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PubMed2026

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans↗
PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗
PubMed2026

Assessing the public health impact of routinely collected electronic healthcare record data in NICE guidelines: A systematic review of CPRD research.

OBJECTIVES: Evidence used in NICE guidance has traditionally prioritised randomised controlled trials, but increasing availability of electronic health record (EHR) data has expanded opportunities for real-world evidence. The Clinical Practice Research Datalink (CPRD) is a commonly used UK primary care EHR resource, yet the extent to which CPRD studies have informed NICE guidelines in the past decade is unclear. STUDY DESIGN: The systematic review was conducted in accordance with PRISMA guidelines. METHODS: We conducted a systematic review of CPRD studies in PubMed, MEDLINE, and Embase published between 04/16-09/25. For each eligible CPRD study, targeted searches of NICE guidelines were performed to identify explicit citations in NICE guidelines. Two reviewers screened and extracted data independently, resolving disagreements by consensus or third reviewer. Guideline information, number of guidelines over time, type of guidelines, and disease area guidelines (using British National Formulary (BNF) chapters) were described. RESULTS: 7181 records were identified. After de-duplication, 2704 unique CPRD studies were screened against NICE guidelines. Of these, 92 CPRD-based studies met inclusion criteria and were cited across 67 NICE documents. The annual number of NICE guidelines citing CPRD studies increased between 2016 and 2025; 1.5% of identified guidelines published in 2016 and 27.7% in 2025. The guideline citing the most CPRD studies was cancer related. The most common types of guidelines included clinical guidelines (49.3%) and technology appraisals (32.8%). Guidelines made up 12 different BNF categories, most frequently central nervous system related (23.9%; n = 16). CONCLUSION: Observational CPRD studies are increasingly referenced in NICE guidelines across multiple disease areas, supporting the growing role of EHR data in national guideline development.

Clinical studies↗
PubMed2026

Data-centric, robust, and explainable multimodal deep learning for clinical decision support: A systematic review.

PURPOSE: Multimodal deep learning is increasingly proposed for clinical decision support (CDS) under a "data-centric" framing that prioritizes label quality, missing-modality robustness, distribution shift, calibration, and explainability. Prior reviews have examined multimodal medical AI, CDS, and data-centric methods separately, but none address their intersection. We mapped the modalities, fusion strategies, and data-centric and explainability techniques used in this recent literature, quantified how often each is implemented rather than merely mentioned, assessed deployment-relevant evidence (external validation, clinical-outcome measurement, equity), and formally appraised study-level risk of bias. METHODS: Following the PRISMA 2020 statement (PROSPERO CRD420261427815; registered retrospectively), we screened 150 records and included primary, clinical, multimodal studies that applied machine or deep learning to a decision-support task and reported at least one quantitative result. Two reviewers screened and extracted data with consensus adjudication. Each study was coded against pre-specified operational definitions, separating implemented or empirically evaluated techniques from those only mentioned. Study-level risk of bias was assessed with PROBAST + AI. Synthesis was narrative. RESULTS: Thirty-one studies met inclusion; 30 (97%) were published between 2024 and 2026, with a median of three modalities (range 2-6), most commonly structured EHR (71%) and imaging (39%). Data-centric techniques were frequently reported (74-84% across label-noise, distribution-shift, calibration, missing-modality and class-imbalance handling; equity 61%). However, external validation was reported in only 4/31 studies (13%), a clinical or provider outcome in 3/31 (10%), and no study reported routine deployment. Overall risk of bias was high in 27/31 studies (87%), driven by the analysis domain. CONCLUSION: Within this recent, self-selected slice of the field, technical robustness and explainability techniques are widely reported but rarely validated out-of-distribution or against clinical outcomes, and the underlying evidence is at high risk of bias. Progress requires external multi-site validation, clinical-outcome measurement, formal bias appraisal, and adherence to AI reporting standards (e.g., TRIPOD + AI) before deployment can be justified.

Deep Learning↗
PubMed2026

Suicide in rural South Africa before and during COVID-19: evidence from forensic mortuary data in Limpopo Province.

