The incidence of culture-negative NVO (CN-NVO) cases is increasing, presenting significant diagnostic and therapeutic challenges due to the inability to isolate causative organisms with conventional microbiological methods. Factors influencing the diagnosis of CN-NVO include prior antimicrobial therapy, low pathogen burden, fastidious or intracellular organisms, technical issues, and non-infectious mimickers. Diagnosis often relies on imaging modalities like magnetic resonance imaging (MRI) and computed tomography (CT)-guided biopsy, though these methods can sometimes fail to yield positive microbiological results. Advanced diagnostic tools, such as polymerase chain reaction (PCR), metagenomic next-generation sequencing (mNGS), and cell-free DNA analysis, may be necessary to identify the pathogen. The causative pathogen cannot be isolated in some patients, among which an empirical antimicrobial therapy should be initiated. This narrative review discusses the management, monitoring, surgical indications, and outcomes for patients with CN-NVO.

Recent advances in shotgun metagenomic sequencing (sMGS) for detecting microbial cell-free DNA (mcfDNA) in peripheral blood have shown promise across various patient populations. This study evaluates the application of sMGS for diagnosing osteoarticular infections (OAIs), a condition with significant diagnostic challenges. We conducted a retrospective analysis on 73 patients suspected of OAIs at the Mayo Clinic from 2019 to 2023, incorporating mcfDNA sMGS (Karius test [KT]) into their diagnostic evaluation. We categorized the clinical impact of KT on OAI diagnoses and management into 4 distinct outcomes. (1) KT was able to confirm an established diagnosis, (2) KT supported noninfectious diseases diagnosis, (3) KT established an unsuspected diagnosis, (4) KT did not add relevant information. In our cohort, KT was performed in 73 patients. Among the infected individuals, KT yielded positive results in 22 of 43 (51.2%) cases. Of these 22 cases, 11 (50%) showed agreement with conventional diagnostic workup, whereas in 5 (22.7%) cases, the KT established an unsuspected diagnosis. Native vertebral osteomyelitis diagnosis (P < .001) or OAIs with concomitant presence of endocarditis or endovascular infection (P = .005) were statistically associated with a definite, probable, or possible diagnostic certainty of KT result. In complex OAIs, KT enhanced diagnostic accuracy by 11.6%, proving especially beneficial in diagnosing native vertebral osteomyelitis and infections with concurrent endocarditis or endovascular complications. Our findings underscore the utility of KT in the diagnostic workflow for challenging OAI cases, potentially altering clinical management for a significant subset of patients.

Dalbavancin is a long-acting lipoglycopeptide that is registered for the treatment of acute bacterial skin and skin structure infections, and it is also increasingly used for infections that require prolonged antibiotic treatment. Here, we present the results from the first 2 years of a service set up in December 2021 for the therapeutic drug monitoring (TDM) of dalbavancin in clinical settings. In particular, we compared the trough concentration (Cmin) to maximum concentration (Cmax) in patients with osteoarticular infections receiving prolonged treatment with dalbavancin. Log-linear regression models were used to estimate the timing of dalbavancin administration with the goal of maintaining Cmin concentrations of >8 mg/L in the two TDM-based strategies. From December 2021 to November 2023, 366 TDMs of dalbavancin from 81 patients were performed. The Cmin and Cmax concentrations of dalbavancin ranged from 4.1 to 70.5 mg/L and from 74.9 to 995.6 mg/L, respectively. With log-linear regression models, we estimated that each injection should be administered every 42–48 days to maintain the Cmin concentrations. Out of the 81 patients, 37 received at least three doses of dalbavancin for the treatment of osteoarticular infections. Despite there being no significant differences in the days of dalbavancin treatment (130 ± 97 versus 106 ± 102 days), the patients in the Cmax-based TDM group received a significantly lower number of dalbavancin injections (5.2 ± 1.8 versus 7.3 ± 2.6 injections, p = 0.005), and they were administered over a longer period of time (40 ± 10 versus 29 ± 14 days, p = 0.013) than in the Cmin-based TDM group. In conclusion, Cmax-based TDM was associated with a significant reduction in the inter-individual variability of dalbavancin concentrations and lower drug dosing frequency than those of Cmin-based TDM. This approach could, therefore, favor a more rational and targeted use of dalbavancin in patients requiring prolonged treatment.

