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1,409 results on '"Shen, Xian"'

4. Perioperative versus adjuvant S-1 plus oxaliplatin chemotherapy for stage II/III resectable gastric cancer (RESONANCE): a randomized, open-label, phase 3 trial

Author

Wang, Xinxin, Lu, Canrong, Wei, Bo, Li, Shuo, Li, Ziyu, Xue, Yingwei, Ye, Yingjiang, Zhang, Zhongtao, Sun, Yihong, Liang, Han, Li, Kai, Zhu, Linghua, Zheng, Zhichao, Zhou, Yanbing, He, Yulong, Li, Fei, Wang, Xin, Liang, Pin, Huang, Hua, Li, Guoli, Shen, Xian, Ji, Jiafu, Tang, Yun, Xu, Zekuan, and Chen, Lin

Published
2024
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16. AIWW: a new nutrition-screening tool for the oncologic population

Author

Ge, Yi-Zhong, Fu, Zhen-Ming, Zhang, Qi, Song, Meng-Meng, Ruan, Guo-Tian, Zhang, Xi, Zhang, Xiao-Wei, Li, Xiang-Rui, Zhang, Kang-Ping, Tang, Meng, Liu, Xiao-Yue, Yang, Ming, Liu, Tong, Xie, Hai-Lun, Zhang, He-Yang, Wang, Zi-Wen, Hu, Chun-Lei, Lin, Shi-Qi, Zhang, Rui, Xu, Hong-Xia, Li, Wei, Song, Chun-Hua, Liu, Ming, Chen, Jun-Qiang, Wang, Kun-Hua, Bo, Li, Cong, Ming-Hua, Li, Zeng-Ning, Guo, Zeng-Qin, Wang, Xiao-Bin, Wang, Bin-Yan, Xu, Benjamin, Qin, Xian-Hui, Xu, Xi-Ping, Barazzoni, Rocco, Yao, Qing-Hua, Weng, Min, Shen, Xian, and Shi, Han-Ping

Published
2023
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19. Angiotensin Receptor Neprilysin Inhibition and Associated Outcomes by Race and Ethnicity in Patients With Heart Failure With Reduced Ejection Fraction: Data From CHAMP‐HF

Author

Chapman, Brittany, Hellkamp, Anne S, Thomas, Laine E, Albert, Nancy M, Butler, Javed, Patterson, J Herbert, Hernandez, Adrian F, Williams, Fredonia B, Shen, Xian, Spertus, John A, Fonarow, Gregg C, and DeVore, Adam D

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Clinical Research, Heart Disease, angiotensin receptor neprilysin inhibitor, heart failure, population groups, registries, sacubitril/valsartan, Cardiorespiratory Medicine and Haematology, Cardiovascular medicine and haematology
Abstract

Background There are limited data on the use of angiotensin receptor neprilysin inhibitors (ARNIs) in minority populations with heart failure (HF) with reduced ejection fraction. We used data from the CHAMP-HF (Change the Management of Patients With Heart Failure) registry to evaluate ARNI initiation and associated changes in health status and clinical outcomes across different races and ethnicities. Methods and Results CHAMP-HF was a prospective, observational registry of US outpatients with chronic HF with reduced ejection fraction. We compared patients starting ARNI with patients not starting ARNI using a propensity-matched analysis. Patients were grouped as Hispanic, non-Hispanic Black, non-Hispanic White, or non-Hispanic other individuals, where "non-Hispanic other" consists of all patients who did not identify as Hispanic, Black, or White. Health status was assessed using the 12-item Kansas City Cardiomyopathy Questionnaire. Outcomes were analyzed with multivariable models that included race and ethnicity, ARNI initiation, and an interaction term between race and ethnicity and ARNI initiation. Cox proportional hazards models were used for death/HF hospitalization, and multiple regression was used for change in Kansas City Cardiomyopathy Questionnaire score. The analysis included 1516 patients, with 758 patients in each group (ARNI and no ARNI). Changes in Kansas City Cardiomyopathy Questionnaire score after ARNI initiation were similar among all race and ethnicity groups (mean [SD], non-Hispanic White individuals, 3.5 [19.0]; non-Hispanic Black individuals, 2.0 [17.0]; non-Hispanic other individuals, 5.5 [20.3]; and Hispanic individuals, 3.2 [20.1]), with no statistically significant interaction between race and ethnicity and ARNI initiation (P=0.21). There was similarly no statistically significant interaction between race and ethnicity and ARNI initiation for HF hospitalization (P=0.82) or all-cause mortality (P=0.92). Conclusions In a large registry of outpatients with HF with reduced ejection fraction, the association between ARNI initiation and outcomes did not differ by race and ethnicity. These data support the use of ARNI therapy for chronic HF with reduced ejection fraction irrespective of race and ethnicity.

