Identification of pathogen composition in a Chinese population with iatrogenic and native vertebral osteomyelitis by using mNGS

Abstract

Background

Early antimicrobial therapy is crucial regarding the prognosis of vertebral osteomyelitis, but early pathogen diagnosis remains challenging.

Objective

In this study, we aimed to differentiate the types of pathogens in iatrogenic vertebral osteomyelitis (IVO) and native vertebral osteomyelitis (NVO) to guide early antibiotic treatment.

Methods

A total of 145 patients, who had confirmed spinal infection and underwent metagenomic next-generation sequencing (mNGS) testing, were included, with 114 in the NVO group and 31 in the IVO group. Using mNGS, we detected and classified 53 pathogens in the 31 patients in the IVO group and 169 pathogens in the 114 patients in the NVO group. To further distinguish IVO from NVO, we employed machine learning algorithms to select serum biomarkers and developed a nomogram model.

Results

The results revealed that the proportion of the Actinobacteria phylum in the NVO group was approximately 28.40%, which was significantly higher than the 15.09% in the IVO group. Conversely, the proportion of the Firmicutes phylum (39.62%) in the IVO group was markedly increased compared to the 21.30% in the NVO group. Further genus-level classification demonstrated that Staphylococcus was the most common pathogen in the IVO group, whereas Mycobacterium was predominant in the NVO group. Through LASSO regression and random forest algorithms, we identified 5 serum biomarkers including percentage of basophils (BASO%), percentage of monocytes (Mono%), platelet volume (PCT), globulin (G), activated partial thromboplastin time (APTT) for distinguishing IVO from NVO. Based on these biomarkers, we established a nomogram model capable of accurately discriminating between the two conditions.

Conclusion

The results of this study hold promise in providing valuable guidance to clinical practitioners for the differential diagnosis and early antimicrobial treatment of vertebral osteomyelitis.

Keywords:

• Iatrogenic vertebral osteomyelitis
• vertebral osteomyelitis
• spinal infection
• mNGS

Acknowledgements

We are grateful for the help and support provided by the Department of Spine Surgery, Xiangya Hospital of Central South University. We also need to thank the Big Data Center of Central South University for its help and support.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Xiangya Hospital, Central South University (Ethics Review Number: 201303232). We provided a comprehensive overview of the study’s objectives and procedures to all enrolled participants and their families and obtained written informed consent from them.

Authors contributions

HX and GQ played crucial roles in this study, as they jointly performed the tasks of data analysis and paper writing. Simultaneously, the other authors made valuable contributions in data collection and case discussions. LQ, ZG, LY, LY, TM, LS, and ZH were responsible for gathering relevant data and participated in the discussion and analysis of cases. All authors reviewed the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Consent for publication

All authors agree to publish this paper.

Data availability and materials statement

The raw data underlying this article can be obtained from the supplementary data or the corresponding author.

Additional information

Funding

The study was supported by National Natural Science Foundation of China (No. 82072460); Natural Science Foundation of Hunan Province (No. 2023JJ30878).