Background: Low and mid station vacuum assisted deliveries (VAD) are delicate manual procedures that entail a high degree of subjectivity from the operator and are associated with adverse neonatal outcome. Little has been done to improve the procedure, including the technical development, traction force and the possibility of objective documentation. We aimed to explore if a digital handle with instant haptic feedback on traction force would reduce the neonatal risk during low or mid station VAD.
Methods: A two centre, randomised superiority trial at Karolinska University Hospital, Sweden, 2016-2018. Cases were randomised bedside to either a conventional or a digital handle attached to a Bird metal cup (50 mm, 80 kPa). The digital handle measured applied force including an instant notification by vibration when high levels of traction force were predicted according to a predefined algorithm. Primary outcome was a composite of hypoxic ischaemic encephalopathy, intracranial haemorrhage, seizures, death and/or subgaleal hematoma. Three hundred eighty low and mid VAD in each group were estimated to decrease primary outcome from six to 2 %.
Results: After 2 years, an interim analyse was undertaken. Meeting the inclusion criteria, 567 vacuum extractions were randomized to the use of a digital handle (n = 296) or a conventional handle (n = 271). Primary outcome did not differ between the two groups: (2.7% digital handle vs 2.6% conventional handle). The incidence of primary outcome differed significantly between the two delivery wards (4% vs 0.9%, p < 0.05). A recalculation of power revealed that 800 cases would be needed in each group to show a decrease in primary outcome from three to 1 %. This was not feasible, and the study therefore closed.
Conclusions: The incidence of primary outcome was lower than estimated and the study was underpowered. However, the difference between the two delivery wards might reflect varying degree of experience of the technical equipment. An objective documentation of the extraction procedure is an attractive alternative in respect to safety and clinical training. To demonstrate improved safety, a multicentre study is required to reach an adequate cohort. This was beyond the scope of the study.
Trial Registration: ClinicalTrials.gov NCT03071783 , March 1, 2017, retrospectively registered.
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http://dx.doi.org/10.1186/s12884-021-03604-z | DOI Listing |
Sci Rep
January 2025
School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi, 830046, Xinjiang, China.
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Mandatory Center of Expertise for the Curation and Management of Archaeological Collections, St. Louis District, U.S. Army Corps of Engineers, St. Louis, Missouri, USA.
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December 2024
Purpose:Health e.V. Dortmund, Deutschland, APOLLON Hochschule der Gesundheitswirtschaft GmbH, Bremen, Deutschland. Electronic address:
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JMIR Res Protoc
December 2024
Business Information Systems, University College Cork, Cork, Ireland.
Background: The COVID-19 pandemic has highlighted the importance of strengthening national monitoring systems to safeguard a globally connected society, especially those in low- and middle-income countries. Africa's rapid adoption of digital technological interventions created a new frontier of digital advancement during crises or pandemics. The use of digital tools for disease surveillance can assist with rapid outbreak identification and response, handling duties such as diagnosis, testing, contact tracing, and risk communication.
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December 2024
Laboratory for Machine Vision and Security Research, Kennesaw State University-Marietta Campus, Marietta, USA.
Accurate lung nodule segmentation is fundamental for the early detection of lung cancer. With the rapid development of deep learning, lung nodule segmentation models based on the encoder-decoder structure have become the mainstream research approach. However, during the encoding process, most models have limitations in extracting edge and semantic information and in capturing long-range dependencies.
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