Suicide is a growing public health concern in South Africa, with rural provinces such as Limpopo facing heightened vulnerability due to limited mental health services and socio-economic inequalities. Evidence on the impact of COVID-19 on suicide in rural contexts remains limited. This study examined suicide trends in Limpopo Province and changes associated with the COVID-19 period. A retrospective interrupted time series analysis was conducted using forensic mortality data from 1 January 2019 to 31 December 2021, allowing assessment of pre-existing trends and changes following COVID-19 lockdowns. Among 5770 unnatural deaths, 957 were suicides. The proportion of suicides increased from 29.5% in 2019 to 36.9% in 2021. Suicides predominantly occurred among males and young adults, with hanging accounting for over 90% of deaths throughout. Interrupted time series analysis revealed a significant downward trend in suicide cases during the strict national lockdown (Alert Level 5), with a 31% reduction in incidence (IRR = 0.69, 95% CI: 0.49-0.98). Less restrictive lockdown levels showed no significant effects. Suicide mortality increased prior to COVID-19, with a subsequent decline during the strictest lockdown period. Stable demographic patterns and methods highlight persistent vulnerabilities and the need for sustained suicide-prevention strategies beyond pandemic.

Humans↗
PubMed2026

Recovering membrane interaction kinetics of single molecules from 3D tracking data.

Interactions between cytosolic biomolecules and the bacterial inner membrane are fundamental to many cellular processes, yet directly measuring their binding kinetics in living cells remains challenging. Conventional 2D single-molecule tracking analyses can be insufficient, particularly when membrane association does not markedly alter the diffusion rate. Here, we present a method to recover membrane interaction kinetics from 3D single-molecule trajectories in rod-shaped bacteria. Using simulated 3D tracking data, we identify membrane-associated motion by quantifying how well short trajectory segments follow the circular curvature of the cell membrane. The resulting measure is further analyzed using a hidden Markov modeling framework, enabling robust discrimination between cytosolic and membrane-bound states and capturing the dynamics of state transitions without requiring diffusion-rate changes or direct colocalization with membrane markers. This work establishes a general framework for extracting membrane interaction kinetics from 3D single-molecule tracking data in live bacteria and highlights the value of realistic microscopy simulations for quantitative interpretation and systematic bias assessment.

Kinetics↗
PubMed2026

Effects of H-coil TMS on suicidality in major depression: A secondary analysis of data from a multisite randomized trial comparing accelerated to once-a-day stimulation.

Suicide is the 10th leading cause of death in US adults. Standard once-daily repetitive transcranial magnetic stimulation (rTMS) can reduce suicidal ideation. Yet, antidepressant and anti-suicidal effects often take several weeks to emerge, while rapid improvement is often required. Accelerated TMS has been proposed as a strategy to hasten therapeutic response. A recent FDA-regulated multicenter trial evaluated accelerated intermittent theta burst Deep TMS with the H1-coil versus standard high-frequency Deep TMS in MDD. Both groups demonstrated high remission and response rates for depression, with the accelerated protocol showing non-inferiority and a shorter time to remission. The goal of this exploratory secondary analysis was to evaluate the impact of these two H-coil TMS dosing paradigms on suicidal ideation. The Scale for Suicide Ideation (SSI), as well as suicidality items of HDRS, MADRS and CUDOS were collected and analyzed. On all scales, both accelerated and standard Deep TMS protocols were associated with meaningful reductions in suicidality. The accelerated protocol achieved a faster onset of improvement. Comparison between the timeline of improvement in suicidality and in overall depressive symptoms found a trend for faster improvement in suicidality, especially with the accelerated protocol. These findings highlight the importance of treatment frequency in determining time to clinical benefit and support the use of scalable accelerated protocols for patients requiring more rapid symptom relief.

Humans↗
PubMed2026

Building phenotypic character matrices for phylogenetic inference: exploration of 35 years of practice.

Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip-dating approaches, including fossil data, for inference of time-scaled trees and rates of evolution. However, attention has largely focused on the improvement of models of morphological evolution and other analytical tools with much less discussion about data acquisition itself. Here we review past and current practice for describing and collecting morphological data for phylogenetic inference. We present a systematic review of 164 phylogenetic analyses conducted over the last 35 years and focused on a diverse group of extinct arthropods: trilobites. Trends in increasing matrix size, data type, and coding strategy are evident. Where present, polymorphic characters have been predominantly derived from discretized continuous characters, although increasingly practitioners are utilizing alternative approaches for the treatment of quantitative characters. Not surprisingly, traditional indices that describe character consistency are highly correlated with matrix size but show surprising variation at different taxonomic scales. More recent attempts to describe data quality using information theory imply that characters can have high information content even if data are missing for many tips, providing support against the exclusion of characters because of missing data. In consideration of this, as well as advances in the study of developmental biology and variational complexity, we identify several avenues for increasing the quality and quantity of morphological data going forward.

