Introduction: There is little recent information about prehospital delay time for Australian patients with myocardial infarction (MI).
Objectives: This study: (1) describes prehospital delay time for patients with MI; (2) identifies variables and presenting symptoms which contribute to the delay.
Methods: This retrospective cohort study identified patients with an Emergency Department (ED) discharge diagnosis of MI, transported by ambulance to one of the seven Perth metropolitan EDs, between January 2008 and October 2009. Prehospital delay times were analysed using linear regression models. Non-numeric (word descriptions) of delay time were categorised.
Results: Of 1,633 patients, symptom onset-time was available for 1,003. For 829 patients with a numeric onset-time, median delay was 2.2hours; decreased delay was associated with age <70 years, presenting with chest pain, and diaphoresis. Increased delay was associated with being with a primary health care provider, and if the patient was at home and if the person who called the ambulance was anyone other than the spouse. For 174 patients with non-numeric onset-times, 37% patients delayed one to three days and 110 (64.0%) patients described their symptoms as intermittent and/or of gradual onset.
Conclusion: Given that prehospital delay times remain longer than is optimal, public awareness of MI symptoms should be enhanced in order to decrease prehospital delay.
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http://dx.doi.org/10.1016/j.hlc.2015.02.026 | DOI Listing |
Medicina (Kaunas)
January 2025
Department of Surgery, General Surgery, Sapienza University of Rome, 00185 Roma, Italy.
Trauma, particularly uncontrolled bleeding, is a major cause of death. Recent evidence-based guidelines recommend the use of a tourniquet when life-threating limb bleeding cannot be controlled with direct pressure. Prehospital hemorrhage management, according to the XABCDE protocol, emphasizes the critical role of tourniquets in controlling massive bleeding.
View Article and Find Full Text PDFIdentifying and managing pediatric sepsis is a major research focus, yet early detection and risk assessment remain challenging. In its early stages, sepsis symptoms often mimic those of mild infections or chronic conditions, complicating timely diagnosis. Although various early warning scores exist, their effectiveness is limited, particularly in prehospital settings where accurate, rapid assessment is crucial.
View Article and Find Full Text PDFJ Neurointerv Surg
January 2025
Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan
Background: Timely treatment within the therapeutic window is critical for patients with stroke. This study adopts a risk-averse optimization approach to maximize the likelihood of receiving treatment within this window.
Methods: We developed an optimization model using data from a citywide stroke registry (July 1, 2019 to December 31, 2020).
J Med Internet Res
January 2025
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Background: The aging global population and the rising prevalence of chronic disease and multimorbidity have strained health care systems, driving the need for expanded health care resources. Transitioning to home-based care (HBC) may offer a sustainable solution, supported by technological innovations such as Internet of Medical Things (IoMT) platforms. However, the full potential of IoMT platforms to streamline health care delivery is often limited by interoperability challenges that hinder communication and pose risks to patient safety.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Cardiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Background: Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage.
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