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STUDY DESIGN - Prospective study.
OBJECTIVE - Assess the contamination rates of sterile microscope drapes after spine surgery.
SUMMARY OF BACKGROUND DATA - The use of the operating microscope has become more prevalent in certain spine procedures, providing superior magnification, visualization, and illumination of the operative field. However, it may represent an additional source of bacterial contamination and increase the risk of developing a postoperative infection.
METHODS - This study included 25 surgical spine cases performed by a single spine surgeon that required the use of the operative microscope. Sterile culture swabs were used to obtain samples from 7 defined locations on the microscope drape after its use during the operation. The undraped technician's console was sampled in each case as a positive control, and an additional 25 microscope drapes were swabbed immediately after they were applied to the microscope to obtain negative controls. Swab samples were assessed for bacterial growth on 5% sheep blood Columbia agar plates using a semiquantitative technique.
RESULTS - No growth was observed on any of the 25 negative control drapes. In contrast, 100% of preoperative and 96% of postoperative positive controls demonstrated obvious contamination. In the postoperative group, all 7 sites of evaluation were found to be contaminated with rates of 12% to 44%. Four of the 7 evaluated locations were found to have significant contamination rates compared with negative controls, including the shafts of the optic eyepieces on the main surgeon side (24%, P = 0.022), "forehead" portion on both the main surgeon (24%, P = 0.022) and assistant sides (28%, P = 0.010), and "overhead" portion of the drape (44%, P = 0.0002).
CONCLUSION - Bacterial contamination of the operative microscope was found to be significant after spine surgery. Contamination was more common around the optic eyepieces, likely due to inadvertent touching of unsterile portions. Similarly, all regions above the eyepieces also have a propensity for contamination because of unknown contact with unsterile parts of the surgeon. Therefore, we believe that changing gloves after making adjustments to the optic eyepieces and avoid handling any portion of the drape above the eyepieces may decrease the risks of intraoperative contamination and possibly postoperative infection as well.
The use of coregistered preoperative anatomical scans to provide navigational information in the operating room has greatly benefited the field of neurosurgery. Nonetheless, it has been widely acknowledged that significant errors between the operating field and the preoperative images are generated as surgery progresses. Quantification of tissue shift can be accomplished with volumetric intraoperative imaging; however, more functional, lower cost alternative solutions to this challenge are desirable. We are developing the strategy of exploiting a computational model driven by sparse data obtained from intraoperative ultrasound and cortical surface tracking to warp preoperative images to reflect the current state of the operating field. This paper presents an initial quantification of the predictive capability of the current model to computationally capture tissue deformation during retraction in the porcine brain. Performance validation is achieved through comparisons of displacement and pressure predictions to experimental measurements obtained from computed tomographic images and pressure sensor recordings. Group results are based upon a generalized set of boundary conditions for four subjects that, on average, account for at least 75% of tissue motion generated during interhemispheric retraction. Individualized boundary conditions can improve the degree of data-model match by 10% or more but warrant further study. Overall, the level of quantitative agreement achieved in these experiments is encouraging for updating preoperative images to reflect tissue deformation resulting from retraction, especially since model improvements are likely as a result of the intraoperative constraints that can be applied through sparse data collection.