5 results on '"Guangda He"'
Search Results
2. Hospital Variation of Spironolactone Use in Patients Hospitalized for Heart Failure in China—The China PEACE Retrospective Heart Failure Study
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Yuan Yu, Wenchi Guan, Frederick A. Masoudi, Bin Wang, Guangda He, John A. Spertus, Yuan Lu, Harlan M. Krumholz, and Jing Li
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Heart Failure ,Treatment Outcome ,Humans ,Stroke Volume ,Spironolactone ,Cardiology and Cardiovascular Medicine ,Hospitals ,United States ,Ventricular Function, Left ,Mineralocorticoid Receptor Antagonists ,Retrospective Studies - Abstract
Background Although aldosterone antagonists improve outcomes in select individuals with heart failure and reduced ejection fraction, studies in the United States have raised concerns about underuse and overuse. Variations in the prescription of aldosterone antagonist in China are unknown. Methods and Results In the multicenter, hospital‐based, retrospective China PEACE (China Patient‐Centered Evaluative Assessment of Cardiac Events) study, we identified a nationally representative cohort of admissions for heart failure in a nationally representative sample of Chinese hospitals in 2015. Patients were classified into 1 of 3 groups according to their eligibility for spironolactone—“ideal” (left ventricular ejection fraction P P =0.020) higher rate of spironolactone use for ideal patients. Conclusions In this national study of hospitals in China, the use of spironolactone among ideal patients and the inappropriate use of spironolactone among patients with contraindications was substantial, with rates that varied markedly by institution. Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT02877914.
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- 2022
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3. Impact of Non-cardiac Comorbidities on Long-Term Clinical Outcomes and Health Status After Acute Heart Failure in China
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Xiqian, Huo, Lihua, Zhang, Xueke, Bai, Guangda, He, Jiaying, Li, Fengyu, Miao, Jiapeng, Lu, Jiamin, Liu, Xin, Zheng, and Jing, Li
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Cardiology and Cardiovascular Medicine - Abstract
BackgroundIndividual non-cardiac comorbidities are prevalent in HF; however, few studies reported how the aggregate burden of non-cardiac comorbidities affects long-term outcomes, and it is unknown whether this burden is associated with changes in health status.AimsTo assess the association of the overall burden of non-cardiac comorbidities with clinical outcomes and quality of life (QoL) in patients hospitalized for heart failure (HF).MethodsWe prospectively enrolled patients hospitalized for HF from 52 hospitals in China. Eight key non-cardiac comorbidities [diabetes, chronic renal disease, chronic obstructive pulmonary disease (COPD), anemia, stroke, cancer, peripheral arterial disease (PAD), and liver cirrhosis] were included, and patients were categorized into four groups: none, one, two, and three or more comorbidities. We fitted Cox proportional hazards models to assess the burden of comorbidities on 1-year death and rehospitalization.ResultsOf the 4,866 patients, 25.3% had no non-cardiac comorbidity, 32.2% had one, 22.9% had two, and 19.6% had three or more in China. Compared with those without non-cardiac comorbidities, patients with three or more comorbidities had higher risks of 1-year all-cause death [heart rate, HR 1.89; 95% confidence interval (CI) 1.48–2.39] and all-rehospitalization (HR 1.35; 95%CI 1.15–1.58) after adjustment. Although all patients with HF experienced a longitudinal improvement in QoL in the 180 days after discharge, those with three or more non-cardiac comorbidities had an unadjusted 11.4 (95%CI −13.4 to −9.4) lower Kansas City Cardiomyopathy Questionnaire (KCCQ) scores than patients without comorbidities. This difference decreased to −6.4 (95%CI −8.6 to −4.2) after adjustment for covariates.ConclusionAmong patients hospitalized with HF in this study, a higher burden of non-cardiac comorbidities was significantly associated with worse health-related QoL (HRQoL), increased risks of death, and rehospitalization post-discharge. The findings highlight the need to address the management of comorbidities effectively in standardized HF care.
