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7. Correction to: Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Published
2022
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8. Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Published
2022
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11. SOFA in sepsis: with or without GCS.

Author

Wang, Lu, Ma, Xudong, Zhou, Guanghua, Gao, Sifa, Pan, Wei, Chen, Jieqing, Su, Longxiang, He, Huaiwu, Long, Yun, Yin, Zhi, Shu, Ting, Zhou, Xiang, Liu, Yongjun, Kang, Yan, Yan, Jing, Chen, Erzhen, Xiong, Bin, Qin, Bingyu, Qian, Kejian, and Fang, Wei

Subjects
SEPSIS, SOFAS, RESPIRATORY organs, GLASGOW Coma Scale, TRACHEA intubation
Abstract

Purpose: Sepsis is a global public health burden. The sequential organ failure assessment (SOFA) is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of the Glasgow Coma Score (GCS) is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis. Methods: First, 3048 patients with sepsis admitted to Peking Union Medical College Hospital (PUMCH) were enrolled in this survey. The data were collected from June 8, 2013 to October 12, 2022. Second, 18,108 patients with sepsis in the eICU database were enrolled. Third, 2397 septic patients with respiratory system ≥ 3 points in SOFA in the eICU database were included. We investigated the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis in various ICUs of PUMCH, and then we validated the results in the eICU database. Main results: In data of ICUs in PUMCH, the predictive efficiency of SOFA without GCS (AUROC [95% CI], 24 h, 0.724 [0.688, 0.760], 48 h, 0.734 [0.699, 0.769], 72 h, 0.748 [0.713, 0.783], 168 h, 0.781 [0.747, 0.815]) was higher than that of SOFA with GCS (AUROC [95% CI], 24 h, 0.708 [0.672, 0.744], 48 h, 0.721 [0.685, 0.757], 72 h, 0.735 [0.700, 0.757], 168 h, 0.770 [0.736, 0.804]) on ICU mortality in patients with sepsis, and the difference was statistically significant (P value, 24 h, 0.001, 48 h, 0.003, 72 h, 0.004, 168 h, 0.005). In septic patients with respiratory system ≥ 3 points in SOFA in the eICU database, although the difference was not statistically significant (P value, 24 h, 0.148, 48 h, 0.178, 72 h, 0.132, 168 h, 0.790), SOFA without GCS (AUROC [95% CI], 24 h, 0.601 [0.576, 0.626], 48 h, 0.625 [0.601, 0.649], 72 h, 0.639 [0.615, 0.663], 168 h, 0.653 [0.629, 0.677]) had a higher predictive efficiency on ICU mortality than SOFA with GCS (AUROC [95% CI], 24 h, 0.591 [0.566, 0.616], 48 h, 0.616 [0.592, 0.640], 72 h, 0.628 [0.604, 0.652], 168 h, 0.651 [0.627, 0.675]). Conclusions: In severe sepsis, it is realistic and feasible to discontinue the routine GCS for SOFA in patients with a respiratory system ≥ 3 points, and even better predict ICU mortality. Key messages: SOFA is the most commonly used scoring system for diagnosing sepsis and assessing severity. Due to the widespread use of endotracheal intubation and sedative medications in sepsis, the accuracy of GCS is the lowest in SOFA. We designed this multicenter, cross-sectional study to investigate the predictive efficiency of SOFA with or without GCS on ICU mortality in patients with sepsis. In severe sepsis, it is realistic and feasible to discontinue the routine GCS for SOFA in patients with a respiratory system ≥ 3 points, and even better predict ICU mortality. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

Author

Ma, Penglin, Liu, Jingtao, Shen, Feng, Liao, Xuelian, Xiu, Ming, Zhao, Heling, Zhao, Mingyan, Xie, Jing, Wang, Peng, Huang, Man, Li, Tong, Duan, Meili, Qian, Kejian, Peng, Yue, Zhou, Feihu, Xin, Xin, Wan, Xianyao, Wang, ZongYu, Li, Shusheng, Han, Jianwei, Li, Zhenliang, Ding, Guolei, Deng, Qun, Zhang, Jicheng, Zhu, Yue, Ma, Wenjing, Wang, Jingwen, Kang, Yan, and Zhang, Zhongheng

Published
2021
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19. Carbapenem is not always the best choice in the treatment of septic shock.

