目的:采用Meta分析构建肿瘤患者经外周置入中心静脉导管(peripherally inserted central catheter,PICC)导管相关性静脉血栓形成风险预测模型。方法:系统检索探讨肿瘤患者PICC导管相关性静脉血栓形成危险因素的前瞻性和回顾性研究,经质...目的:采用Meta分析构建肿瘤患者经外周置入中心静脉导管(peripherally inserted central catheter,PICC)导管相关性静脉血栓形成风险预测模型。方法:系统检索探讨肿瘤患者PICC导管相关性静脉血栓形成危险因素的前瞻性和回顾性研究,经质量评价及数据提取后,采用meta分析合并结果以获得各个危险因素的综合危险度(pooled odds ratio,ORP),并以综合危险度的自然对数转换值为基础构建风险预测模型。结果:经文献检索及筛查后,最终纳入符合标准的文献6篇,包括1277例患者,其中病例组和对照组分别包括253和1 024例。Meta分析显示:基于累积样本的血栓发生率为19.81%。通过数据筛选,有5个危险因素进入模型,包括:COPD、高血压、糖尿病、化疗史、活动量减少,其综合危险度分别为:1.824,1.624,1.986,3.074和1.563。结论:以Meta分析结果为基础建立了具有循证基础的肿瘤患者PICC导管相关性静脉血栓形成风险预测模型。展开更多
[目的]基于已发表的齐拉西酮速效针剂对精神分裂症患者激越症状治疗的中英文文献,综合分析齐拉西酮针剂治疗激越症状的疗效及其相关影响因素。[方法]检索PubMed、EMBASE、W eb of Knowledge、Cochrane Library、万方数据,中国期刊全文...[目的]基于已发表的齐拉西酮速效针剂对精神分裂症患者激越症状治疗的中英文文献,综合分析齐拉西酮针剂治疗激越症状的疗效及其相关影响因素。[方法]检索PubMed、EMBASE、W eb of Knowledge、Cochrane Library、万方数据,中国期刊全文数据库,中国生物医学文献数据库(C BMdisc)和维普网,应用随机临床试验报告的声明(CONSORT)为参照标准进行入组和评价文献,采用STATA软件进行Meta分析,以疗效的效应值为因变量,性别、年龄、治疗前PANSS量表总分、是否合并口服抗精神病药物等为协变量,进行Meta回归模型分析。[结果]根据GRADE方法,主要结局指标的证据水平为“中度”。共有14项研究纳入Meta分析和Meta回归,其中英文5篇、中文9篇。治疗前后样本量分别为1197和1149。随机效应Meta分析结果显示齐拉西酮针剂疗效显著[SMD=2.04,95%CI(1.47,2.61),P=0.000]。Meta回归分析显示,疗效与基线PANSS分数(t=5.57,P=0.011)、合并使用口服抗精神病药物(t=4.07,P=0.027)有关,与文献发表语种(t=-0.57,P=0.625)、年龄(t=0.74,P=0.539)无关,女性相对于男性有疗效更优的统计学差异趋势(t=-2.95,P=0.060)。[结论]齐拉西酮速效针剂治疗精神分裂症患者激越症状疗效良好,Meta回归模型显示治疗前病情较重、合并口服抗精神病药物的患者疗效更好。展开更多
A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference veget...A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in China's Mainland for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.展开更多
基金supported by the National Basic Research Program of China (2007CB714404)the National Natural Science Foundation of China (40871173)the Spe-cial Grant for the Prevention and Treatment of Infectious Diseases (2008ZX10004-012)
文摘A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 22 d. The potential risk of occurrence of HPAI in China's Mainland for the period from January 23 to February 17, 2004 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.