Phylogeny↗
PubMed2026

Relationship Between Number of Acute Pancreatitis Episodes and Risk of New-onset Diabetes in the U.S.: A Real-world Data Analysis.

INTRODUCTION: Acute pancreatitis (AP) is a common inflammatory disorder that is associated with increased risk for diabetes mellitus (DM). It remains unclear whether recurrent acute pancreatitis (RAP) is associated with further increased risk of incident DM. This study aims to investigate the association between RAP and incident DM using real-world data. METHODS: We conducted a retrospective cohort study using the MerativeTM MarketScan&#xae; claims database (2016-2023), identifying patients with AP and no prior history of DM at baseline. The primary exposure of interest, RAP, was defined as one or more episodes of AP occurring &#x2265;90 days after the index AP diagnosis, whereas one episode of AP referred to a single episode of AP (SAP) with no subsequent recurrence within 90 days following the index event. A multivariable stratified Cox proportional hazards regression models were used to determine the association between RAP and incident DM, identified using ICD-10 codes. RESULTS: In total, 16,184 individuals with AP (mean [SD] age: 45.8 [12.3]) contributed 40,712 person-years of follow-up, during which 1,477 incident cases of DM were documented. Individuals with RAP had an increased risk of incident DM compared with those with a SAP(adjusted HR, 1.92; 95% CI, 1.61-2.29). The risk increased significantly with the frequency of RAP. In comparing the modifying effect of patient demographics and comorbidities, a stronger association between RAP and incident DM was observed in females (adjusted HR, 2.44; 95% CI, 1.87-3.19) than in males (adjusted HR, 1.64; 95% CI, 1.30-2.07; Pinteraction=0.03). Also, stronger associations were observed among younger patients (18-46&#xa0;y) (adjusted HR=2.56; 95% CI, 1.97-3.31) and among non-tobacco abuse (adjusted HR=2.19; 95% CI, 1.81-2.65), with significant interactions for all comparisons (Pinteraction<0.05. CONCLUSIONS: In this real-world study, RAP was associated with an increased risk of incident DM. Our findings highlight an opportunity for glycemic monitoring and proactive management of patients with RAP to mitigate their risk of developing DM.

AP↗
PubMed2026

Identification Matters: How Data Sharing Affects Pupil Honesty and Engagement in Universal School Well-Being Assessments.

PURPOSE: Universal well-being assessments in schools may support early identification of pupils needing mental health support. However, little is known about how privacy and confidentiality concerns influence pupils' acceptability of assessments and willingness to engage authentically. This study examined how hypothetical identification, where responses are linked to pupils and shared with key stakeholders, affects pupils' anticipated honesty and engagement, and whether known help-seeking barriers predict negative responses. METHODS: Cross-sectional data were collected from 12,377 primary (ages 8-10) and secondary pupils (ages 11-17) across 55 schools in England. Pupils reported whether their responses would change if identifiable and shared with school staff, parents/guardians, or external professionals. Responses indicating reduced honesty or likelihood of disengagement were coded as negative. Predictors were examined using mixed-effects logistic regression models, including demographics, school connectedness, and mental well-being. RESULTS: Identification and data sharing influenced pupils' anticipated engagement, particularly in secondary schools. Identification by school staff elicited the highest proportion of negative responses in both phases, whereas external professionals elicited the fewest. Most primary pupils reported they would respond authentically, while a larger proportion of secondary pupils indicated they would respond less honestly or disengage when responses were identifiable and shared. Across primary and secondary samples, low well-being, low school connectedness, and being female were associated with greater likelihood of negative response. DISCUSSION: Pupils' anticipated engagement with well-being assessments is shaped by who accesses their data, with marked developmental differences. Strengthening trust, privacy, and connectedness, and supporting pupils' autonomy, may improve the acceptability and response accuracy.

Humans↗
PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗
PubMed2026

List randomization for prevalence estimation of sensitive behavioral data among women with HIV of reproductive age in Lilongwe, Malawi.

