ArticleAnnals of medicine2024
Identification of pathogen composition in a Chinese population with iatrogenic and native vertebral osteomyelitis by using mNGS.
Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 5 citations in OpenAlex.
- Recommendations of the International Consensus Meeting (ICM) 2025 on Spinal Infections: Part A Pyogenic Spondylodiscitis.Global spine journal · 2026Article
- [A clinical auxiliary differential diagnostic model for thoracolumbar spinal tuberculosis based on routine laboratory indicators: A multicenter retrospective cohort study].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- From serum inflammatory markers to fluid, tissue, and molecular assays: current advances in the laboratory diagnosis of bone and joint infections.Frontiers in cellular and infection microbiology · 2026Review
- Article
- Incremental value of quantitative SPECT/CT integrated parameters in a multimodal machine-learning model for differentiating spinal tuberculosis from pyogenic spondylitis.Frontiers in cellular and infection microbiology · 2026Article
- Transforming long-term post-acute care for the aging population through home infusion therapy in China: an assessment of need and demand (Part 1).Frontiers in public health · 2026Review
- Charcot Spinal Arthropathy Secondary to Spinal Cord Injury - A Case Report.Journal of orthopaedic case reports · 2025Article
- Multiple skin abscesses due to Nocardia neocaledoniensis: a case report and literature review.BMC infectious diseases · 2024Review
- Culture-Negative Native Vertebral Osteomyelitis: A Narrative Review of an Underdescribed Condition.Journal of clinical medicine · 2024Review
- A transformer-based deep learning model for identifying the occurrence of acute hematogenous osteomyelitis and predicting blood culture results.Frontiers in microbiology · 2024Article
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Authors and funding
9 authors at 1 institution in 1 country.
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Abstract
backgroundEarly antimicrobial therapy is crucial regarding the prognosis of vertebral osteomyelitis, but early pathogen diagnosis remains challenging.
objectiveIn 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.
methodsA 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.
resultsThe 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.
conclusionThe results of this study hold promise in providing valuable guidance to clinical practitioners for the differential diagnosis and early antimicrobial treatment of vertebral osteomyelitis.
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