In the hands of ultrasound (US)-experienced neonatologists, RTUS-guided PICC placement reduces catheter insertion duration, and is associated with fewer manipulations and radiographs when compared with conventional placement.
Objective: Umbilical catheter placement is a routine neonatal emergency procedure that has large variability in technical methods. Commonly used methods are unable to accurately estimate insertion lengths, and X-rays cannot always identify malpositioned catheters. In clinical practice, the placement of umbilical lines takes time away from nursing during a critical transition period. Ultrasound is a safe and commonly used tool in the nursery for clinical management of sick neonates and has been shown to readily identify central catheter tip position. In this study, we sought to determine a more time-efficient and accurate means of umbilical catheter placement using bedside ultrasound.Study Design: This is a prospective, randomized, pilot trial of infants of any age or weight admitted to the neonatal intensive care unit who required umbilical catheter placement. Infants were excluded if they had congenital heart disease, abdominal wall defects or had a single umbilical artery. Catheters were placed using either the conventional method, with blinded evaluation of the catheters using ultrasound, or with ultrasound guidance, with input pertaining to catheter tip location. The number of X-rays required to confirm proper positioning, completion time points throughout the procedure and manipulations of the lines were recorded for both groups.Result: Ultrasound use decreased the time of line placement with an average saving of 64 min, as well as decreased the number of manipulations required and X-rays taken to place the catheters. The average X-ray time from request to viewing per X-ray was 40 min for all subjects.Conclusion: Ultrasound-guided umbilical catheter placement is a faster method to place catheters requiring fewer manipulations and X-rays when compared with conventional catheter placement.
Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert's manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.
For a number of compelling scientific, operational, and regulatory reasons, the use of electronic data capture is becoming the preferred means of collecting clinical outcome assessment (eg, patient-reported outcome [PRO]) data in clinical trials. Electronic PRO (ePRO) data collection leverages screen-based technologies (eg, handheld devices, tablet computers, and web-based systems) and telephone-based (eg, interactive voice response) systems. Data collection is routinely either site based (ie, clinical study site) or field based (eg, subject's home, school, or workplace). While tablet computers are often used for site-based PRO data collection, handheld devices have become the mainstay for ePRO data capture in field-based settings. The data collection devices are usually provisioned to the sites or subjects by an ePRO system provider contracted by the clinical trial sponsor. With site-based data collection, study staff are responsible for ensuring subject compliance with the protocol-driven data collection procedures, whereas with field-based data collection, the subject is responsible for compliance with the data entry requirements and sites are accountable for remotely monitoring the data for compliance. In addition to site and subject compliance issues, technology-related factors must be anticipated in order to adhere to the electronic PRO data collection plan. The objective of this paper is to describe study site-, subject-, and technology-related factors that may lead to deviations from the planned electronic collection of PRO data (eg, defaulting to paper-based data collection) and to provide recommendations aimed at preventing potential problems or quickly resolving problems once they occur.
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