Self-reported data are subject to reporting biases, including social desirability bias. List randomization is one method that can help mitigate the impact of such biases. Here, we examined the utility of list randomization among women of reproductive age living with HIV in sub-Saharan Africa. In the Family Planning and Antiretroviral Therapy study, participants were randomized to answer 5 blocks of true/false statements via either direct or list response. Each block contained 3 nonsensitive statements and 1 sensitive statement related to either condom use or HIV disclosure. For each sensitive statement, we calculated the prevalence difference (PD) comparing list response to direct response overall and stratified by socioeconomic status. The PD for 4 of the sensitive statements was negligible. However, we found that self-report of always using a condom was reported by 53.1% at list response visits vs 34.7% at direct response visits (PD, 18.5%; 95% CI, 6.2%-30.7%), a difference that was attenuated among those with higher socioeconomic status. In this setting, list randomization did not meaningfully change the estimated prevalence for most questions, except for one question, which unexpectedly produced a higher estimate for a positive behavior. Examining this method in other settings and populations is warranted.

Humans↗
PubMed2026

A mechanism-guided framework for prioritizing membrane-interaction anti-Vibrio peptides from peptidomics data.

A mechanism-guided framework for prioritizing membrane-interaction antimicrobial peptide candidates from proteomics-derived peptide mixtures is presented. The framework integrates conservative machine-learning-based antimicrobial peptide (AMP) screening with a literature-derived membrane-interaction plausibility (MAP) assessment and a data-driven membrane-interaction ranking function (AIPx), followed by structural visualization for interpretability. MAP encodes physicochemical characteristics commonly associated with peptide-membrane interaction and provides a graded plausibility assessment. Building upon this physicochemically interpretable framework, AIPx ranks peptides using feature weights calibrated from experimentally characterized anti-Vibrio peptides, where minimum inhibitory concentration (MIC) values are used as a coarse-grained ranking reference rather than a direct prediction target. In a peptidomics-based peptide fractionation study targeting Vibrio spp., AIPx exhibited a consistent relationship with experimentally observed antibacterial activity. Distributional analysis revealed that peptide fractions exhibiting high anti-Vibrio activity are characterized by enrichment of high-ranking peptides rather than by AMP abundance alone. By structuring AMP identification and prioritization as sequential stages, the MAP&#xa0;+&#xa0;AIPx framework enables interpretable and experimentally actionable candidate selection by reducing biologically implausible candidates. The framework facilitates species-oriented prioritization of AMP candidates, addressing a key challenge in antimicrobial peptide discovery where activity may depend on target-specific membrane characteristics. Moreover, the approach is extensible through species-specific calibration and supports interpretable, mechanism-informed prioritization in antimicrobial peptide discovery.

Proteomics↗
PubMed2026

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7&#xa0;&#xd7;&#xa0;108&#xa0;CFU/mL and a low detection limit of 1.66&#xa0;CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19%&#xa0;&#x223c;&#xa0;104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗
PubMed2026

Pembrolizumab-Chemotherapy Versus Pembrolizumab in Head and Neck Squamous Cell Carcinoma: A PD-L1 CPS-Stratified Analysis of Updated KEYNOTE-048 Data.

Based on KEYNOTE-048, pembrolizumab monotherapy and pembrolizumab-chemotherapy are established category 1 first-line treatments for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) with programmed death ligand-1 (PD-L1) combined positive score (CPS) &#x2265;&#x2009;1. We compared their efficacy using updated trial data. We analyzed 4-year progression-free survival on next-line therapy (PFS2) and 5-year overall survival (OS) data from KEYNOTE-048 by reconstructing time-to-event data using KMSubtraction. Efficacy was compared in CPS 1-19 and CPS &#x2265;&#x2009;20 subgroups using Kaplan-Meier estimates, Cox models, restricted mean survival time (RMST), and landmark analyses. Among 499 patients with CPS &#x2265;&#x2009;1, 240 (48.1%) had CPS 1-19 and 259 (51.9%) had CPS &#x2265;&#x2009;20. In the CPS 1-19 subgroup, pembrolizumab-chemotherapy showed numerically longer median PFS2 (10.1 vs. 8.0&#x2009;months; hazard ratio [HR]: 0.81; 95% confidence interval [CI]: 0.62-1.06) and OS (12.8 vs. 10.8&#x2009;months; HR: 0.87; 95% CI: 0.67-1.15) versus monotherapy, without statistical significance. For CPS &#x2265;&#x2009;20 patients, efficacy was comparable between regimens, with similar median PFS2 (11.3 vs. 11.7&#x2009;months; HR: 0.95) and OS (14.7 vs. 14.9&#x2009;months; HR: 0.96). RMST and landmark analyses showed an early PFS2 benefit and a trend toward OS benefit with pembrolizumab-chemotherapy in CPS 1-19, with comparable outcomes in CPS &#x2265;&#x2009;20. Pembrolizumab-chemotherapy showed a trend toward improved outcomes in the CPS 1-19 subgroup, with comparable efficacy in the CPS &#x2265;&#x2009;20 subgroup, supporting a refined first-line strategy: monotherapy for CPS &#x2265;&#x2009;20 to minimize toxicity, and combination therapy for CPS 1-19 to potentially enhance disease control.

