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Multimodal imaging guides surgical management in a preclinical spinal implant infection model.

ABSTRACT: Spine implant infections portend disastrous outcomes, as diagnosis is challenging and surgical eradication is at odds with mechanical spinal stability. Current imaging modalities can detect anatomical alterations and anomalies but cannot differentiate between infection and aseptic loosening, diagnose specific pathogens, or delineate the extent of an infection. Herein, a fully human monoclonal antibody 1D9, recognizing the immunodominant staphylococcal antigen A on the surface of Staphylococcus aureus, was assessed as a nuclear and fluorescent imaging probe in a preclinical model of S. aureus spinal implant infection, utilizing bioluminescently labeled bacteria to confirm the specificity and sensitivity of this targeting. Postoperative mice were administered 1D9 probe dual labeled with 89-zirconium (89Zr) and a bars represent SEM dye (NIR680) (89Zr-NIR680-1D9), and PET-CT and in vivo fluorescence and bioluminescence imaging were performed. The 89Zr-NIR680-1D9 probe accurately diagnosed both acute and subacute implant infection and permitted fluorescent image-guided surgery for selective debridement of infected tissue. Therefore, a single probe could noninvasively diagnose an infection and facilitate image-guided surgery to improve the clinical management of implant infections.

SUBMITTER: Zoller SD

PROVIDER: S-EPMC6413782 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Multimodal imaging guides surgical management in a preclinical spinal implant infection model.

Zoller Stephen D SD Park Howard Y HY Olafsen Tove T Zamilpa Charles C Burke Zachary Dc ZD Blumstein Gideon G Sheppard William L WL Hamad Christopher D CD Hori Kellyn R KR Tseng Jen-Chieh JC Czupryna Julie J McMannus Craig C Lee Jason T JT Bispo Mafalda M Romero Pastrana Francisco F Raineri Elisa Jm EJ Miller Jeffery F JF Miller Lloyd S LS van Dijl Jan Maarten JM Francis Kevin P KP Bernthal Nicholas M NM

JCI insight 20190207 3

Spine implant infections portend disastrous outcomes, as diagnosis is challenging and surgical eradication is at odds with mechanical spinal stability. Current imaging modalities can detect anatomical alterations and anomalies but cannot differentiate between infection and aseptic loosening, diagnose specific pathogens, or delineate the extent of an infection. Herein, a fully human monoclonal antibody 1D9, recognizing the immunodominant staphylococcal antigen A on the surface of Staphylococcus a ...[more]

PMID: 30728332

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Project description:MINI: We compared the sensitivity and specificity of peri-implant tissue culture to the vortexing-sonication technique for the diagnosis of spinal implant infection (SII). Lower thresholds of sonicate fluid culture positivity showed increased sensitivity with maintained specificity. We recommend a threshold of 20 CFU/10 mL for sonicate culture positivity for the diagnosis of SII.Study designThis is a retrospective study comparing the diagnosis of spinal implant infection (SII) by peri-implant tissue culture to vortexing-sonication of retrieved spinal implants.ObjectiveWe hypothesized that vortexing-sonication would be more sensitive than peri-implant tissue culture.Summary of background dataWe previously showed implant vortexing-sonication followed by culture to be more sensitive than standard peri-implant tissue culture for diagnosing of SII. In this follow-up study, we analyzed the largest sample size available in the literature to compare these two culture methods and evaluated thresholds for positivity for sonicate fluid for SII diagnosis.MethodsWe compared peri-implant tissue culture to the vortexing-sonication technique which samples bacterial biofilm on the surface of retrieved spinal implants. We evaluated different thresholds for sonicate fluid positivity and assessed the sensitivity and specificity of the two culture methods for the diagnosis of SII.ResultsA total of 152 patients were studied. With more than 100 colony forming units (CFU)/10 mL as a threshold for sonicate fluid culture positivity, there were 46 patients with SII. The sensitivities of peri-implant tissue and sonicate fluid culture were 65.2% and 79.6%; the specificities were 88.7% and 93.4%, respectively. With more than 50 CFU/10 mL as a threshold, there were 50 patients with SII. The sensitivities of peri-implant tissue and sonicate fluid culture were 68.0% and 76.0%; the specificities were 92.2% for both methods. Finally, with more than or equal to 20 CFU/10 mL as a threshold, there were 52 patients with SII. The sensitivities of peri-implant tissue and sonicate fluid culture were 69.2% and 82.7%; the specificities were 94.0% and 92.0%, respectively.ConclusionImplant sonication followed by culture is a sensitive and specific method for the diagnosis of SII. Lower thresholds for defining sonicate fluid culture positivity allow for increased sensitivity with a minimal decrease in specificity, enhancing the clinical utility of implant sonication.Level of evidence4.

