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find Author "任燕" 22 results
  • 风湿性心脏病合并慢性粒细胞白血病围手术期护理一例

    Release date:2016-08-26 02:09 Export PDF Favorites Scan
  • Nursing Experiences of Invasive Blood Pressure Monitoring for Patients Having Undergone Open-heart Surgery

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  • 脉波轮廓温度稀释连续心排量测量技术在心脏直视术后的临床监测及应用

    目的探讨脉波轮廓温度稀释连续心排量测量技术(PICCO)在心脏直视术后患者血流动力学参数监测中的应用及效果。 方法2011年1月-2012年6月采用PICCO监测20例术后危重患者的心功能指数(CI),全心舒张末期容积指数(GEDI),血管外肺血指数(ELWI),对监测结果为CI<3 L/(min·m2)、GEDI<700 mL/m2、ELWI>10 mL/kg的患者,治疗上慎重增加容量,同时增加儿茶酚胺类药物剂量的对策;对CI<3 L/(min·m2)、GEDI>700 mL/m2、ELWI>10 mL/kg的患者,治疗上予以增加儿茶酚胺类药物剂量同时严格控制容量,每日严格泵入液体量及管喂量的处置方式;对CI>3 L/(min·m2)、GEDI<700 mL/m2、ELWI>10 mL/kg、SVRI<900 kPa·s/(min·m2)的患者,则采取容量增加慎重同时增加儿茶酚胺类药物剂量,调节去甲肾上腺素用量的方式。 结果经PICCO严密监测以及药物和容量调整,19例患者循环逐渐稳定,均拔除气管插管后转出重症监护室(ICU)回病房继续治疗,1例因全心功能衰竭,抢救无效死亡。 结论通过应用PICCO对心脏直视术后患者血流动力学参数进行监测,能更直观有效、及时精确的找准血流动力学不稳定因素,对症下药,改善患者心功能情况,减少ICU住院天数,提高患者治愈率。

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  • Research Progress of Delirium after Cardiac Surgery

    Delirium is an acute, transient, usually reversible, fluctuating disturbance in consciousness, attention, cognition, and perception. Delirium after cardiac operations is associated with increased morbidity, reduced cognitive functioning, increased short-term and long-term mortality, longer hospitalization and higher hospitalization cost. The diagnosis, prevention and treatment of delirium are of great importance for perioperative care of patients undergoing cardiac surgery. Effective delirium screening tools are very helpful for the recognition and monitoring of delirium after cardiac surgery. In recent years, there has been many new strategies for the treatment, nursing care and prevention of delirium after cardiac surgery. This review focuses on the incidence, risk factors, diagnostic methods, treatment and preventive strategies of delirium after cardiac surgery.

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  • 房间隔缺损修补术后体外膜肺联合体位疗法的观察及护理一例

    Release date:2017-07-21 03:43 Export PDF Favorites Scan
  • Statistical methods in pragmatic randomized controlled trials (Ⅱ): Addressing missing outcome data

    Pragmatic randomized controlled trials can provide high-quality evidence. However, pragmatic trials need to frequently encounter the missing outcome data due to the challenges of quality assurance and control. The missing outcome could lead to bias which may misguide the conclusions. Thus, it is crucial to handle the missing outcome data appropriately. Our study initially summarized the bias structures and missingness mechanisms, and then reviewed important methods based on the assumption of missing at random. We referred to the multiple imputations and inverse probability of censoring weighting for dealing with missing outcomes. This paper aimed to provide insights on how to choose the statistical methods on missing outcome data.

    Release date:2021-07-22 06:18 Export PDF Favorites Scan
  • Evaluation of statistical performance for rare-event meta-analysis

    ObjectiveTo examine statistical performance of different rare-event meta-analyses methods.MethodsUsing Monte-Carlo simulation, we set a variety of scenarios to evaluate the performance of various rare-event meta-analysis methods. The performance measures included absolute percentage error, root mean square error and interval coverage.ResultsAcross different scenarios, the absolute percentage error and root mean square error were similar for Bayesian logistic regression model, generalized mixed linear effects model and continuity correction, but the interval coverage was higher with Bayesian logistic regression model. The statistical performances with Mantel-Haenszel method and Peto method were consistently suboptimal across different scenarios.ConclusionsBayesian logistic regression model may be recommended as a preferred approach for rare-event meta-analysis.

    Release date:2021-04-23 04:04 Export PDF Favorites Scan
  • Multilevel model and its application in evaluation of medicine policy intervention

    With the establishment and development of regional healthcare big data platforms, regional healthcare big data is playing an increasingly important role in health policy program evaluations. Regional healthcare big data is usually structured hierarchically. Traditional statistical models have limitations in analyzing hierarchical data, and multilevel models are powerful statistical analysis tools for processing hierarchical data. This method has frequently been used by healthcare researchers overseas, however, it lacks application in China. This paper aimed to introduce the multilevel model and several common application scenarios in medicine policy evaluations. We expected to provide a methodological framework for medicine policy evaluation using regional healthcare big data or hierarchical data.

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  • Exploring the patterns of real-world data governance in the context of special healthcare policy

    With the increasing improvement of real-world evidence as a research system and guideline specification for pre-market registration and post-market regulatory decision support of clinically urgent drug and mechanical products, identifying an approach to ensure the high quality and standards of real-world data and establishing a basis for the generation of real-world evidence is receiving increasing attention and concern from regulatory authorities. Based on the experience of Boao hope city real-world data research pattern and ophthalmic data platform construction, this paper discussed the "source data-database-evidence chain" generation process, data management, and data governance in real-world study from the special features and necessity of multiple sources and heterogeneity of data, multiple research designs, and standardized regulatory requirements, and provided references for further construction of comprehensive research data platforms in the future.

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  • Technical guidance for statistical analysis to assess therapeutic outcomes using real-world data

    Research of generating real-world evidence using real world data has attracted considerable attention globally. Outcome research of treatment based on existing health and medical data or registries has become one of the most important topics. However, there exists certain confusions in this line of research on how to design and implement appropriate statistical analysis. Therefore, in the fourth chapter of the series technical guidance to develop real world evidence by China REal world data and studies Alliance (ChinaREAL), we aim to provide an guidance on statistical analysis in the study to assess therapeutic outcomes based on existing health and medical data or registries.In this chapter, we first emphasize the significance of pre-specified statistical analysis plan, recommending key components of the statistical analysis plan. We then summarize the issue of sample size calculation in this content and clarify the interpretation of statistical p-value. Secondly, we recommend procedures to be considered to tackle the issue related to the selection bias, information bias and most importantly, confounding bias. We discuss the multivariable regression analysis as well as the popular causal inference models. We also suggest that careful consideration should be made to deal with missing data in real-world databases. Finally, we list core content of the statistical report.

    Release date:2019-07-18 10:28 Export PDF Favorites Scan
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