Author

Wang, Lu, Ma, Xudong, Chen, Yujie, Gao, Sifa, He, Huaiwu, Su, Longxiang, Guo, Yanhong, Shan, Guangliang, Hu, Yaoda, Zhou, Xiang, Liu, Dawei, China National Critical Care Quality Control Centre Group (China-NCCQC), Wang, Xue, Guan, Xiangdong, Kang, Yan, Xiong, Bin, Qin, Bingyu, Qian, Kejian, Wang, Chunting, and Zhao, Mingyan

Subjects
SEPTIC shock, SHOCK therapy, PATHOGENIC bacteria
Abstract

Background: Septic shock is a global public health burden. In addition to the improvement of the level of individual care, the improvement of the overall hospital quality control management is also an essential key aspect of the Surviving Sepsis Campaign (SSC). Using of antibiotics is a cornerstone in the treatment of septic shock, so we conducted this study to investigate the influence of antibiotics and pathogenic bacteria on the mortality of septic shock at the level of overall hospital in China. Methods: This was an observational database study in 2021 enrolled the data of 787 hospitals from 31 provinces/municipalities/autonomous regions of Mainland China collected in a survey from January 1, 2021 to December 31, 2021. Results: The proportion of ICU patients with septic shock was 3.55%, while the patient mortality of septic shock was 23.08%. While carbapenem was the most preferred antibiotic medication used in 459 of the 782 hospitals, the preference for carbapenem did not show significant effect on the patient mortality in the treatment of septic shock (p-value 0.59). Compared with patients with fermenting bacteria as the most common pathogenic bacteria causing septic shock, patients with non-fermenting bacteria had a higher mortality (p-value 0.01). Conclusions: Whether using carbapenem as the preferred antibiotic or not, did not show effect on the patient mortality of septic shock. Compared with patients with fermenting bacteria as the most common pathogenic bacteria, patients of septic shock with non-fermenting bacteria had a higher mortality. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Enteral nutrition feeding in Chinese intensive care units: a cross-sectional study involving 116 hospitals

Author

Xing, Juan, Zhang, Zhongheng, Ke, Lu, Zhou, Jing, Qin, Bingyu, Liang, Hongkai, Chen, Xiaomei, Liu, Wenming, Liu, Zhongmin, Ai, Yuhang, Wang, Difeng, Wang, Qiuhui, Zhou, Qingshan, Zhang, Fusen, Qian, Kejian, Jiang, Dongpo, Zang, Bin, Li, Yimin, Huang, Xiaobo, Qu, Yan, Xie, Yinguang, Xu, Donglin, Zou, Zhiqiang, Zheng, Xiangde, Liu, Jianbo, Guo, Feng, Liang, Yafeng, Sun, Qiang, Gao, Hongmei, Liu, Yang, Chang, Ping, Ceng, Aibin, Yang, Rongli, Yao, Gaiqi, Sun, Yun, Wang, Xiaorong, Zhang, Yi, Wen, Yichao, Yu, Jian, Sun, Rongqing, Li, Zhiwei, Yuan, Shiying, Song, Yunlin, Gao, Peiyang, Liu, Haiyan, Zhang, Zhaohui, Wu, Yunfu, Ma, Biao, Guo, Qiang, Shan, Feng, Yang, Mingshi, Li, Hailing, Li, Yuanfei, Lu, Weihua, Wang, Lei, Qian, Chuangyun, Wang, Zhiyong, Lin, Jiandong, Zhang, Rumin, Wan, Peng, Peng, Zhiyong, Gong, Yuqiang, Huang, Linxi, Wu, Guobao, Sun, Jie, Deng, Yijun, Shi, Dongwu, Zhou, Lixin, Zhou, Fachun, Shi, Qindong, Guo, Xiaodong, Liu, Xueyan, Wu, Weidong, Meng, Xiangzhong, Li, Liandi, Chen, Weiwei, Li, Shusheng, Wan, Xianyao, Chao, Zhixin, Zhang, An, Gu, Liming, Chen, Wei, Wu, Jinglan, Zhou, Lihua, Zhang, Zhenhuan, Weng, Yibing, Feng, Yongshun, Yang, Chunli, Feng, Yongjian, Zhao, Sumin, Tong, Fei, Hao, Dong, Han, Hui, Fu, Baocai, Gong, Chuanyong, Li, Zhiping, Hu, Kunlin, Kou, Qiuye, Zhang, Han, Liu, Jie, Fan, Chuming, Zhou, Xin, Chen, Xiumei, Sun, Junli, Zhou, Xuejun, Song, Bin, Sun, Cheng, Zhao, Liyun, Dong, Xinglu, Zhang, Linlin, Tong, Dafei, Pan, Zhiguo, Cai, Chuangjie, Wang, Donghao, Dong, Yingjun, Gong, Yuanqi, Wu, Zhisong, Meng, Xinke, Wang, Ping, and Li, Weiqin

