维普期刊资源整合服务平台（V6.5）

维普中文期刊产品整合服务

6篇 您的检索式：作者名="Mackram"

选中清除导出查看参考文献查看引证文献引用追踪共1页首页上一页第1页下一页末页 /1 跳转

		题名	作者	年代	出处	被引量
	1	Device-aware desktop web page transformation for rendering on handhelds显示文摘	Hassan A Artail Mackram Raydan	2005	Personal and Ubiquitous Computing2005，9，6：	1
	2	Device-aware desktop web- page transformation for rendering on handhehls 显示文摘	Hassan A Artail Mackram Raydan	2005	Personal and Ubiquitous Computing2005，9，6：	1
	3	Type A aortic dissection in patients with bicuspid aortic valves: clinical and pathological comparison with tricuspid aortic valves显示文摘	Mackram F Eleid Inga Forde William D Edwards Joseph J Maleszewski Rakesh M Suri Hartzell V Schaff Maurice Enriquez-Sarano Hector I Michelena	2013	Heart2013，，22：	1
	4	Flow-Gradient Patterns in Severe Aortic Stenosis With Preserved Ejection Fraction: Clinical Characteristics and Predictors of Survival显示文摘	Mackram F. Eleid Paul Sorajja Hector I. Michelena Joseph F. Malouf Christopher G. Scott Patricia A. Pellikka	2013	Circulation2013，，：	1
	5	Device-aware desktop web page transformation for rendering on haudhelds显示文摘	Hassan A Artail Mackram Raydan	2005	Personal and Ubiquitous Computing2005，9，6：	1
	6	Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement:The role of machine learning显示文摘BACKGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used to predict preprocedural risk for PPM.AIM To apply machine learning to be used to predict pre-procedural risk for PPM.METHODS A retrospective study of 1200 patients who underwent TAVR(January 2014-December 2017)was performed.964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis.After the exclusion of variables with near-zero variance or≥50%missing data,167 variables were included in the random forest gradient boosting algorithm(GBM)optimized using 5-fold cross-validations repeated 10 times.The receiver operator curve(ROC)for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.RESULTS Of 964 patients included in the 30-d analysis without prior PPM,19.6%required PPM post-TAVR.The mean age of patients was 80.9±8.7 years.42.1%were female.Of 657 patients included in the 1-year analysis,the mean age of the patients was 80.7±8.2.Of those,42.6%of patients were female and 26.7%required PPM at 1-year post-TAVR.The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model(0.66 and 0.72)was superior to that of the PPM risk score(0.55 and 0.54)with a P value<0.001.CONCLUSION The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.	Pradyumna Agasthi Hasan Ashraf Sai Harika Pujari Marlene Girardo Andrew Tseng Farouk Mookadam Nithin Venepally Matthew R Buras Bishoy Abraham Banveet K Khetarpal Mohamed Allam Siva K Mulpuru MD Mackram F Eleid Kevin L Greason Nirat Beohar John Sweeney David Fortuin David R Jr Holmes Reza Arsanjani	2023	World Journal of Cardiology2023，15，3：	0

返回顶部每页显示：

共1页首页上一页第1页下一页末页 /1 跳转

goolge学术搜索-维普

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费