Humans↗
PubMed2026

Genome-wide SNP data support species boundaries in sympatric Polylepis Ruiz & Pav. (Rosaceae) species from Bolivia and Ecuador.

Species delimitation in the South American genus Polylepis is notoriously challenging due to high morphological similarity and phenotypic plasticity, likely driven by hybridization and gene flow. Previous phylogenetic studies suggested that genetic structure aligns more strongly with geography than with taxonomy, questioning existing species concepts and hampering conservation efforts. We used double-digest RAD sequencing (ddRADseq) to generate genome-wide SNP data for 11 Polylepis species sampled across multiple localities in Bolivia and Ecuador. Population genetic analyses, phylogenetic inference, and network approaches were combined to assess whether genetic structure aligns more closely with taxonomy or geography. Morphologically defined species formed largely cohesive genetic lineages across regions, with species identity explaining substantially more genetic variation than locality. While localized admixture and reticulation were detected among closely related taxa, widespread species showed strong genetic cohesion and clear separation from congeners. Our results indicate that the sampled Polylepis species from Bolivia and Ecuador maintain distinct genetic identities despite localized signals consistent with gene flow. This genome-wide support for current taxonomy highlights Polylepis as a valuable model for studying speciation under gene flow and indicates that multiple geographic sampling will be essential in reconstructing a robust phylogeny of the genus, with important implications for conservation planning in Andean montane forests.

Bolivia↗
PubMed2026

Vancomycin Effectiveness in Reducing Surgical Site Infection in Posterior Spinal Fusion Surgery: A Retrospective Data Analysis of the STRIVE Trial.

STUDY DESIGN: Retrospective analysis of prospectively collected data. OBJECTIVE: To re-evaluate vancomycin as a preventive measure for surgical site infection (SSI). SUMMARY OF BACKGROUND DATA: Intrawound vancomycin powder is used to prevent SSIs in spinal surgery. Prior studies, often limited to single institutions or small samples, have shown mixed efficacy and potential increases in non- S. aureus and Gram-negative infections. We hypothesized that SSIs rates would be similar with and without intrawound vancomycin in posterior spinal fusion (PSF) surgery. METHODS: Prospectively collected data from the 3595 patients in the STaphylococcus aureus suRgical Inpatient Vaccine Efficacy (STRIVE) trial were stratified by intrawound antibiotic usage. Multivariate logistic regression assessed the effect of vancomycin use on SSI, adjusting for patient demographics and SSI-associated risk factors. Secondary outcomes included critical care stay, reoperation, sepsis, and hospital readmission. RESULTS: Of 3311 patients who underwent surgery, 847 (26%) received only intrawound vancomycin and 1534 (46%) received no intrawound antibiotics. Sixty (8%) patients developed postoperative SSI, of whom 20 (33%) had received intrawound vancomycin. Receiving intrawound vancomycin was not associated with SSI incidence versus no intrawound antibiotics [odds ratio (OR): 0.77; 95% CI: 0.42-1.42], critical care stay (OR: 0.94; 95% CI: 0.78-1.12), or sepsis (OR: 2.04; 95% CI: 0.62-6.73). However, intrawound vancomycin was associated with increased odds of hospital readmission (OR: 1.82; 95% CI: 1.28-2.6; P < 0.001) and reoperation (OR: 1.75; 95% CI: 1.18-2.6; P = 0.005). Factors significantly associated with intrawound vancomycin use included intraoperative antibiotic readministration (OR: 2.97; 95% CI: 1.36-6.5; P =0.006) and hospital location, lower odds in Europe (OR: 0.13; 95% CI: 0.06-0.29; P < 0.001) or Asia (OR: 0.02; 95% CI: 0-0.08; P < 0.001) versus North America. CONCLUSIONS: Intraoperative vancomycin use was not associated with reduced SSI incidence compared with no intrawound antibiotics after PSF surgery. LEVEL OF EVIDENCE: Level II.

Humans↗