John E Tidwell1, Ben Dawson-Andoh2, Emmanuel O Adedipe3, Kofi Nkansah4, Matthew J Dietz5. 1. Department of Orthopaedics, West Virginia University, PO Box 9196, Morgantown, WV, 26506-9196, USA. 2. Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV, USA. 3. Department of Horticultural Sciences, North Carolina State University, Raleigh, NC, USA. 4. Division of Resource Management, West Virginia University, Morgantown, WV, USA. 5. Department of Orthopaedics, West Virginia University, PO Box 9196, Morgantown, WV, 26506-9196, USA. mdietz@hsc.wvu.edu.
Abstract
BACKGROUND: Established bacterial diagnostic techniques for orthopaedic-related infections rely on a combination of imperfect tests that often can lead to negative culture results. Spectroscopy is a tool that potentially could aid in rapid detection and differentiation of bacteria in implant-associated infections. QUESTIONS/PURPOSES: We asked: (1) Can principal component analysis explain variation in spectral curves for biofilm obtained from Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa? (2) What is the accuracy of Fourier transformed-near infrared (FT-NIR)/multivariate data analysis in identifying the specific species associated with biofilm? METHODS: Three clinical isolates, S aureus, S epidermidis, and P aeruginosa were cultured to create biofilm on surgical grade stainless steel. At least 52 samples were analyzed per group using a FT-NIR spectrometer. Multivariate and principal component analyses were performed on the spectral data to allow for modeling and identification of the bacterial species. RESULTS: Spectral analysis was able to correctly identify 86% (37/43) of S aureus, 89% (16/18) of S epidermidis, and 70% (28/40) of P aeruginosa samples with minimal error. Overall, models developed using spectral data preprocessed using a combination of standard normal variant and first-derivative transformations performed much better than models developed with the raw spectral data in discriminating between the three classes of bacteria because of its low Type 1 error and large intermodel distinction. CONCLUSIONS: The use of spectroscopic methods to identify and classify bacterial biofilms on orthopaedic implant material is possible and improves with advanced modeling that can be obtained rapidly with little error. The sensitivity for identification was 97% for S aureus (95% CI, 88-99%), 100% for S epidermidis (95% CI, 95-100%), and 77% for P aeruginosa (95% CI, 65-86%). The specificity of the S aureus was 86% (95% CI, 3-93%), S epidermidis was 89% (95% CI, 67-97%), and P aeruginosa was 70% (95% CI, 55-82%). CLINICAL RELEVANCE: This technique of spectral data acquisition and advanced modeling should continue to be explored as a method for bacterial biofilm identification. A spectral databank of bacterial and potentially contaminating tissues should be acquired initially through an in vivo animal model and quickly transition to explanted devices and the clinical arena.
BACKGROUND: Established bacterial diagnostic techniques for orthopaedic-related infections rely on a combination of imperfect tests that often can lead to negative culture results. Spectroscopy is a tool that potentially could aid in rapid detection and differentiation of bacteria in implant-associated infections. QUESTIONS/PURPOSES: We asked: (1) Can principal component analysis explain variation in spectral curves for biofilm obtained from Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa? (2) What is the accuracy of Fourier transformed-near infrared (FT-NIR)/multivariate data analysis in identifying the specific species associated with biofilm? METHODS: Three clinical isolates, S aureus, S epidermidis, and P aeruginosa were cultured to create biofilm on surgical grade stainless steel. At least 52 samples were analyzed per group using a FT-NIR spectrometer. Multivariate and principal component analyses were performed on the spectral data to allow for modeling and identification of the bacterial species. RESULTS: Spectral analysis was able to correctly identify 86% (37/43) of S aureus, 89% (16/18) of S epidermidis, and 70% (28/40) of P aeruginosa samples with minimal error. Overall, models developed using spectral data preprocessed using a combination of standard normal variant and first-derivative transformations performed much better than models developed with the raw spectral data in discriminating between the three classes of bacteria because of its low Type 1 error and large intermodel distinction. CONCLUSIONS: The use of spectroscopic methods to identify and classify bacterial biofilms on orthopaedic implant material is possible and improves with advanced modeling that can be obtained rapidly with little error. The sensitivity for identification was 97% for S aureus (95% CI, 88-99%), 100% for S epidermidis (95% CI, 95-100%), and 77% for P aeruginosa (95% CI, 65-86%). The specificity of the S aureus was 86% (95% CI, 3-93%), S epidermidis was 89% (95% CI, 67-97%), and P aeruginosa was 70% (95% CI, 55-82%). CLINICAL RELEVANCE: This technique of spectral data acquisition and advanced modeling should continue to be explored as a method for bacterial biofilm identification. A spectral databank of bacterial and potentially contaminating tissues should be acquired initially through an in vivo animal model and quickly transition to explanted devices and the clinical arena.
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