Assessing dosing errors in legacy and low dose tip ENFit® syringes.

WHAT IS KNOWN AND OBJECTIVE: To avoid misconnections between different medical devices, a unique standardized design of connectors (ENFit® ) for enteral medical devices has been developed. It was expected that the syringes with these connectors will replace the pre-existing syringes, henceforth referred to as legacy syringes. However, the changes in the connector's design led to concerns regarding dosing errors for low volume syringes (≤2 ml). Therefore, novel low dose tip (LDT) syringes were designed to address these concerns. These LDT syringes can connect with the standardized ENFit® male connectors. Only a few studies have investigated dosing errors, and findings have largely been mixed. The objective of this report was to calculate the contributions of unavoidable dosing errors for LDT syringes, compare with legacy syringes and to suggest strategies to optimize dose accuracy for enteral applications. METHODS: Studies performed with a limited number of syringes to date may not reflect the actual diversity of dosing error that can occur across syringe orientations, batches, manufacturers, medications, etc. A computer-aided design software SolidWorks® was used to calculate the dosing errors in 0.5 and 1.0 ml legacy syringe connectors and were compared with dosing errors in LDT syringe connectors with the same nominal volume. Influence of orientation during delivery, spillage and flushing on dosing error was also investigated. RESULTS AND DISCUSSION: For 0.5 and 1.0 ml LDT syringes, in absence of medication in the moat area, the maximum dosing error will be ±5% when delivering 100% of nominal volume, which is also equal to the dosing error in 0.5 and 1.0 ml slip tip legacy syringes. However, with medication present in moat area, and with syringe reused during flushing, the LDT dosing error can range from 1% to 18% and 28% to 35% for 1.0 and 0.5 ml syringes, respectively. The corresponding dosing error for legacy syringes would be when the same syringe is used for flushing or when syringe disengages pointing vertically up. The corresponding dosing errors for legacy syringes could range from -7 to 12% and -9% to 19% for 1.0 and 0.5 ml syringes, respectively. Dosing errors for legacy and LDT syringes increase as the nominal capacity of syringe reduces, or when the dose delivered is lower than the nominal capacity of the syringe. WHAT IS NEW AND CONCLUSION: For LDT syringes, dosing errors can be reduced by clearing the moat area of the syringe and by using a new syringe for flushing post-delivery of medication. For legacy syringes, dosing errors can be minimized by ensuring the female connector points up during disengagement from the syringe post-medication administration, and by using a new syringe for flushing.

Comparison of Motion Analysis Systems in Tracking Upper Body Movement of Myoelectric Bypass Prosthesis Users.

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.

Evaluating the Impact of IMU Sensor Location and Walking Task on Accuracy of Gait Event Detection Algorithms.

There are several algorithms that use the 3D acceleration and/or rotational velocity vectors from IMU sensors to identify gait events (i.e., toe-off and heel-strike). However, a clear understanding of how sensor location and the type of walking task effect the accuracy of gait event detection algorithms is lacking. To address this knowledge gap, seven participants were recruited (4M/3F; 26.0 ± 4.0 y/o) to complete a straight walking task and obstacle navigation task while data were collected from IMUs placed on the foot and shin. Five different commonly used algorithms to identify the toe-off and heel-strike gait events were applied to each sensor location on a given participant. Gait metrics were calculated for each sensor/algorithm combination using IMUs and a reference pressure sensing walkway. Results show algorithms using medial-lateral rotational velocity and anterior-posterior acceleration are fairly robust against different sensor locations and walking tasks. Certain algorithms applied to heel and lower lateral shank sensor locations will result in degraded algorithm performance when calculating gait metrics for curved walking compared to straight overground walking. Understanding how certain types of algorithms perform for given sensor locations and tasks can inform robust clinical protocol development using wearable technology to characterize gait in both laboratory and real-world settings.

Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics.

There is an increased interest in using wearable inertial measurement units (IMUs) in clinical contexts for the diagnosis and rehabilitation of gait pathologies. Despite this interest, there is a lack of research regarding optimal sensor placement when measuring joint kinematics and few studies which examine functionally relevant motions other than straight level walking. The goal of this clinical measurement research study was to investigate how the location of IMU sensors on the lower body impact the accuracy of IMU-based hip, knee, and ankle angular kinematics. IMUs were placed on 11 different locations on the body to measure lower limb joint angles in seven participants performing the timed-up-and-go (TUG) test. Angles were determined using different combinations of IMUs and the TUG was segmented into different functional movements. Mean bias and root mean square error values were computed using generalized estimating equations comparing IMU-derived angles to a reference optical motion capture system. Bias and RMSE values vary with the sensor position. This effect is partially dependent on the functional movement analyzed and the joint angle measured. However, certain combinations of sensors produce lower bias and RMSE more often than others. The data presented here can inform clinicians and researchers of placement of IMUs on the body that will produce lower error when measuring joint kinematics for multiple functionally relevant motions. Optimization of IMU-based kinematic measurements is important because of increased interest in the use of IMUs to inform diagnose and rehabilitation in clinical settings and at home.

Characterizing loads at transfemoral osseointegrated implants.

Establishing normative and outlying loads on transfemoral osseointegrated devices will assist development of preclinical mechanical testing strategies to inform manufacturers and government regulators. Therefore, force and moment data from osseointegrated transfemoral transcutaneous implants were collated to better understand baseline load levels. Load data were also collected from other devices including transfemoral socket prostheses, instrumented hip stems, instrumented knee devices, instrumented limb salvage femoral endoprostheses, as well as estimated loads on transfemoral prostheses using data from able-bodied subjects. These additional data were assessed for their ability to bolster the limited osseointegrated device data. Several activities of daily living were investigated to characterize normative loading. Falling events were investigated to characterize outlying loads. Results revealed that limited loading data exist for osseointegrated devices. The most often reported activity was level walking. While these normative data may inform fatigue testing, they may not fully characterize fatigue loads during all activities of daily living. Socket prosthetics and able-bodied individuals may provide supplementary data, but significance is limited by sample sizes. Falling data are sparse, and insufficient data exist for characterizing adverse loads on osseointegrated devices. Future data collection should include more activities of daily living and adverse events to better define osseointegrated device loading profiles.