Implant healing in experimental animal models of diabetes.

Diabetes mellitus is becoming increasingly prevalent worldwide. Additionally, there is an increasing number of patients receiving implantable devices such as glucose sensors and orthopedic implants. Thus, it is likely that the number of diabetic patients receiving these devices will also increase. Even though implantable medical devices are considered biocompatible by the Food and Drug Administration, the adverse tissue healing that occurs adjacent to these foreign objects is a leading cause of their failure. This foreign body response leads to fibrosis, encapsulation of the device, and a reduction or cessation of device performance. A second adverse event is microbial infection of implanted devices, which can lead to persistent local and systemic infections and also exacerbates the fibrotic response. Nearly half of all nosocomial infections are associated with the presence of an indwelling medical device. Events associated with both the foreign body response and implant infection can necessitate device removal and may lead to amputation, which is associated with significant morbidity and cost. Diabetes mellitus is generally indicated as a risk factor for the infection of a variety of implants such as prosthetic joints, pacemakers, implantable cardioverter defibrillators, penile implants, and urinary catheters. Implant infection rates in diabetic patients vary depending upon the implant and the microorganism, however, for example, diabetes was found to be a significant variable associated with a nearly 7.2% infection rate for implantable cardioverter defibrillators by the microorganism Candida albicans. While research has elucidated many of the altered mechanisms of diabetic cutaneous wound healing, the internal healing adjacent to indwelling medical devices in a diabetic model has rarely been studied. Understanding this healing process is crucial to facilitating improved device design. The purpose of this article is to summarize the physiologic factors that influence wound healing and infection in diabetic patients, to review research concerning diabetes and biomedical implants and device infection, and to critically analyze which diabetic animal model might be advantageous for assessing internal healing adjacent to implanted devices.

Perioperative cerebrospinal fluid and plasma inflammatory markers after orthopedic surgery.

BACKGROUND: Postoperative delirium is prevalent in older patients and associated with worse outcomes. Recent data in animal studies demonstrate increases in inflammatory markers in plasma and cerebrospinal fluid (CSF) even after aseptic surgery, suggesting that inflammation of the central nervous system may be part of the pathogenesis of postoperative cognitive changes. We investigated the hypothesis that neuroinflammation was an important cause for postoperative delirium and cognitive dysfunction after major non-cardiac surgery. METHODS: After Institutional Review Board approval and informed consent, we recruited patients undergoing major knee surgery who received spinal anesthesia and femoral nerve block with intravenous sedation. All patients had an indwelling spinal catheter placed at the time of spinal anesthesia that was left in place for up to 24 h. Plasma and CSF samples were collected preoperatively and at 3, 6, and 18 h postoperatively. Cytokine levels were measured using ELISA and Luminex. Postoperative delirium was determined using the confusion assessment method, and cognitive dysfunction was measured using validated cognitive tests (word list, verbal fluency test, digit symbol test). RESULTS: Ten patients with complete datasets were included. One patient developed postoperative delirium, and six patients developed postoperative cognitive dysfunction. Postoperatively, at different time points, statistically significant changes compared to baseline were present in IL-5, IL-6, I-8, IL-10, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, IL-6/IL-10, and receptor for advanced glycation end products in plasma and in IFN-γ, IL-6, IL-8, IL-10, MCP-1, MIP-1α, MIP-1β, IL-8/IL-10, and TNF-α in CSF. CONCLUSIONS: Substantial pro- and anti-inflammatory activity in the central neural system after surgery was found. If confirmed by larger studies, persistent changes in cytokine levels may serve as biomarkers for novel clinical trials.

Variation in the type and frequency of postoperative invasive Staphylococcus aureus infections according to type of surgical procedure.

OBJECTIVE: To determine the epidemiological characteristics of postoperative invasive Staphylococcus aureus infection following 4 types of major surgical procedures.design. Retrospective cohort study. SETTING: Eleven hospitals (9 community hospitals and 2 tertiary care hospitals) in North Carolina and Virginia. PATIENTS: Adults undergoing orthopedic, neurosurgical, cardiothoracic, and plastic surgical procedures. METHODS: We used previously validated, prospectively collected surgical surveillance data for surgical site infection and microbiological data for bloodstream infection. The study period was 2003 through 2006. We defined invasive S. aureus infection as either nonsuperficial incisional surgical site infection or bloodstream infection. Nonparametric bootstrapping was used to generate 95% confidence intervals (CIs). P values were generated using the Pearson chi2 test, Student t test, or Wilcoxon rank-sum test, as appropriate. RESULTS: In total, 81,267 patients underwent 96,455 procedures during the study period. The overall incidence of invasive S. aureus infection was 0.47 infections per 100 procedures (95% CI, 0.43-0.52); 227 (51%) of 446 infections were due to methicillin-resistant S.aureus. Invasive S. aureus infection was more common after cardiothoracic procedures (incidence, 0.79 infections per 100 procedures [95%CI, 0.62-0.97]) than after orthopedic procedures (0.37 infections per 100 procedures [95% CI, 0.32-0.42]), neurosurgical procedures (0.62 infections per 100 procedures [95% CI, 0.53-0.72]), or plastic surgical procedures (0.32 infections per 100 procedures [95% CI, 0.17-0.47]) (P < .001). Similarly, S. aureus bloodstream infection was most common after cardiothoracic procedures (incidence, 0.57 infections per 100 procedures [95% CI, 0.43-0.72]; P < .001, compared with other procedure types), comprising almost three-quarters of the invasive S. aureus infections after these procedures. The highest rate of surgical site infection was observed after neurosurgical procedures (incidence, 0.50 infections per 100 procedures [95% CI, 0.42-0.59]; P < .001, compared with other procedure types), comprising 80% of invasive S.aureus infections after these procedures. CONCLUSION: The frequency and type of postoperative invasive S. aureus infection varied significantly across procedure types. The highest risk procedures, such as cardiothoracic procedures, should be targeted for ongoing preventative interventions.

Dosimetric Evaluation of Metal Artefact Reduction using Metal Artefact Reduction (MAR) Algorithm and Dual-energy Computed Tomography (CT) Method

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.