Utility of Plasma Protein Biomarkers and Mid-infrared Spectroscopy for Diagnosing Fracture-related Infections: A Pilot Study

Future directions for early detection of fracture related infections

Introduction

Fracture related infection (FRI) refers to pathogens infecting a fracture site and hence impeding fracture healing. It is a significant complication that carries substantial disease burden and socio-economic costs, but has had limited scientific development. Hence, this paper will review the existing strategies for early detection of FRI, in the form of serum markers, molecular diagnostics and imaging modalities, and further discuss potential future directions for improved detection of FRI.

Existing Strategies for Diagnosis of FRI

The Anti-infection Global Expert Committee (AIGEC) developed a consensus definition for FRI in 2017, which includes confirmatory and suggestive criteria for diagnosis of FRI. Existing strategies for diagnosis include clinical, laboratory, histopathological, microbiological and radiological investigations.

Future Directions for Early Detection of FRI

With increasing recognition of FRI, early detection is crucial for early treatment to be enforced. We have identified potential areas for future development in diagnostics for early detection of FRI, which are discussed in this manuscript. They include inflammatory cytokines, serum calcium levels, platelet count, improved management of histopathological and microbiological specimens, metagenomics, wound biomarkers, gut microbiota analysis, and novel imaging technologies.

Proteomic Analyses of Plasma From Patients With Fracture-Related Infection Reveals Systemic Activation of the Complement and Coagulation Cascades

OBJECTIVES

The objective of this study was to compare plasma proteomes of patients with confirmed fracture-related infections (FRIs) matched to noninfected controls using liquid chromatography-mass spectrometry.

METHODS

DESIGN

This was a prospective case-control study.

SETTING

The study was conducted at a single, academic, Level 1 trauma center.

PATIENT SELECTION CRITERIA

Patients meeting confirmatory FRI criteria were matched to controls without infection based on fracture region, age, and time after surgery from June 2019 to January 2022. Tandem mass tag liquid chromatography-mass spectrometry analysis of patient plasma samples was performed.

OUTCOME MEASURES AND COMPARISONS

Protein abundance ratios in plasma for patients with FRI compared with those for matched controls without infection were calculated.

RESULTS

Twenty-seven patients meeting confirmatory FRI criteria were matched to 27 controls. Abundance ratios for more than 1000 proteins were measured in the 54 plasma samples. Seventy-three proteins were found to be increased or decreased in patients with FRI compared with those in matched controls (unadjusted t test P < 0.05). Thirty-two of these proteins were found in all 54 patient samples and underwent subsequent principal component analysis to reduce the dimensionality of the large proteomics dataset. A 3-component principal component analysis accounted for 45.7% of the variation in the dataset and had 88.9% specificity for the diagnosis of FRI. STRING protein-protein interaction network analysis of these 3 PCs revealed activation of the complement and coagulation cascades through the Reactome pathway database (false discovery rates <0.05).

CONCLUSIONS

Proteomic analyses of plasma from patients with FRI demonstrate systemic activation of the complement and coagulation cascades. Further investigation along these lines may help to better understand the systemic response to FRI and improve diagnostic strategies using proteomics.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Establishment and application of TSDPSO-SVM model combined with multi-dimensional feature fusion method in the identification of fracture-related infection

Fracture-related infection (FRI) is one of the most common and intractable complications in orthopedic trauma surgery. This complication can impose severe psychological burdens and socio-economic impacts on patients. Although the definition of FRI has been proposed recently by an expert group, the diagnostic criteria for FRI are not yet standardized. A total of 4761 FRI patients and 4761 fracture patients (Non-FRI) were included in the study. The feature set of patients included imaging characteristics, demographic information, clinical symptoms, microbiological findings, and serum inflammatory markers, which were reduced by the Principal Component Analysis. To optimize the Support Vector Machine (SVM) model, the Traction Switching Delay Particle Swarm Optimization (TSDPSO) algorithm, a recognition method was proposed. Moreover, five machine learning models, including TSDPSO-SVM, were employed to distinguish FRI from Non-FRI. The Area under the Curve of TSDPSO-SVM was 0.91, at least 5% higher than that of other models. Compared with the Random Forest, Backpropagation Neural Network (BP), SVM and eXtreme Gradient Boosting (XGBoost), TSDPSO-SVM demonstrated remarkable accuracy in the test set ([Formula: see text]). The recall of TSDPSO-SVM was 98.32%, indicating a significant improvement ([Formula: see text]). Compared with BP and SVM, TSDPSO-SVM exhibited significantly superior specificity, false positive rate and precision ([Formula: see text]. The five models yielded consistent results in the training and testing of FRI patients across different age groups. TSDPSO-SVM is validated to have the maximum overall prediction ability and can effectively distinguish between FRI and Non-FRI. For the early diagnosis of FRI, TSDPSO-SVM may provide a reference basis for clinicians, especially those with insufficient experience. These results also lay a foundation for the intelligent diagnosis of FRI. Furthermore, these findings exhibit the application potential of this model in the diagnosis and classification of other diseases.

The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results

INTRODUCTION

Obstructive sleep apnea (OSA) is frequently associated with a chronic inflammatory state and cardiovascular/metabolic complications. The aim of this study was to evaluate the influence of certain comorbidities on a panel of 45 chemokines and cytokines in OSA patients with special regard to their possible association with cardiovascular diseases.

MATERIAL AND METHODS

This cross-sectional study was performed on 61 newly diagnosed OSA patients. For the measurement of the plasma concentration of chemokines and cytokines, the magnetic bead-based multiplex assay for the Luminex^® platform was used.

RESULTS

In the patients with concomitant COPD, there were increased levels of pro-inflammatory cytokines (CCL11, CD-40 ligand) and decreased anti-inflammatory cytokine (IL-10), while in diabetes, there were increased levels of pro-inflammatory cytokines (IL-6, TRIAL). Obesity was associated with increased levels of both pro-inflammatory (IL-13) and anti-inflammatory (IL-1RA) cytokines. Hypertension was associated with increased levels of both pro-inflammatory (CCL3) and anti-inflammatory (IL-10) cytokines. Increased daytime pCO₂, low mean nocturnal SaO₂, and the oxygen desaturation index were associated with increased levels of pro-inflammatory cytokines (CXCL1, PDGF-AB, TNF-α, and IL-15).

CONCLUSIONS

In OSA patients with concomitant diabetes and COPD, elevated levels of certain pro-inflammatory and decreased levels of certain anti-inflammatory cytokines may favor the persistence of a chronic inflammatory state with further consequences. Nocturnal hypoxemia, frequent episodes of desaturation, and increased daytime pCO₂ are factors contributing to the chronic inflammatory state in OSA patients.