The diagnostic value of metagenomic next-generation sequencing versus traditional microbiological testing in native pyogenic spinal infections: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Native pyogenic spinal infections (PSIs), including spondylodiscitis and vertebral osteomyelitis, are challenging to diagnose due to low culture sensitivity and delayed results. Metagenomic next-generation sequencing (mNGS) has emerged as a promising diagnostic tool, but its comparative clinical utility remains uncertain. The purpose of this study is to systematically compare the diagnostic performance and clinical impact of mNGS versus conventional microbial culture in detecting pathogens responsible for native PSIs. Methods: The current systematic review and meta-analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed across 6 major databases. Eligible studies directly compared mNGS with standard culture for native PSIs and reported diagnostic performance metrics. Data were extracted and analyzed using a random-effects model to produce pooled estimates. Study quality was assessed using the Newcastle-Ottawa Scale. Primary outcomes included pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Secondary outcomes assessed diagnostic yield, time to diagnosis, treatment modification, and false-positive or contamination events. Results: A total of 1,227 patients from 12 studies were included, encompassing those with suspected or confirmed native PSIs. Pooled sensitivity and specificity of mNGS were 89.7% (95% CI: 85.6-93.1%) and 86.2% (95% CI: 80.5-91.0%), respectively. mNGS demonstrated a significantly higher diagnostic yield (69-90%) compared to culture (27.2-44.7%) and enabled faster diagnosis (range, 17.7-48 hours). mNGS informed antimicrobial selection in up to 70.3% of cases and detected a broader pathogen spectrum. The incidence of false positives was low (range, 1-5) but non-negligible, emphasizing the need for careful interpretation. Conclusions: mNGS outperforms conventional culture in sensitivity, speed, and breadth of pathogen detection in native PSIs and supports more tailored antimicrobial therapy. However, careful interpretation is necessary due to potential false positives. These findings support the integration of mNGS into clinical workflows, particularly in complex or culture-negative infections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".