An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data.

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95-96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

Bowel preparation for colostomy reversal in children.

PURPOSE: Pediatric bowel preparation protocols used before colostomy reversal vary. The aim of this study is to determine institutional practices at our institution and evaluate the impact of bowel preparations on postoperative outcomes and hospital length of stay in children. METHODS: This was a retrospective review of children ≤18 years old undergoing colostomy reversal at Texas Children's Hospital (TCH) between 12/2013 and 8/2017. Preoperative bowel regimens and outcomes were collected and analyzed using descriptive statistics, Wilcoxon Rank-Sum and Fishers Exact tests. Continuous variables are presented as median [IQR]. RESULTS: Sixty-one children underwent colostomy reversal. Thirty-eight (62%) did not receive a preoperative bowel preparation. The two cohorts were similar in age, gender, and race. The most common indication for colostomy was anorectal malformation for thirty-seven (61%). Time from admission to surgery (19 h [17, 23] vs 3 [2, 3]; p < 0.01) and HLOS (6 days [5, 8] vs 5 [4, 6]; p = 0.02) were both longer in the bowel preparation cohort. Complications (3 [13%] vs 5 [22%]; p = 0.12) and 90-day readmissions (3 [13%] vs 6 [16%]; p = 0.64) were similar in both cohorts. CONCLUSION: Foregoing bowel preparation may have the potential to improve cost and reduce morbidity in children undergoing colostomy closure. LEVEL OF EVIDENCE: III. STUDY TYPE: Treatment study.