The numbers within the figure represent the number of tests; (b) detection rates of plausible pathogens by mNGS in different lesion sites; (c) detection rates of plausible pathogens by mNGS in different tissue samples.

Fig. 3

Detection of plausible pathogens using tissue mNGS testing. (a) proportions of single-pathogen results and multi-pathogen results; (b) pathogens in the 260 single-pathogen results, with the Arabic numerals in the figure representing the number of pathogens; (c) pathogens in the 22 multi-pathogen cases dominated by anaerobic bacteria. The lower x-axis shows patient age and sex, the upper x-axis indicates the sample types, the left y-axis lists the pathogen names, and the size of the circles on the right represents the total number of each pathogen.

Fig. 4

Factors affecting detection of plausible pathogen by mNGS. The association between various factors (age, sex, immune status, antibiotic use, and the indication for mNGS testing) and plausible pathogen detection was assessed using modified Poisson regression approach to calculate prevalence ratios (PRs). Only p-values less than 0.05 were considered statistically significant and are indicated by red dots. HIV, human immunodeficiency virus; HSCT, haematopoietic stem cell transplantation; PR, prevalence ratio; CI, confidence interval.

Fig. 5

Clinical impact of tissue mNGS on patients care. (a) impact of mNGS on diagnosis and treatment strategies; (b) diagnostic impact of mNGS and subsequent changes in treatment regimens in each group.

Fig. 6

Turnaround time (TAT) for 520 tissue mNGS tests from sample collection to result reporting. The red line in the figure represents the distribution of TAT for the 520 tests, with the median TAT being 23.5 h (indicated by the light red circles in the figure) and an interquartile range (IQR) of 20.8–26.3 h.