In recent years, there has been a notable increase in research output on native vertebral osteomyelitis (NVO), coinciding with a rise in its incidence. However, clinical outcomes remain poor, due to frequent relapse and long-term sequelae. Additionally, the lack of a standardized definition and the use of various synonyms to describe this condition further complicate the clinical understanding and management of NVO. We propose a new framework to integrate the primary diagnostic tools at our disposal. These collectively fall into three main domains: clinical, radiological, and direct evidence. Moreover, they and can be divided into seven main categories: (a) clinical features, (b) inflammatory biomarkers, (c) imaging techniques, microbiologic evidence from (d) blood cultures and (e) invasive techniques, (f) histopathology, and (g) empirical evidence of improvement following the initiation of antimicrobial therapy. We provide a review on the evolution of these techniques, explaining why no single method is intrinsically sufficient to formulate an NVO diagnosis. Therefore, we argue for a consensus-driven, multi-domain approach to establish a comprehensive and universally accepted definition of NVO to enhance research comparability, reproducibility, and epidemiological tracking. Ongoing research effort is needed to refine these criteria further, emphasizing collaboration among experts through a Delphi method to achieve a standardized definition. This effort aims to streamline research, expedite accurate diagnoses, optimize diagnostic tools, and guide patient care effectively.

Background: Native vertebral osteomyelitis (NVO) is a life-threatening spinal infection with rising incidence and significant morbidity. Despite its growing burden, long-term data on clinical characteristics, management trends, and outcomes remain limited. Methods: We conducted a 26-year multicenter retrospective cohort study of adults (≥18 years) diagnosed with NVO at Mayo Clinic sites between 1999 and 2024. Demographic, microbiologic, treatment, and outcome data were analyzed across five time periods. Predictors of treatment failure were assessed using a multivariable competing risk model. Results: Among 1255 patients (median age 67; 66% male), lumbosacral involvement was most common (65%), and 21% had multilevel involvement. Pathogens were identified in 77%, most commonly Staphylococcus aureus (49%; Methicillin-susceptible S. aureus 37%, methicillin-resistant S. aureus 13%). Over time from 1999–2004 to 2020–2024, Gram-negative bacilli increased from 6% to 14% (P = .048). Comorbidities including chronic kidney disease (10% to 21%), active chemotherapy (6% to 11%), and immunosuppression (8% to 17%) increased significantly. Additionally, 1-year treatment failure declined (16% to 10%). In multivariable analysis, diabetes mellitus (subdistribution hazard ratio [sHR] 1.92, 95% CI 1.18–3.13) and multilevel involvement (sHR 1.67, 95% CI 1.17–2.38) were associated with increased incidence of treatment failure, while concurrent infections (sHR 0.57, 95% CI 0.37–0.87) and higher Charlson Comorbidity Index (sHR 0.62, 95% CI 0.43–0.90) were associated with lower failure. Conclusions: This large multicenter cohort highlights increasing host complexity, shifting microbiology, and predictors of failure, emphasizing the importance of early risk stratification and tailored strategies, such as multidisciplinary evaluation and close follow-up of high-risk patients to improve outcomes.

Surgical site infections (SSIs) are a significant complication following spinal surgery. These infections contribute to increased morbidity, prolonged hospital stays, and substantial healthcare costs. Traditional statistical models have been widely used to predict SSI risk, but artificial intelligence (AI) and its machine learning (ML) methods have also been used for SSI prediction. This systematic review aims to evaluate the predictive accuracy of AI models versus traditional statistical models in assessing SSI risk following spinal surgery. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched Medline, Embase, Scopus, Web of Science, and ClinicalTrials.gov. Studies were included if they developed predictive models for SSI following spinal surgery using either AI or traditional statistical approaches. Risk of Bias for all studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Predictive model performance was compared using metrics such as the C-statistic and Area under the Receiver Operating Characteristic curve (AUC-ROC). A total of 51 studies were included. Among these, 42 studies used traditional statistical methods, while 9 used AI / ML models. Logistic regression was the most common method among traditional models (95.2%). Across the ML studies, all of which used supervised models trained on tabular data, decision‑tree-based and linear algorithms (n=7, 77.8% each) were the most common, followed by neural networks and support vector machines (n=4, 44.4% each). Traditional models achieved a C-statistic between 0.7 and 0.8 in 40.5% of cases (n=17), with only 4.8% (n=2) exceeding 0.9. AI models showed a C-statistic of 0.9 or higher in 44.4% of cases (n=4). However, 77.8% of those ML-based models (n=7) performed an internal cross validation and only 33.3% reported calibration data (n=3), and none of the studies are externally validated, which raises important concerns about their current clinical applicability and generalizability. This systematic review, the first of its kind, observed that studies utilizing the AI models reported a potential for excellent classification accuracy in predicting SSI following spinal surgery. However, the current shortcomings in methodology limit their generalizability and immediate clinical implementation. For existing models, most AI studies remain in the early stages of development and its findings in excellent performance should be taken with caution. This review highlights the need for standardized model benchmarking and employing external validation to reliably assess generalizability. Furthermore, advancing beyond conventional tabular data by incorporating state-of-the art AI models that leverage multimodal data could significantly expand the potential of predictive analytics in this domain - thus help guide clinical decision making.