Published
2018
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22. Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial (vol 26, 46, 2022)

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S, van Zanten, Arthur RH, Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, Li, Weiqin, and Trials, Chinese Critical Care Nutr

Published
2022

23. Effect of an Herbal-Based Injection on 28-Day Mortality in Patients With Sepsis: The EXIT-SEP Randomized Clinical Trial

Author

Liu, Songqiao, Yao, Chen, Xie, Jianfeng, Liu, Hong, Wang, Hongliang, Lin, Zhaofen, Qin, Bingyu, Wang, Difen, Lu, Weihua, Ma, Xiaochun, Liu, Yan, Liu, Ling, Zhang, Chi, Xu, Lei, Zheng, Ruiqiang, Zhou, Feihu, Liu, Zhongmin, Zhang, Guoqiang, Zhou, Lixin, Liu, Jian, Fei, Aihua, Zhang, Guoxiu, Zhu, Yimin, Qian, Kejian, Wang, Ruilan, Liang, Yafeng, Duan, Meili, Wu, Dawei, Sun, Rongqing, Wang, Ying, Zhang, Xijing, Cao, Quan, Yang, Mingshi, Jin, Minggen, Song, Yan, Huang, Ling, Zhou, Fachun, Chen, Dechang, Liang, Qun, Qian, Chuanyun, Tang, Zhongzhi, Zhang, Zhong, Feng, Qiming, Peng, Zhiyong, Sun, Renhua, Song, Zhenju, Sun, Yunbo, Chai, Yanfen, Zhou, Lihua, Cheng, Chen, Li, Li, Yan, Xiaoyan, Zhang, Junhua, Huang, Yingzi, Guo, Fengmei, Li, Chuan, Yang, Yi, Shang, Hongcai, and Qiu, Haibo

Abstract

IMPORTANCE: Previous research has suggested that Xuebijing injection (XBJ), an herbal-based intravenous preparation, may reduce mortality among patients with sepsis. OBJECTIVE: To determine the effect of XBJ vs placebo on 28-day mortality among patients with sepsis. DESIGN, SETTING, AND PARTICIPANTS: The Efficacy of Xuebijing Injection in Patients With Sepsis (EXIT-SEP) trial was a multicenter, randomized double-blind, placebo-controlled trial conducted in intensive care units at 45 sites and included 1817 randomized patients with sepsis (sepsis 3.0) present for less than 48 hours. Patients aged 18 to 75 years with a Sequential Organ Failure Assessment score of 2 to 13 were enrolled. The study was conducted from October 2017 to June 2019. The final date of follow-up was July 26, 2019. Data analysis was performed from January 2020 to August 2022. INTERVENTIONS: The patients were randomized to receive either intravenous infusion of XBJ (100 mL, n = 911) or volume-matched saline placebo (n = 906) every 12 hours for 5 days. MAIN OUTCOMES AND MEASURES: The primary outcome was 28-day mortality. RESULTS: Among the 1817 patients who were randomized (mean [SD] age, 56.5 [13.5] years; 1199 [66.0%] men), 1760 (96.9%) completed the trial. In these patients, the 28-day mortality rate was significantly different between the placebo group and the XBJ group (230 of 882 patients [26.1%] vs 165 of 878 patients [18.8%], respectively; P < .001). The absolute risk difference was 7.3 (95% CI, 3.4-11.2) percentage points. The incidence of adverse events was 222 of 878 patients (25.3%) in the placebo group and 200 of 872 patients (22.9%) in the XBJ group. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial among patients with sepsis, the administration of XBJ reduced 28-day mortality compared with placebo. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03238742

