Comparison of mass spectrometry and fourier transform infrared spectroscopy of plasma samples in identification of patients with fracture-related infections

Objectives: Fracture-related infections (FRIs) have significant impact on patient outcomes. Diagnosing FRIs is challenging due to lack of robust, minimally invasive diagnostic tests in the early stages of the disease. The objective of this study was to evaluate the ability of proteomic mass spectrometry (MS) (quantitative approach) and spectral pattern analysis based on fourier transform infrared (FTIR) spectroscopy of plasma samples (qualitative approach) in discriminating between FRI and controls. Materials and methods: A prospective case-control study at a level 1 trauma center was conducted. Patients meeting confirmatory FRI criteria were matched with controls without infection based on age, time after surgery, and fracture region. Plasma samples were collected at the time of presentation for FRI and saved for batch analysis. Tandem mass tag liquid chromatography-mass spectrometry was used for proteomics, and FTIR spectroscopy of dried films was used to obtain mid-infrared spectra from samples. Mid-infrared spectra were preprocessed, and for MS data, protein abundance ratios of FRI and controls were compared. Multivariate analysis-based predictive models were developed separately for FTIR-based spectra and MS-based protein ratio data. Results: Thirteen FRI and 13 controls were included in the study. The predictive models based on FTIR spectroscopy data had an average area under the receiver operating characteristic (AUROC) of ≈0.803, CI95(0.8, 0.81), the average sensitivity was ≈ 0. 0.755, CI95(0.75, 0.76), and the specificity was ≈ 0.677, CI95(0.672, 0.682). The MS-based predictive models from protein abundance ratio results had an average AUROC of ≈0.735, CI95(0.732, 0.737), the average sensitivity was ≈ 0.74, CI95 (0.739, 0.747), and the specificity was ≈ 0.653, CI95(0.649, 0.656). Discussion and conclusions: Mass spectrometry and spectral pattern recognition based on FTIR spectroscopy can both be used to develop predictive models that can discriminate between FRI and control samples. There is potential for both analytical approaches as candidate diagnostic biomarkers in FRI patients that require further validation in future studies.