Published
2022

27. Identifying patients at increased risk for poor outcomes from heart failure with reduced ejection fraction: the PROMPT‐HF risk model

Author

DeVore, Adam D, Hill, Claude Larry, Thomas, Laine E, Albert, Nancy M, Butler, Javed, Patterson, J Herbert, Hernandez, Adrian F, Williams, Fredonia B, Shen, Xian, Spertus, John A, and Fonarow, Gregg C

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Patient Safety, Heart Disease, Clinical Research, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Health Status, Heart Failure, Hospitalization, Humans, Quality of Life, Stroke Volume, Heart failure, Left ventricular dysfunction, Quality of life, Risk model, Cardiorespiratory Medicine and Haematology, Cardiovascular medicine and haematology
Abstract

AimsWe aimed to develop a risk prediction tool that incorporated both clinical events and worsening health status for patients with heart failure (HF) with reduced ejection fraction (HFrEF). Identifying patients with HFrEF at increased risk of a poor outcome may enable proactive interventions that improve outcomes.Methods and resultsWe used data from a longitudinal HF registry, CHAMP-HF, to develop a risk prediction tool for poor outcomes over the next 6 months. A poor outcome was defined as death, an HF hospitalization, or a ≥20-point decrease (or decrease below 25) in 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12) overall summary score. Among 4546 patients in CHAMP-HF, 1066 (23%) experienced a poor outcome within 6 months (1.3% death, 11% HF hospitalization, and 11% change in KCCQ-12). The model demonstrated moderate discrimination (c-index = 0.65) and excellent calibration with observed data. The following variables were associated with a poor outcome: age, race, education, New York Heart Association class, baseline KCCQ-12, atrial fibrillation, coronary disease, diabetes, chronic kidney disease, smoking, prior HF hospitalization, and systolic blood pressure. We also created a simplified model with a 0-10 score using six variables (New York Heart Association class, KCCQ-12, coronary disease, chronic kidney disease, prior HF hospitalization, and systolic blood pressure) with similar discrimination (c-index = 0.63). Patients scoring 0-3 were considered low risk (event rate 40%).ConclusionsThe PROMPT-HF risk model can identify outpatients with HFrEF at increased risk of poor outcomes, including clinical events and health status deterioration. With further validation, this model may help inform therapeutic decision making.

Published
2022

50. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network

Author

LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao

Subjects
intraframe coding, virtual reality, convolutional neural network, coding unit, convolution kernel size, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

In order to improve the performance of virtual reality video intraframe prediction coding,convolutional neural network algorithm is used to select video frame coding unit(CU) to reduce the complexity of video image coding.Firstly,quantization parameters are set to obtain the virtual reality video frame samples,then the image coding tree is constructed,and the convolutional neural network (CNN) frame coding unit optimization model is established.The image brightness of frame samples is taken as the CNN input,combined with the image rate distortion cost threshold,the optimization results of the frame coding unit are obtained through training.Using CNN training optimization,the coding tree(CTU) structure with different depths and an appro-priate number of CU modules can be obtained according to the intraframe coding requirements of different texture modules of the image.Experiments show that,by reasonably setting the convolution kernel size and quantization parameters,CNN algorithm can obtain better image quality and less coding time than common video intraframe prediction coding algorithms.

Published
2022
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