Potential benefit of a screening tool in selecting head-injured children aged 36 months or younger who can cooperate in computed tomography

Purpose We aimed to investigate the benefit of a screening tool in selecting head-injured children who can potentially cooperate with computed tomography (CT). Methods The study population consisted of head-injured children aged 36 months or younger who visited the emergency department (ED) and underwent CT from January 2013 through December 2020. Procedural sedation and analgesia (PSA) using per os chloral hydrate or per rectal thiopental was implemented to children presumed less cooperative for CT as per a clinical screening tool for cooperative candidates for CT (e.g., ability to lie still on bed for 10 seconds without a guardian). According to the PSA and the first attempt success of CT, we compared baseline characteristics, CT findings, clinically important traumatic brain injury, ED length of stay (EDLOS), and ED disposition. Results Among the 247 children, PSA was used in 102 (41.3%). The PSA group showed a higher proportion of 3-36 months of age (PSA, 96.1% vs. non-PSA, 82.8%; P = 0.001) and longer median EDLOS (PSA, 127.0 [interquartile range, 101.0-172.0] vs. non-PSA, 85.0 minutes [63.0-130.0]; P < 0.001). The number of children undergoing the first attempt success was 213 (86.2%) without a difference in both groups (PSA, 84.3% vs. non-PSA, 87.6%; P = 0.645). In the 213 children, the implementation of PSA was associated with longer median EDLOS (PSA, 121.0 [99.0-156.0] vs. non-PSA, 77.0 minutes [60.0-122.0]; P < 0.001). In the non-PSA group (n = 145), the first attempt failure was associated with the presence of skull fracture (success, 7.1% vs. failure, 27.8%; P = 0.009). Conclusion This study suggests a potential benefit of the screening tool in selecting cooperative candidates for CT, i.e., those who could forgo PSA using oral chloral hydrate or per rectal thiopental, in young children with head injury

Real-Time Ventricular Fibrillation Detection Using an Embedded Microcontroller in a Pervasive Environment

Many healthcare problems are life threatening and need real-time detection to improve patient safety. Heart attack or ventricular fibrillation (VF) is a common problem worldwide. Most previous research on VF detection has used ECG devices to capture data and sent to other higher performance units for processing and has relied on domain experts and/or sophisticated algorithms for detection. In this case, it delayed the response time and consumed much more energy of the ECG module. In this study, we propose a prototype that an embedded microcontroller where an ECG sensor is used to capture, filter and process data, run VF detection algorithms, and only transmit the detected event to the smartphone for alert and call for services. We discuss how to adapt a common filtering and scale process and five light-weighted algorithms from open literature to realize the idea. We also develop an integrated prototype, which emulates the VF process from existing data sets, to evaluate the detection capability of the framework and algorithms. Our results show that (1) TD outperforms the other four algorithms considered with sensitivity reaching 96.56% and specificity reaching 81.53% in the MIT-BIH dataset. Our evaluations confirm that with some adaptation the conventional filtering process and detection algorithms can be efficiently deployed in a microcontroller with good detection accuracy while saving battery power, shortening response time, and conserving the network bandwidth