Published
2023
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25. Additional file 2 of Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Abstract

Additional file 2. Table S1. Feeding intolerance score. Table S2. Recruitment of patients. Table S3. Proportion of patients receiving enteral and/or parenteral nutrition. Table S4. Proportion of patients receiving enteral nutrition. Table S5. Proportion of patients receiving parenteral nutrition. Table S6. Proportion of enteral nutrition delivered energy in daily energy delivery. Table S7. Target-reaching rate in fed patients from day1 to day7 after enrollment. Table S8. Protein intake from day1 to day7 after enrollment. Table S9. Organ failure-related outcomes. Figure S1. Proportion of enteral nutrition delivered energy in daily energy delivery within the first seven days after enrollment. Figure S2. Target-reaching (more than 70% of the estimated energy target) rate in fed patients for energy delivery from day1 to day7 after enrollment. Figure S3. Daily protein intake from day1 to day7 after enrollment.

Published
2022
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26. Additional file 3 of Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Subjects
health care facilities, manpower, and services
Abstract

Additional file 3. Continued Table 1. Baseline ICU and Patient-Level Characteristics..

Published
2022
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27. Additional file 1 of Actively implementing an evidence-based feeding guideline for critically ill patients (NEED): a multicenter, cluster-randomized, controlled trial

Author

Ke, Lu, Lin, Jiajia, Doig, Gordon S., van Zanten, Arthur R. H., Wang, Yang, Xing, Juan, Zhang, Zhongheng, Chen, Tao, Zhou, Lixin, Jiang, Dongpo, Shi, Qindong, Lin, Jiandong, Liu, Jun, Cheng, Aibin, Liang, Yafeng, Gao, Peiyang, Sun, Junli, Liu, Wenming, Yang, Zhenyu, Zhang, Rumin, Xing, Wei, Zhang, An, Zhou, Zhigang, Zhou, Tingfa, Liu, Yang, Tong, Fei, Wang, Qiuhui, Pan, Aijun, Huang, Xiaobo, Fan, Chuming, Lu, Weihua, Shi, Dongwu, Wang, Lei, Li, Wei, Gu, Liming, Xie, Yingguang, Sun, Rongqing, Guo, Feng, Han, Lin, Zhou, Lihua, Zheng, Xiangde, Shan, Feng, Liu, Jianbo, Ai, Yuhang, Qu, Yan, Li, Liandi, Li, Hailing, Pan, Zhiguo, Xu, Donglin, Zou, Zhiqiang, Gao, Yan, Yang, Chunli, Kou, Qiuye, Zhang, Xijing, Wu, Jinglan, Qian, Chuanyun, Zhang, Weixing, Zhang, Minjie, Zong, Yuan, Qin, Bingyu, Zhang, Fusen, Zhai, Zhe, Sun, Yun, Chang, Ping, Yu, Bo, Yu, Min, Yuan, Shiying, Deng, Yijun, Zhao, Liyun, Zang, Bin, Li, Yuanfei, Zhou, Fachun, Chen, Xiaomei, Shao, Min, Wu, Weidong, Wu, Ming, Zhang, Zhaohui, Li, Yimin, Guo, Qiang, Wang, Zhiyong, Gong, Yuanqi, Song, Yunlin, Qian, Kejian, Feng, Yongjian, Fu, Baocai, Liu, Xueyan, Li, Zhiping, Gong, Chuanyong, Sun, Cheng, Yu, Jian, Tang, Zhongzhi, Huang, Linxi, Ma, Biao, He, Zhijie, Zhou, Qingshan, Yu, Rongguo, Tong, Zhihui, and Li, Weiqin

Subjects
Data_FILES
Abstract

Additional file 1. Protocol and Statistical Analysis Plan.

Published
2022
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32. Additional file 1 of Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

Author

Ma, Penglin, Liu, Jingtao, Shen, Feng, Liao, Xuelian, Xiu, Ming, Zhao, Heling, Zhao, Mingyan, Xie, Jing, Wang, Peng, Huang, Man, Li, Tong, Duan, Meili, Qian, Kejian, Peng, Yue, Zhou, Feihu, Xin, Xin, Wan, Xianyao, Wang, ZongYu, Li, Shusheng, Han, Jianwei, Li, Zhenliang, Ding, Guolei, Deng, Qun, Zhang, Jicheng, Zhu, Yue, Ma, Wenjing, Wang, Jingwen, Kang, Yan, and Zhang, Zhongheng

Abstract

Additional file 1. Supplemental Digital Content for Individualized resuscitation strategy forseptic shock formalized by finite mixture modeling and dynamic treatment regimen.

