Borgonovo, Fabio

Abstract Background Prosthetic joint infection (PJI) represents an important complication of total joint arthroplasty, affecting approximately 2% of patients. However, considerable heterogeneity in outcome definitions renders cross-study and clinical comparisons challenging. Methods We performed a meta-epidemiological study and systematic review of cohort studies and randomized controlled trial (RCTs) reporting clear definitions of PJI outcomes. Relevant data regarding outcome definitions of treatment success or failure were extracted and categorized into 7 outcome domains: clinical signs and symptoms, imaging, microbiology, mortality, antibiotics-related, surgical-related, and histology. A network plot was generated to visualize domains combinations. Data on patient-reported outcome measures (PROMs) were also collected. Based on these results, we finally proposed a working definition of PJI core outcome set. Results The literature search identified 7585 references, leading to 461 studies. The majority were retrospective (416/461, 90.2%), with limited representation of prospective studies (37/461, 8.0%) and RCTs (8/461, 1.7%). Outcome reporting was highly heterogeneous, with the clinical criterion being the most frequently used domain, combined with microbiological, surgical, and mortality-related outcomes. In contrast, histopathological, imaging, and antibiotic-related criteria were rarely reported. Thirty-six PROMs were included in only 111 (24.1%) studies, most commonly the Knee Society Score (26%) and Harris Hip Score (23%). Conclusions We showed substantial heterogeneity in PJI outcome definitions, with inconsistent reporting across key domains, limiting comparability and evidence synthesis. Standardized, consensus-based outcomes incorporating patient-reported and antibiotic-related measures are needed, and the proposed core outcome set may support harmonized research and clinical practice aligned with the new PJI definition.

Abstract Background The number of arthroplasty procedures and infection-related complications, including prosthetic joint infections (PJIs) and surgical site infections (SSIs), continues to rise. Although current guidelines recommend cefazolin or cefuroxime as the first-line perioperative prophylactic antibiotic, substitutions with non-cephalosporins remain common, especially among patients with reported β-lactam allergies. These substitutions may increase infection risk and healthcare costs. Evidence comparing outcomes across antibiotic classes remains variable. Methods We conducted a systematic review and meta-analysis of studies comparing single-agent cephalosporin versus single-agent non-cephalosporin prophylaxis in adults undergoing primary arthroplasty. Cochrane, Embase, Medline, Scopus, and Web of Science were searched from database inception through January 2025. Random-effect or fixed-effect models were used to estimate pooled odds ratios (OR) and 95% confidence intervals (CI). Subgroup analyses stratified results by cephalosporin type, study era, and risk of bias. Results Twenty-three studies (7 randomized controlled trials and 16 observational studies) were included, encompassing 191 527 arthroplasties in the cephalosporin arm and 20 058 in the non-cephalosporin arm. The odds of PJI were lower with cephalosporins (OR 0.59; 95% CI, .46–.75). No overall difference was observed for SSI (OR 0.70; 95% CI, .36–1.36), though post-2013 studies and the cefazolin subgroup demonstrated a significant protective effect. Randomized trial estimates were limited and heterogeneous and did not reach statistical significance. Certainty of evidence was graded as moderate. Conclusions Cephalosporins, particularly cefazolin or cefuroxime, remain the preferred prophylactic agents for primary arthroplasty. This quantitative synthesis reinforces their protective association against prosthetic joint infection and supports adherence to guideline-endorsed prophylaxis.

Abstract 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.

Background: Mosquito-borne viral diseases are a growing global health threat, and artificial intelligence (AI) and machine learning (ML) are increasingly proposed as forecasting tools to support early-warning and response. However, the available evidence is fragmented across pathogens, settings and modelling approaches. This review provides, to the best of our knowledge, the first comprehensive comparative assessment of AI/ML models forecasting mosquito-borne viral diseases in human populations, jointly synthesising predictive performance across model families and appraising both methodological quality and operational readiness. Methods: Following PRISMA 2020, we searched PubMed, Embase and Scopus up to August 2025. We included studies applying AI/ML or statistical models to predict arboviral incidence, outbreaks or temporal trends and reporting at least one quantitative performance metric. Given the substantial heterogeneity in outcomes, predictors and time–space scales, we conducted a descriptive synthesis. Risk of bias and applicability were evaluated using PROBAST. Results: Ninety-eight studies met the inclusion criteria, of which 91 focused on dengue. The forecasts spanned national to city-level settings and annual-to-weekly resolutions. Across classification tasks, tree-ensemble models showed the most consistent performance, with accuracies typically above 0.85, while classical ML and deep-learning models showed wider variability. For regression tasks, errors increased with temporal horizon and spatial aggregation: short-term, fine-scale forecasts (e.g., weekly city level) often achieved low absolute errors, whereas long-horizon national models frequently exhibited very large errors and unstable performance. PROBAST assessment indicated that most studies (63/98) were at high risk of bias, with only 24 judged at low risk and limited external validation. Conclusions: AI/ML models, especially tree-ensemble approaches, show strong potential for short-term, fine-scale forecasting, but their reliability drops substantially at broader spatial and temporal scales. Most remain research-stage, with limited external validation and minimal operational deployment. This review clarifies current capabilities and highlights three priorities for real-world use: standardised reporting, rigorous external validation, and context-specific calibration.