Current Pharmaceutical BiotechnologyCurrent Pharmaceutical BiotechnologyCurrent Pharmaceutical Biotechnology1389-20101873-4316Bentham Science64530810.2174/0113892010246350231030042340BiotechnologyResearch ArticleDetermination of Ideal Factors for Early Adoption and Standardization of Metagenomic Next-generation Sequencing for Respiratory System InfectionsZhaoLeiinfo@benthamscience.netFormslagColeinfo@benthamscience.netZhangQinginfo@benthamscience.netCowanBraydoninfo@benthamscience.netMayberryTrentoninfo@benthamscience.netBarnhillAaroninfo@benthamscience.netWangYongshenginfo@benthamscience.netFangYujianginfo@benthamscience.netThe Department of Respiratory Medicine, the 2nd People’s Hospital of Hefei and Hefei Hospital Affiliated to Anhui Medical UniversityDepartment of Microbiology, Immunology & Pathology, Des Moines UniversityDepartment of Surgery, University of Missouri School of Medicine0109202425172266227707012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://journals.eco-vector.com/1389-2010/article/view/645308

Background:Metagenomic next-generation sequencing (mNGS) demonstrates great promise as a diagnostic tool for determining the cause of pathogenic infections. The standard diagnostic procedures (SDP) include smears and cultures and are typically viewed as less sensitive and more time-consuming when compared to mNGS. There are concerns about the logistics and ease of transition from SDP to mNGS. mNGS lacks standardization of collection processes, databases, and sequencing. Additionally, there is the burden of training clinicians on interpreting mNGS results.

Objective:Until now, few studies have explored factors that could be used as early adoption candidates to ease the transition between SDP and mNGS. This study evaluated 123 patients who had received both SDP and mNGS and compared several variables across a diagnostic test evaluation.

Methods:The diagnostic test evaluation observed metrics such as sensitivity, specificity, positive and negative likelihood ratios (PLR, NLR), positive and negative predictive values (PPV, NPV), and accuracy. Factors included various sample sources such as bronchoalveolar lavage fluid (BALF), lung tissue, and cerebral spinal fluid (CSF). An additional factor observed was the patient's immune status.

Results:Pathogen detection was found to be significantly greater for mNGS for total patients, BALF sample source, CSF sample source, and non-immunocompromised patients (p(<0.05). Pathogen detection was found to be insignificant for lung tissue sample sources and immunocompromised patients. Sensitivity, PLR, NLR, PPV, NPV, and accuracy appeared to be higher with mNGS for the total patients, BALF sample source, and non-immunocompromised patients when compared with SDP (p(<0.05).

Conclusion:With higher metrics in sensitivity, specificity, PLR, NLR, PPV, NPV, and accuracy for overall patients, mNGS may prove a better diagnostic tool than SDP. When addressing sample sources, mNGS for BALF-collected samples appeared to have higher scores than SDP for the same metrics. When patients were in a non-immunocompromised state, mNGS also demonstrated greater diagnostic benefits to BALF and overall patients compared to SDP. This study demonstrates that using BALF as a sample source and selecting non-immunocompromised patients may prove beneficial as early adoption factors for mNGS standard protocol. Such a study may pave the road for mNGS as a routine clinical method for determining the exact pathogenic etiology of lung infections.

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