The purpose of this method is to describe a novel mouse model of spinal implant infection (SII). This model was designed to provide an inexpensive and accurate tool to flexibly assess the effect of host, pathogen, and/or implant variables in vivo. Testing potential therapeutics and treatment strategies for spinal implant infections in this model is aimed at guiding research development prior to application in larger animal models and clinical trials.
Implant related infection after spine surgery is a devastating complication and unfortunately occurs in approximately 3–8% of patients undergoing elective spine surgery1,2,3,4,5 and up to 65% of patients undergoing multilevel or revision surgery6. Treatment of spinal implant infections often requires multiple hospitalizations, multiple surgeries, and prolonged antibiotic therapy. SIIs portend poor patient outcomes including neurological compromise, disability, and an increased risk of mortality. Management of SII is extremely expensive, costing upwards of $900,000 per patient7.
Staphylococcus aureus is the most common virulent pathogen of SII8,9,10,11. Bacteria can seed the hardware directly during surgery, through the wound during the postoperative period, or later via hematogenous spread. In the presence of metal implants, S. aureus form biofilm that protects the bacteria from antibiotic therapy and immune cells. While removal of infected hardware may help effectively eradicate an infection, this is frequently not feasible in the spine without causing destabilization and risking neurologic compromise12.
In the absence of explanting infected hardware, novel approaches are needed to prevent, detect, and treat SII. Historically, there have been limited animal models of SII to efficiently assess the safety and efficacy of novel therapies. Previous animal models of SII require large numbers of animals and collection of data points requiring euthanasia including colony counting, histology, and culture13,14,15. Lacking longitudinal in vivo monitoring, these models only provide one data point per animal and are therefore expensive and inefficient.
Previous work studying a mouse model of knee arthroplasty infection established the value and accuracy of noninvasive in vivo optical imaging to longitudinally monitor infection burden16. The detection of bioluminescence allows bacterial burden to be quantified over a longitudinal time course in a single animal humanely, accurately, and efficiently. Moreover, prior studies have demonstrated a high correlation between in vivo bioluminescence and CFUs adherent to implants17. The capacity to track infection over time, has led to a more nuanced understanding of implant related infection. In addition, monitoring longitudinal infection in this way, has allowed the effectiveness of antibiotic therapy and novel antimicrobials to be accurately assessed16,17,18.
Leveraging these tools, we developed and validated a model of postoperative spinal implant infection. In the method presented, we utilize an inoculum of bioluminescent S. aureus Xen36 to establish an in vivo mouse model of SII to longitudinally monitor bacterial burden16,17,18. This novel model provides a valuable tool to efficiently test potential detection, prevention, and treatment strategies for SII prior to their application in larger animal models and clinical trials.