分诊安全管理结合风险管理提高急诊科护理质量评分的作用分析

目的探讨分诊安全管理结合风险管理提高急诊科护理质量评分的作用。方法该院于2018年6月起加强对急诊科的分诊安全管理结合风险管理,以于2017年6月—2019年6月在该院急诊科就诊的患者为研究对象,评价管理策略实施前后护理风险发生率、护理质量评分以及患者满意度等评价指标变化情况。结果实施后基础护理、实践操作、消毒隔离、急救药品、护理文件方面的护理质量评分均较实施前显著提高(P<0.05);实施后护理差错、护理缺陷、护理投诉的发生率分别为0.26%、0.58%、0.19%,均较实施前显著降低(P<0.05);实施后护理总满意率为94.92%,较实施前显著提高(P<0.05)。结论分诊安全管理结合风险管理有效提高了急诊科护理质量,提升了患者满意度,减少了护理风险事件的发生。     

口腔医疗机构应对国家突发公共卫生事件的问题与思考

突发公共卫生事件应急管理是一个全球性的课题。本文就2019年12月以来发生在中国的新型冠状病毒肺炎这一突发公共卫生事件,口腔医疗机构及其从业人员面临的相关问题进行法律层面的分析与思考,以期对口腔医疗行业应对突发公共卫生事件提供帮助和指导。     

Measuring the appropriateness of carbamazepine and valproic acid prescribing and utilization using a newly implemented online system in the Tabuk Region of Saudi Arabia

Epilepsy is a common neurologic disorder, which is efficiently treated with carbamazepine and valproic acid. Moreover, Saudi Ministry of Health implemented a new E-system for Poison Control Centers called Awtar to enhance technology utilization in ensuring patients’ satisfaction and to improve treatment outcomes. Therefore, we conducted this study to assess appropriateness of indication of requests and therapeutic levels of carbamazepine and valproic acid in Tabuk area, North West Saudi Arabia. This is a retrospective observational study conducted in Poison Control & Forensic Chemistry Center, Tabuk, Saudi Arabia. Patients’ data were obtained for years 2018 and 2019. The blood levels of carbamazepine and valproic acid were measured by Therapeutic Drug Monitoring (TDM) Unit. We selected patients treated with either valproic acid or carbamazepine alone without any history of drug allergy. Data of 264 patients were extracted from Awtar E-system. Serum carbamazepine levels were within therapeutic range in 114 patients (75.50%), above-therapeutic range in 13 patients (8.61%) and sub-therapeutic levels in 24 patients (15.89%). Regarding serum valproic acid, it is within therapeutic range in 62 patients (54.87%), above-therapeutic range in 11 patients (9.73%) and sub-therapeutic levels in 40 patients (35.40%). In conclusion, this study gives information about partial appropriateness of usage of carbamazepine and low level of appropriateness of valproic acid. However, more efforts are needed to improve results of appropriateness of indication of antiepileptic drugs.     

Development of an IgG-Fc fusion COVID-19 subunit vaccine,AKS-452

AKS-452 is a biologically-engineered vaccine comprising an Fc fusion protein of the SARS-CoV-2 viral spike protein receptor binding domain antigen (Ag) and human IgG1 Fc (SP/RBD-Fc) in clinical development for the induction and augmentation of neutralizing IgG titers against SARS-CoV-2 viral infection to address the COVID-19 pandemic. The Fc moiety is designed to enhance immunogenicity by increasing uptake via Fc-receptors (FcγR) on Ag-presenting cells (APCs) and prolonging exposure due to neonatal Fc receptor (FcRn) recycling. AKS-452 induced approximately 20-fold greater neutralizing IgG titers in mice relative to those induced by SP/RBD without the Fc moiety and induced comparable long-term neutralizing titers with a single dose vs. two doses. To further enhance immunogenicity, AKS-452 was evaluated in formulations containing a panel of adjuvants in which the water-in-oil adjuvant, Montanide™ ISA 720, enhanced neutralizing IgG titers by approximately 7-fold after one and two doses in mice, including the neutralization of live SARS-CoV-2 virus infection of VERO-E6 cells. Furthermore, ISA 720-adjuvanted AKS-452 was immunogenic in rabbits and non-human primates (NHPs) and protected from infection and clinical symptoms with live SARS-CoV-2 virus in NHPs (USA-WA1/2020 viral strain) and the K18 human ACE2-trangenic (K18-huACE2-Tg) mouse (South African B.1.351 viral variant). These preclinical studies support the initiation of Phase I clinical studies with adjuvanted AKS-452 with the expectation that this room-temperature stable, Fc-fusion subunit vaccine can be rapidly and inexpensively manufactured to provide billions of doses per year especially in regions where the cold-chain is difficult to maintain.     

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).