Project description:3D printed surgical guides are used for prosthetically-driven oral implant placement. When manufacturing these guides, information regarding suitable printing techniques and materials as well as the necessity for additional, non-printed stock parts such as metal sleeves is scarce. The aim of the investigation was to determine the accuracy of a surgical workflow for oral implant placement using guides manufactured by means of fused deposition modeling (FDM) from a biodegradable and sterilizable biopolymer filament. Furthermore, the potential benefit of metal sleeve inserts should be assessed. A surgical guide was designed for the installation of two implants in the region of the second premolar (SP) and second molar (SM) in a mandibular typodont model. For two additive manufacturing techniques (stereolithography [SLA]: reference group, FDM: observational group) n = 10 surgical guides, with (S) and without (NS) metal sleeves, were used. This resulted in 4 groups of 10 samples each (SLA-S/NS, FDM-S/NS). Target and real implant positions were superimposed and compared using a dedicated software. Sagittal, transversal, and vertical discrepancies at the level of the implant shoulder, apex and regarding the main axis were determined. MANOVA with posthoc Tukey tests were performed for statistical analyses. Placed implants showed sagittal and transversal discrepancies of <1 mm, vertical discrepancies of <0.6 mm, and axial deviations of ?3°. In the vertical dimension, no differences between the four groups were measured (p ? 0.054). In the sagittal dimension, SLA groups showed decreased deviations in the implant shoulder region compared to FDM (p ? 0.033), whereas no differences in the transversal dimension between the groups were measured (p ? 0.054). The use of metal sleeves did not affect axial, vertical, and sagittal accuracy, but resulted in increased transversal deviations (p = 0.001). Regarding accuracy, biopolymer-based surgical guides manufactured by means of FDM present similar accuracy than SLA. Cytotoxicity tests are necessary to confirm their biocompatibility in the oral environment.

Project description:In vivo and direct imaging of the murine spinal cord and its vasculature using multimodal (optical and acoustic) imaging techniques could significantly advance preclinical studies of the spinal cord. Such intrinsically high resolution and complementary imaging technologies could provide a powerful means of quantitatively monitoring changes in anatomy, structure, physiology and function of the living cord over time after traumatic injury, onset of disease, or therapeutic intervention. However, longitudinal in vivo imaging of the intact spinal cord in rodent models has been challenging, requiring repeated surgeries to expose the cord for imaging or sacrifice of animals at various time points for ex vivo tissue analysis. To address these limitations, we have developed an implantable spinal cord window chamber (SCWC) device and procedures in mice for repeated multimodal intravital microscopic imaging of the cord and its vasculature in situ. We present methodology for using our SCWC to achieve spatially co-registered optical-acoustic imaging performed serially for up to four weeks, without damaging the cord or induction of locomotor deficits in implanted animals. To demonstrate the feasibility, we used the SCWC model to study the response of the normal spinal cord vasculature to ionizing radiation over time using white light and fluorescence microscopy combined with optical coherence tomography (OCT) in vivo. In vivo power Doppler ultrasound and photoacoustics were used to directly visualize the cord and vascular structures and to measure hemoglobin oxygen saturation through the complete spinal cord, respectively. The model was also used for intravital imaging of spinal micrometastases resulting from primary brain tumor using fluorescence and bioluminescence imaging. Our SCWC model overcomes previous in vivo imaging challenges, and our data provide evidence of the broader utility of hybridized optical-acoustic imaging methods for obtaining multiparametric and rich imaging data sets, including over extended periods, for preclinical in vivo spinal cord research.

Project description:BACKGROUND:Surgical site infection (SSI) is one of the most common types of health care-associated infections. It increases mortality, prolongs hospital length of stay, and raises health care costs. Many institutions developed risk assessment models for SSI to help surgeons preoperatively identify high-risk patients and guide clinical intervention. However, most of these models had low accuracies. OBJECTIVE:We aimed to provide a solution in the form of an Artificial intelligence-based Multimodal Risk Assessment Model for Surgical site infection (AMRAMS) for inpatients undergoing operations, using routinely collected clinical data. We internally and externally validated the discriminations of the models, which combined various machine learning and natural language processing techniques, and compared them with the National Nosocomial Infections Surveillance (NNIS) risk index. METHODS:We retrieved inpatient records between January 1, 2014, and June 30, 2019, from the electronic medical record (EMR) system of Rui Jin Hospital, Luwan Branch, Shanghai, China. We used data from before July 1, 2018, as the development set for internal validation and the remaining data as the test set for external validation. We included patient demographics, preoperative lab results, and free-text preoperative notes as our features. We used word-embedding techniques to encode text information, and we trained the LASSO (least absolute shrinkage and selection operator) model, random forest model, gradient boosting decision tree (GBDT) model, convolutional neural network (CNN) model, and self-attention network model using the combined data. Surgeons manually scored the NNIS risk index values. RESULTS:For internal bootstrapping validation, CNN yielded the highest mean area under the receiver operating characteristic curve (AUROC) of 0.889 (95% CI 0.886-0.892), and the paired-sample t test revealed statistically significant advantages as compared with other models (P<.001). The self-attention network yielded the second-highest mean AUROC of 0.882 (95% CI 0.878-0.886), but the AUROC was only numerically higher than the AUROC of the third-best model, GBDT with text embeddings (mean AUROC 0.881, 95% CI 0.878-0.884, P=.47). The AUROCs of LASSO, random forest, and GBDT models using text embeddings were statistically higher than the AUROCs of models not using text embeddings (P<.001). For external validation, the self-attention network yielded the highest AUROC of 0.879. CNN was the second-best model (AUROC 0.878), and GBDT with text embeddings was the third-best model (AUROC 0.872). The NNIS risk index scored by surgeons had an AUROC of 0.651. CONCLUSIONS:Our AMRAMS based on EMR data and deep learning methods-CNN and self-attention network-had significant advantages in terms of accuracy compared with other conventional machine learning methods and the NNIS risk index. Moreover, the semantic embeddings of preoperative notes improved the model performance further. Our models could replace the NNIS risk index to provide personalized guidance for the preoperative intervention of SSIs. Through this case, we offered an easy-to-implement solution for building multimodal RAMs for other similar scenarios.

Dataset Information

Multimodal imaging guides surgical management in a preclinical spinal implant infection model.

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Multimodal imaging guides surgical management in a preclinical spinal implant infection model.

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