Published
2021
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33. Additional file 2 of Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

Author

Ma, Penglin, Liu, Jingtao, Shen, Feng, Liao, Xuelian, Xiu, Ming, Zhao, Heling, Zhao, Mingyan, Xie, Jing, Wang, Peng, Huang, Man, Li, Tong, Duan, Meili, Qian, Kejian, Peng, Yue, Zhou, Feihu, Xin, Xin, Wan, Xianyao, Wang, ZongYu, Li, Shusheng, Han, Jianwei, Li, Zhenliang, Ding, Guolei, Deng, Qun, Zhang, Jicheng, Zhu, Yue, Ma, Wenjing, Wang, Jingwen, Kang, Yan, and Zhang, Zhongheng

Subjects
Data_FILES
Abstract

Additional file 2. R code for data analysis.

Published
2021
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39. Active Implementation of an Evidence-Based Nutrition Guideline for Critically Ill Patients Versus Standard Care: A Cluster Randomised, Multicentre, Controlled Trial

Author

Ke, Lu, primary, Lin, Jiajia, additional, Doig, Gordon S., additional, van Zanten, Arthur R.H., additional, Wang, Yang, additional, Xing, Juang, additional, Zhang, Zhongheng, additional, Chen, Tao, additional, Zhou, Lixin, additional, Jiang, Dongpo, additional, Shi, Qindong, additional, Lin, Jiandong, additional, Liu, Jun, additional, Cheng, Aibin, additional, Liang, Yafeng, additional, Gao, Peiyang, additional, Sun, Junli, additional, Liu, Wenming, additional, Yang, Zhenyu, additional, Zhang, Rumin, additional, Xing, Wei, additional, Zhang, An, additional, Zhou, Zhigang, additional, Zhou, Tingfa, additional, Liu, Yang, additional, Tong, Fei, additional, Wang, Qiuhui, additional, Pan, Aijun, additional, Huang, Xiaobo, additional, Fan, Chuming, additional, Lu, Weihua, additional, Shi, Dongwu, additional, Wang, Lei, additional, Li, Wei, additional, Gu, Liming, additional, Xie, Yingguang, additional, Sun, Rongqing, additional, Guo, Feng, additional, Han, Lin, additional, Zhou, Lihua, additional, Zheng, Xiangde, additional, Shan, Feng, additional, Liu, Jianbo, additional, Ai, Yuhang, additional, Qu, Yan, additional, Li, Liandi, additional, Li, Hailing, additional, Pan, Zhiguo, additional, Xu, Donglin, additional, Zou, Zhiqiang, additional, Gao, Yan, additional, Yang, Chunli, additional, Kou, Qiuye, additional, Zhang, Xijing, additional, Wu, Jinglan, additional, Qian, Chuanyun, additional, Zhang, Weixing, additional, Zhang, Minjie, additional, Zong, Yuan, additional, Qin, Bingyu, additional, Zhang, Fusen, additional, Zhai, Zhe, additional, Sun, Yun, additional, Chang, Ping, additional, Yu, Bo, additional, Yu, Min, additional, Yuan, Shiying, additional, Deng, YiJun, additional, Zhao, Liyun, additional, Zang, Bin, additional, Li, Yuanfei, additional, Zhou, Fachun, additional, Chen, Xiaomei, additional, Shao, Min, additional, Wu, Weidong, additional, Wu, Ming, additional, Zhang, Zhaohui, additional, Li, Yimin, additional, Guo, Qiang, additional, Wang, Zhiyong, additional, Gong, Yuanqi, additional, Song, Yunlin, additional, Qian, Kejian, additional, Feng, Yongjian, additional, Fu, Baocai, additional, Liu, Xueyan, additional, Li, Zhiping, additional, Gong, Chuanyong, additional, Sun, Cheng, additional, Yu, Jian, additional, Tang, Zhongzhi, additional, Huang, Linxi, additional, Ma, Biao, additional, He, Zhijie, additional, Zhou, Qingshan, additional, Yu, Rongguo, additional, Tong, Zhihui, additional, and Li, Weiqin, additional

