Background: A recent survey of in-hospital reprocessing in Tanzanian hospitals identified bag-valve masks (BVM) as a commonly reused single-use device. In low-and middle-income countries (LMIC), in-hospital reprocessing supports neonatal resuscitation strategies by helping to maintain adequate supplies of BVM. However, there is a need for device-specific protocols defining reprocessing procedures and inspection criteria to overcome variations in reprocessing practices between hospitals. The purposes of this study were: 1) to complete a comprehensive design review and identify challenges to reprocessing BVMs; and 2) to investigate three different residual bioburden analysis methods for assessing the efficacy of decontaminating a disposable BVM. Methods: New, unused bag-valve-masks were contaminated with Staphylococcus epidermidis and Artificial Mucus Soil to simulate the worst case soiling conditions. Devices underwent one of five disinfection protocols, including one currently used in a LMIC hospital. Three analytical (two quantitative and one qualitative) methods were selected to evaluate residual bioburden on the device following decontamination. Results: Of all protocols tested, only the positive control and the Soap and Bleach protocols met disinfection targets. Most cleaning outcomes were consistent from trial to trial for each protocol. However, cleaning outcomes varied greatly for the Alcohol Wipe protocol. For the residual bioburden analyses, the two quantitative methods produced similar results, but the qualitative measurement exhibited increased variability. Conclusion: While this study revealed positive disinfection outcomes for the Tanzanian hospital decontamination protocol, more studies are required to support these findings. Design features of the BVM mask presented challenges to cleaning and drying during different decontamination protocols, as seen in the variability in the Alcohol Wipe protocol performance. These

Cadaveric testing is a common approach for verifying mathematical algorithms in computational modeling. In models of total knee replacement (TKR) motion, model inputs include rigid body motions defined using the Grood-Suntay spatial linkage between tibial and femoral components. This approach requires definition of coordinate systems for each rigid TKR component based on fiducial points, manual digitization of a point cloud within the experimental setup, and registration of the orientation relative to bone marker arrays. This study compared variability between two different manual point digitization methods (hand-held stylus and pivot tool each registered in an optical tracking system). This was accomplished by verifying the mathematical algorithm used to calculate the coordinate system from digitized points and quantifying the variability of the digitization methods in a simulated cadaver limb experimental setup. For the hand-held stylus method, the standard deviation was 0.50mm for the origin and 1.31, 0.51, and 0.62 degrees for the x-y-z axes, respectively. Required digitization of each rigid marker array created additional errors of 0.54mm for the origin and 1.70, 1.66, and 0.98 degrees for the x-y-z axes, respectively. For the pivot tool method, the standard deviation was 0.35mm for the origin and 0.37, 1.27, and 1.24 degrees for the x-y-z axes, respectively. In this experimental setup, the pivot tool was the better option for minimizing error while providing for repeatable manual digitization of fiducial points and point clouds.

Cadaveric testing is a common approach for verifying mathematical models used in computational modeling work. In the case of a knee joint model for calculating ligament tension during total knee replacement (TKR) motion, model inputs include rigid body motions defined using the Grood-Suntay coordinate system as a spatial linkage between the tibial component orientation relative to the femoral component. Using this approach requires the definition of coordinate systems for each rigid TKR component (i.e. tibial and femoral) based on fiducial points, manual digitization of a point cloud within the experimental setup, and registration of the orientation relative to the relevant bone marker array. The purpose of this study was to compare the variability between two different manual point digitization methods (a hand-held stylus and pivot tool each calibrated in the optical tracking system), using a TKR femoral component in a simulated cadaver limb experimental setup as an example. This was accomplished by verifying the mathematical algorithm used to calculate the coordinate system from the digitized points, quantifying the variability of the manual digitization methods, and discussing how any error could affect the computational model. For the hand-held stylus method, the standard deviation of the origin and, x-, y-, and z-axis calculations were 0.50mm, 1.31 degrees, 0.51 degrees, and 0.62 degrees, respectively. It is important to note that there is an additional error created using the hand-held stylus from required manual digitization of each rigid marker array. This average additional error was 0.54mm for the origin and 1.70, 1.66, and 0.98 degrees for the x-, y-, and z-axes, respectively. For the pivot tool method, the standard deviation was 0.35mm, 0.37 degrees, 1.27 degrees, and 1.24 degrees for the origin, x-, y-, and z-axes, respectively. It is essential to minimize experimental error, as small errors in alignment can substantially alter model outputs. In this study of cadaver simulation of limb motion, the pivot tool is the better option for minimizing error. Careful definition of fiducial points and repeatable manual digitization of the point cloud is critical for meaningful computational models of TKR motion based on cadaver experimental work.

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