Flying with a Safety Net: Use of REBOA to Enable Safe Transfer to a Level 1 Trauma Center

Background: Using Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) during air and ground transport requires coordination among the responding clinical team, transport team, and receiving surgical team. Here, we describe the development of a REBOA transport program in a civilian medical system that demonstrates the value of REBOA as part of the toolkit for safe casualty transport. Methods: The regional REBOA program was developed at St. Anthony Summit Medical Center in a multi-step planning and training process to ensure coordination among the facilities and transport resources during trauma patient care. Retrospective record review was performed on all patients (n=5) that received REBOA for transport from the Level 3 Trauma Center to the Level 1 Trauma Center, since inception of the program in March 2019. Data were gathered from hospital electronic medical records. Results: SASMC has transported five trauma patients under the REBOA program; all successfully arrived at the Level 1 Trauma Center to receive definitive care. The integrated arterial blood pressure monitoring capability in the REBOA catheter provided robust physiologic data to enable data-driven interventions during transport. Conclusion: The REBOA program described here is a model of how REBOA can be used to enable safe transport between levels of care, when, without REBOA, such transport might not be possible. The model is applicable during care of civilian trauma patients and combat casualties, where injured patients are initially treated in a prehospital or Role1/2 environment but require transport to a Level 1 Trauma Center or Role 3+ for definitive care. Keywords: REBOA, non-compressible hemorrhage, patient transport

The role of non-drug methods of increasing adherence to the treatment of patients with arterial hypertension in a polyclinic

Introduction. Arterial hypertension (AH) is one of the important high-risk factors for the development of heart diseases, but its diagnosis and treatment does not require high technologies and substantial costs. However, the asymptomatic course of AH combined with a low level of medical literacy contributes to low adherence to treatment.Aim. To study the indirect impact of remote blood pressure monitoring (RBPM), the introduction of educational materials (EMs) in comparison with self-monitoring of blood pressure (SMBP) on adherence of patients with AH to outpatient treatment.Materials and methods. 60 subjects with an established fact of partial or complete refusal to use antihypertensive therapy were enrolled in the study and randomized into four equal and comparable groups: 1 – SMBP, 2 – RBPM, 3 – SMBP + EMs, 4 – RBPM + EMs. The impact of using RBPM was assessed by comparing blood pressure values in groups 1 and 2; the impact of EM – by comparing the results of questionnaire surveys in groups 1 and 2 and groups 3 and 4.Results and discussion. Additional training of patients with AH contributed to a twofold increase in adherence in using SCAD and RBPM. The average systolic blood pressure in groups 1 and 2 was 134 mm Hg, in groups 3 and 4 – 129 mm Hg; diastolic blood pressure in groups 1 and 2 – 78.5 mm Hg, in groups 3 and 4 – 77.5 mm Hg.Conclusion. The maximum impact on adherence to AH treatment can be achieved with the introduction of a set of measures. However, the introduction of RBPM has a number of limitations associated with the additional economic burden and the lack of an approved methodology. However, the provision of treatment and prevention facilities with EMs does not require additional development of methodology and is significantly cheaper than the cost of modern blood pressure monitors.

An internet of things-based automatic brain tumor detection system

Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.