Published
2021
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40. COVID-19 mortality in ICUs associated with critical care staffing

Author

Xi, Jingjing, primary, Zeng, Lin, additional, Li, Shusheng, additional, Ai, Yuhang, additional, He, Xiandi, additional, Kang, Yan, additional, Li, Yimin, additional, Mo, Yanping, additional, Peng, Yue, additional, Qian, Kejian, additional, Qin, Bingyu, additional, Wang, Chunting, additional, Yan, Jing, additional, Zhou, Fachun, additional, Zhang, Hua, additional, and Ma, Penglin, additional

Published
2021
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43. Association between hospital and ICU structural factors and patient outcomes in China: a secondary analysis of the National Clinical Improvement System Data in 2019.

Author

Li, Zhen, Ma, Xudong, Gao, Sifa, Li, Qi, Luo, Hongbo, Sun, Jianhua, Du, Wei, Su, Longxiang, Wang, Lu, Zhang, Qing, Li, Zunzhu, Zhou, Xiang, Liu, Dawei, China National Critical Care Quality Control Center Group, Wang, Xue, Guan, Xiangdong, Kang, Yan, Xiong, Bin, Qin, Bingyu, and Qian, Kejian

Abstract

Background: Hospital and ICU structural factors are key factors affecting the quality of care as well as ICU patient outcomes. However, the data from China are scarce. This study was designed to investigate how differences in patient outcomes are associated with differences in hospital and ICU structure variables in China throughout 2019. Methods: This was a multicenter observational study. Data from a total of 2820 hospitals were collected using the National Clinical Improvement System Data that reports ICU information in China. Data collection consisted of a) information on the hospital and ICU structural factors, including the hospital type, number of beds, staffing, among others, and b) ICU patient outcomes, including the mortality rate as well as the incidence of ventilator-associated pneumonia (VAP), catheter-related bloodstream infections (CRBSIs), and catheter-associated urinary tract infections (CAUTIs). Generalized linear mixed models were used to analyse the association between hospital and ICU structural factors and patient outcomes. Results: The median ICU patient mortality was 8.02% (3.78%, 14.35%), and the incidences of VAP, CRBSI, and CAUTI were 5.58 (1.55, 11.67) per 1000 ventilator days, 0.63 (0, 2.01) per 1000 catheter days, and 1.42 (0.37, 3.40) per 1000 catheter days, respectively. Mortality was significantly lower in public hospitals (β = − 0.018 (− 0.031, − 0.005), p = 0.006), hospitals with an ICU-to-hospital bed percentage of more than 2% (β = − 0.027 (− 0.034, -0.019), p < 0.001) and higher in hospitals with a bed-to-nurse ratio of more than 0.5:1 (β = 0.009 (0.001, 0.017), p = 0.027). The incidence of VAP was lower in public hospitals (β = − 0.036 (− 0.054, − 0.018), p < 0.001). The incidence of CRBSIs was lower in public hospitals (β = − 0.008 (− 0.014, − 0.002), p = 0.011) and higher in secondary hospitals (β = 0.005 (0.001, 0.009), p = 0.010), while the incidence of CAUTIs was higher in secondary hospitals (β = 0.010 (0.002, 0.018), p = 0.015). Conclusion: This study highlights the association between specific ICU structural factors and patient outcomes. Modifying structural factors is a potential opportunity that could improve patient outcomes in ICUs. [ABSTRACT FROM AUTHOR]

Published
2022
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44. Changes in the clinical characteristics of severe Corona Virus Disease 2019 in Jiangxi Province

Author

Yu, Jingjing, primary, Xiang, Tianxin, additional, Xu, Xinping, additional, Zuo, Wei, additional, Zhou, Congyang, additional, Liu, Yu, additional, Liu, Yang, additional, Peng, Dechang, additional, Zhang, Ning, additional, Liu, Fen, additional, Cheng, Na, additional, Xu, Fei, additional, Xie, Siguang, additional, Yang, Jibin, additional, Shao, Yi, additional, Qian, Kejian, additional, and Zhang, Wei, additional

Published
2020
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