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- 2022
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4. Health Status Predicts Short- and Long-Term Risk of Composite Clinical Outcomes in Acute Heart Failure
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Jinzhuo Ge, Xinghe Huang, Wei Li, Fengyu Miao, Runqing Ji, Jiaying Li, Xueke Bai, Ke Zhou, Guangda He, Lihua Zhang, Aoxi Tian, Danli Hu, Jing Li, Jiamin Liu, and Min Gao
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Adult ,Heart Failure ,medicine.medical_specialty ,business.industry ,Health Status ,Stroke Volume ,medicine.disease ,Prognosis ,humanities ,Peptide Fragments ,Ventricular Function, Left ,Cardiovascular death ,Long term risk ,Hospitalization ,Kansas City Cardiomyopathy Questionnaire ,Heart failure ,Emergency medicine ,Natriuretic Peptide, Brain ,medicine ,Humans ,Cardiology and Cardiovascular Medicine ,business ,Biomarkers - Abstract
This study aims to examine the association between the Kansas City Cardiomyopathy Questionnaire (KCCQ)-12 score and the 30-day and 1-year rates of composite events of cardiovascular death and heart failure (HF) rehospitalization in patients with acute HF.Few studies reported the prognostic effects of KCCQ in acute HF.This study prospectively enrolled adult patients hospitalized for HF from 52 hospitals in China and collected the KCCQ-12 score within 48 hour of index admission. The study used multivariable Cox regression to examine the association between KCCQ-12 score and 30-day and 1-year composite events and was further stratified by new-onset HF and acutely decompensated chronic heart failure (ADCHF). Subgroup analyses were performed to explore the potential heterogeneity. The study evaluated the incremental prognostic value of KCCQ-12 score over N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels and established risk scores by C-statistics, net reclassification improvement, and integrated discrimination improvement.Among 4,898 patients, 29.4% had new-onset HF. After adjustment, each 10-point decrease in the KCCQ-12 score was associated with a 13% increase in 30-day risk and a 7% increase in 1-year risk. The associations were consistent regardless of new-onset HF or ADCHF, age, sex, left ventricular ejection fraction, New York Heart Association functional class, NT-proBNP level, comorbidities, and renal function. Adding KCCQ-12 score to NT-proBNP and established risk scores significantly improved prognostic capabilities measured by C-statistics, net reclassification improvement, and integrated discrimination improvement.In acute HF, a poor KCCQ-12 score predicted short- and long-term risks of cardiovascular death and HF rehospitalization. KCCQ-12 could serve as a convenient tool for rapid initial risk stratification and provide additional prognostic value over NT-proBNP and established risk scores.
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- 2021
5. Are medical record front page data suitable for risk adjustment in hospital performance measurement? Development and validation of a risk model of in-hospital mortality after acute myocardial infarction
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Chao Zhang, Lin Li, Yan Liu, Jun Wang, Xueke Bai, Qianying Wang, Jing Zhang, Zhong Li, Lili Sun, Yuzhen Zhang, Qian Wang, Lei Wu, Bo Yu, Hui Dai, Tianyu Liu, Guang Ma, Xiaoping Gao, Jie Wu, Shu Zhang, Junli Wang, Yan Han, Guiling Li, Yi Tian, Zheng Wan, Chuanyu Gao, Yin Zhou, Jian Liu, Xin Jin, Jian Guan, Yu Huang, Feng Sun, Ruijun Zhang, Wei Luo, Xuexin Li, Weiwei Zhou, Long Chen, Weimin Li, Qing Huang, Yong Yi, Bin Xu, Zhenqiu Lin, Chun Yuan, Ping Yang, Haifeng Wang, Zhiming Li, Kaihong Chen, Guangming Yang, Chun Wu, Liang Lu, Ge Zhang, Yong Gao, Hongyan Li, Xiaofei Li, Hua Lu, Yanlong Liu, Yuhong Liu, Ting Jiang, Yuhui Lin, Chaoqun Wu, Danwei Zhang, Tiannan Zhou, Guangda He, Shiping Weng, Shuying Xie, Lirong Wu, Jiulin Chen, Tianfa Li, Qin Yu, Shiguo Hao, Xuemei Wu, Yachen Zhang, Zhifeng Liu, Zhongxin Wang, Hao Jia, Bayin Bate, Badeng Qiqige, Xiang Jin, Fengqin Liu, Dayong Xu, Xuejin He, Shui Yang, Jiping Wang, Lihua Gu, Shijiao Chen, Yongchao Zhi, Shengcheng Zhou, Lingjiao Jin, Yong Leng, Liangchuan Zhang, Tianyun Deng, Yuanjin Wang, Wenhua Zhang, Xinmin Ma, Xuan Ge, Xiaoping Wu, Yanming He, Fanju Meng, Dexi Liao, Guangyong Liu, Wen Qin, Wen Long, Xiangwen Chen, Baohong