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Frontiers in Microbiology· 2026Q1

Metagenomic next-generation sequencing improves pathogen detection in culture-negative infections: a diagnostic performance study

Hamsa T. Tayeb, Mohamed Abouelhoda, Abdulmohsen alqasabi, Amna I. Magrashi et al.

Short summary

Metagenomic next-generation sequencing (mNGS) identified pathogens in 45.3% of samples compared to 16.3% with conventional microbiology, demonstrating significantly higher detection rates (p=0.0015) in culture-negative infections.

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Key points

  • mNGS detected pathogens in 45.3% of samples, versus 16.3% for conventional microbiology.
  • mNGS demonstrated a sensitivity of 57.1% and specificity of 84.2%.
  • There was poor agreement (kappa=0.20) between mNGS and conventional methods.
  • Immunocompromised status (OR 2.8) and positive imaging (OR 3.1) were associated with mNGS positivity.

AI-generated from the title and abstract; the full text is not read.

Abstract

The conventional microbiological diagnosis is frequently insensitive, time consuming and targeted. Patients with underlying immune suppression and those who have recently been on antibiotics often have culture-negative infections. In recent years, metagenomic next-generation sequencing (mNGS) has emerged as a novel, pathogen-agnostic diagnostic approach. A total of 136 clinical specimens were submitted for metagenomic next-generation sequencing (mNGS) between August 2022 and January 2024. Of these samples, 86 from 73 patients were included in this retrospective study. Thirteen patients had more than one specimen included in the study. All specimens were processed using conventional microbiological methods and analyzed using mNGS technology, either by using Illumina next-generation sequencing technology or Oxford Nanopore long-read technology. The diagnostic performance characteristics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated for both methods. The agreement between the two methods was evaluated using Cohen’s kappa coefficient. The paired comparisons were made using the McNemar test. The areas under the ROC curves for both methods were calculated, and the multivariable logistic regression analysis was used to identify the variables that were independently associated with mNGS positivity. Here, conventional microbiology identified pathogens in 16.3% of samples, whereas mNGS identified pathogens in 45.3% of samples. The sensitivity and specificity of mNGS were 57.1 and 84.2%, respectively. By McNemar testing, there was significant detection of pathogens by mNGS ( p = 0.0015). The agreement between two methods was poor (kappa = 0.20). The ROC curve for mNGS results showed moderate diagnostic accuracy with an AUC of 0.71. By multivariable logistic regression, immunocompromised status (OR 2.8, 95% CI 1.3–6.0, p = 0.018) and positive imaging findings (OR 3.1, 95% CI 1.4–6.9, p = 0.010) were independently associated with mNGS positivity. mNGS is able to identify more pathogens in culture-negative cases than conventional methods. There is scope for mNGS to be used in a complementary fashion, whilst relying on conventional methods as the primary tool for diagnosis of infections. Notably, the greatest additional information will come from the use of mNGS in immunocompromised patients.

The authors' abstract, as published at the source. Frontiers in Microbiology, 2026 · DOI ↗

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Field: Clinical Biochemistry

Clinical BiochemistryBiochemistry, Genetics and Molecular Biology