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Comprehensive analysis of the impact of primary percutaneous coronary intervention on patients with ST-segment elevation myocardial infarction
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作者 Ayrton I Bangolo Nikita Wadhwani 《World Journal of Experimental Medicine》 2024年第4期170-173,共4页
This comprehensive analysis by Saeed and Faeq investigates the impact of primary percutaneous coronary intervention(pPCI)on mortality among patients with ST-segment elevation myocardial infarction(STEMI)at the Erbil C... This comprehensive analysis by Saeed and Faeq investigates the impact of primary percutaneous coronary intervention(pPCI)on mortality among patients with ST-segment elevation myocardial infarction(STEMI)at the Erbil Cardiac Center.Analyzing data from 96 consecutive STEMI patients,the study identified significant predictors of in-hospital mortality,emphasizing the critical impact of time of hospital arrival post-symptom onset on overall prognosis.Findings indicate that factors such as atypical presentation,cardiogenic shock,chronic kidney disease,and specific coronary complications are associated with higher mortality rates.The study underscores the necessity of prompt medical intervention for improving survival outcomes in STEMI patients,especially in the high-risk subgroup.This research offers valuable insights into optimizing STEMI management and enhancing patient survival rates through effective and timely pPCI. 展开更多
关键词 ST-segment elevation myocardial infarction Primary percutaneous coronary intervention Mortality predictors timely hospital arrival Cardiogenic shock
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江西省某三级甲等医院2型糖尿病患者次均住院费用新灰色关联分析 被引量:27
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作者 马强 张彩凤 +2 位作者 李芬 陈玉倩 万晓文 《中国卫生资源》 北大核心 2021年第2期171-175,共5页
目的分析江西省某三级甲等医院2型糖尿病患者次均住院费用的构成以及影响次均住院费用的主要因素,为有效控制此类患者的住院费用及减轻疾病经济负担提供依据。方法选取江西省某三级甲等医院2015—2018年1928例2型糖尿病患者的住院费用明... 目的分析江西省某三级甲等医院2型糖尿病患者次均住院费用的构成以及影响次均住院费用的主要因素,为有效控制此类患者的住院费用及减轻疾病经济负担提供依据。方法选取江西省某三级甲等医院2015—2018年1928例2型糖尿病患者的住院费用明细,利用新灰色关联分析法对患者住院各项费用的关联度、贡献率以及变动率等指标进行分析。结果2015—2018年,次均住院费用的构成比和关联系数最大的影响因素是治疗费,而结构贡献率最高的因素是药品费;与次均住院费用关联度最大的前三项目顺位分别是治疗费(0.9650)、药品费(0.9100)和检查费(0.8125);结构贡献率最高的前三项目顺位是药品费(30.00%)、护理费(11.69%)和诊察费(10.61%),三者累计结构贡献率达到52.30%。结论治疗费、药品费和检查费是影响患者次均住院费用最主要的因素,但服务性项目的收费较低。建议着重控制药品费用的增长,适当减轻检查类项目对费用的影响,严格规范临床路径,合理提高服务性项目费用的比例,进一步体现医护人员的劳务价值,构建糖尿病精细化管理体系,丰富健康管理内涵。 展开更多
关键词 2型糖尿病type 2 diatetes 次均住院费用average hospitalization cost per time 新灰色关联分析new grey relational analysis 结构变动度structural change
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Occurrence of Delirium and Length of Stay of Patients in the Intensive Care Unit
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作者 Aparecida Sátira da Silva Machado Mayara Rabello Teixeira Alves +4 位作者 Daniella Nogueira Vieira Sara Ellias de Sousa Felipe Rodrigues Maia Daniel Almeida da Costa Leandro Raider 《Journal of Biosciences and Medicines》 2021年第8期1-9,共9页
<strong>Introduction: </strong>The delirium has received little attention from professionals working in the intensive care unit, mainly due to the fact that this is, rarely, the primary reason for patient ... <strong>Introduction: </strong>The delirium has received little attention from professionals working in the intensive care unit, mainly due to the fact that this is, rarely, the primary reason for patient admission. Given the high prevalence of delirium in an intensive care environment, the current guidelines recommend the daily assessment of delirium and a multidisciplinary approach. Delirium is a frequent and severe form of acute brain dysfunction, as well as an important source of concern in critical care. <strong>Objective:</strong> To assess the occurrence of delirium and time of stay in the intensive care unit. <strong>Method:</strong> This is a quantitative, descriptive study, with a cross-sectional design, which was carried out in a university hospital located in the interior of the State of Rio de Janeiro. The sample consisted of 89 patients, of both sexes, aged between 24 and 92 years. The RASS and CAM-ICU scales were used to assess delirium. The data were collected every 12 hours, for 3 months, 7 days a week and in an uninterrupted manner. <strong>Results:</strong> Were evaluated 89 patients, of which 16 were excluded according to the scale criteria, leaving 73 patients. After evaluation, 22 patients were diagnosed with delirium and 51 patients without delirium. Of the patients who presented delirium, 13 deaths and 9 had high to the nursery. Of the patients who did not have delirium, 40 had high to the nursery and 11 deaths. Patients with delirium had an average hospital stay of 23.25 days and patients who did not have delirium had an average of 4.5 days hospitalization.<strong> Conclusion: </strong>We can infer that the longer the patient spends in the intensive care unit, the greater the chance of delirium occurring. Therefore, preventive and interventional measures are necessary to decrease the mortality rate in patients with delirium and early detection is an excellent tool to improve this outcome. 展开更多
关键词 DELIRIUM Intensive Care Unit time of hospitalization
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Association between Air Cane Field Burning Pollution and Respiratory Diseases:A Bayesian Approach
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作者 Jorge Alberto Achcar Mayara Piani Luna da Silva Sicchieri Edson Zangiacomi Martinez 《Journal of Environmental Protection》 2013年第8期161-167,共7页
Respiratory diseases and air pollution are the goals of many scientific works, but studies of the relations between these diseases and cane field burning pollution are still not well studied in the literature. In this... Respiratory diseases and air pollution are the goals of many scientific works, but studies of the relations between these diseases and cane field burning pollution are still not well studied in the literature. In this work, we consider the times between days of extrapolations of the number of daily hospitalizations due to respiratory diseases as our data. To analyze this data set, we introduce different statistical models related to burning focus pollution and their relations with the counting of hospitalizations due to respiratory diseases. Under a Bayesian approach and with the help of the free available WinBUGS software, we get posterior summaries of interest using standard MCMC (Markov Chain Monte Carlo) methods. 展开更多
关键词 hospitalization Counting Bayesian Models times between hospitalization Extrapolation Days Respiratory Diseases
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