Zhang, Yonghou Yin, Bin Tian, Chaoyong Wu, Baoqi Liu, Zhihui Zhao, Haiming Li, Yansong Guo, Xinjing Chen, Liquan Xiang, Lin Ning, Xiuqi Li, Xing’an Wu, Congjun Tan, Mingfang Feng, Meili Wang, Liangfa Wen, Xiang Fu, Qunxing Xie, Yanni Zhuang, Jiaqian Lu, Qiuling Hu, Chunhui Xiao, Xiaoli Hu, Yongshuan Wu, Qiuli Wang, Youlin Xu, Xuefei Yu, Jianhong Zhang, You Zhang, Wentang Niu, Xiaolei Ma, Xiaowen Pan, Lifu Miao, Yanping Yin, Zhiying Zhang, Shutang Feng, Aiping Wang, Jiangli Zhang, Feipeng Li, Lijun Yu, Xinxin Zhao, Yuansheng Shen, Lizhen He, Zhiyi Rong, Xueqiao Wang, Rongjun Wan, Jianglin Tang, Guanghan Wu, Xiaohe Wu, Sang Ge, Pian Pu, Pingcuo Duoji, Yuming Du, Jianping Shi, Peihua Zhao, Jingsheng Sun, Hongxiang Li, Wen Liang, Zhiwen Dong, Zhenhai Zhao, Yaofeng Yuan, Zhirong Li, Jinbo Gao, Qiu’e Guo, Ruiqing Zhao, Guangjun Song, Lize Wang, Haiyun Song, Jinwen He, Jinming He, Keyong Shang, Changjiang Liu, Kuituan Xi, Rihui Liu, Peng Guo, Chaoyang Guo, Xiangjun Liu, Rujun Zhao, Zeyong Yu, Wenzhou Li, Xudong Jing, Huanling Wang, Xiyuan Zhao, Meifa Wei, Shengde Chen, Yong Fang, Ying Liao, Suzhe Cheng, Yunke Zhou, Xiaoxia Niu, Huifang Cao, Zebin Feng, Feilong Duan, Haiming Yi, Yuanxun Xu, Anran Guo, Xianshun Zhou, Hongzhuan Cai, Peng Zheng, Gaofeng Guo, Minwu Bao, Shaoliang Chen, Haibo Jia, Hongjuan Peng, Duanping Dai, Shaoxiong Hong, Song Chen, Dongya Zhang, Yudong Li, Jianbu Gao, Shouzhong Yang, Junhu An, Chenyang Shen, Yunfeng Liu, Huan Qu, Saiyong Chen, Dehai Jiao, Manhong Wang, Qiu Wang, Yingliang Xue, Cheng Yuan, Jianqing Zhang, Chunmei Wei, Yanmei Shen, Hehua Zhang, Hongmei Pan, Xiaowen Ma, Yanli Liang, Tianbiao Wang, Daguo Zhao, Xiaoming Tu, Zhenyan Gao, Fangning Wang, Qiang Yang, Xiaoping Kang, Jianbin Fang, Dongmei Liu, Chengning Shen, Mengfei Li, Yingmin Guan, Wenfeng Wang, Ting Xiao, Fengyun Jiang, Kaiyou Wu, Songguo Wang, Xujie Fu, Lifang Gao, Kai Fu, Xiaojing Duan, Rui Xiao, Ruixia Wu, Hongtu Zhang, Yuerong Ma, Zhonghui Cao, Zhansheng Ba, Wanhai Fu, Jianjun Jiang, Yafei Mi, Shiyu Zheng, Yang Zhong, Fangjiang Li, Xiaoyuan Wang, Pingshuan Dong, Laijing Du, Zhaofa He, Meihua Jin, Zhuoyan Chen, Manli Cheng, Yuqiang Ji, Youhua Zhou, Jvyuan Li, Yizhi Pan, Tianxun Wang, Guiyu Huang, Jianjun Pan, Qingliang Cai, Yuanming Yi, Xuelian Deng, Wenhua Chen, RongCai, Bing Zhang, Yousheng Xu, Zhengqiu Wang, Jun Shu, Puxia Suo, Aimin Zhang, Yongfen Kang, Yuemin Sun, Bo Bian, Xuejun Hu, Dawa Ciren, Guojiong Jia, Jieli Pan, Guofu Li, Hongliang Zhang, Longliang Zhan, Junping Fang, Xinli Yu, Dacheng Wang, Dajun Liu, Xinhong Cao, Haisheng Zhu, Wanchuan Liu, Zhaohai Zhou, Wuwang Fang, Manxin Chen, Fuqin Han, Jianye Fu, Yunmei Wang, Binglu Liu, Yanliang Zhang, Xiupin Yuan, Qingfei Lin, Yuliang Zhu, Zhiqiang Cai, Xingping Li, Lirong Ao, Shubing Wu, Fusheng Zhao, Renfei Liu, Wenwei Ai, Jianbao Chang, Haijie Zhao, Qijun Ran, Xuan Ma, Shijun Jiang, Xiaochun Shu, Zhiru Peng, Jianbin Wang, Li Yang, Yu Shen, Xingcun Shang, Zhisong Liao, Meiying Cai, Lining You, Shuqin Li, Yingjia Li, Jianxun Yang, Song Ai, Jianfei Ma, Lailin Deng, Keyu Wang, Shitang Gao, Banghua He, Youyi Lu, Weirong Yang, Zhizhong Zhang, Xiaohong Chi, Ru Duan, and Guangli Wang
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China ,medicine.medical_specialty ,Percentile ,Health Informatics ,030204 cardiovascular system & hematology ,Medical Records ,quality in health care ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Medicine ,Hospital Mortality ,030212 general & internal medicine ,Retrospective Studies ,Receiver operating characteristic ,business.industry ,Mortality rate ,Medical record ,Linear model ,Regression analysis ,Retrospective cohort study ,General Medicine ,Hospitals ,myocardial infarction ,Emergency medicine ,Cohort ,Risk Adjustment ,business - Abstract
ObjectivesTo develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.DesignA nationally representative retrospective study.SettingRepresentative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.ParticipantsRepresentative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measuresIn-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).ResultsA total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.ConclusionsThe models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.
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- 2021
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