1.Internet of things follow-up improves blood pressure management in patients with continuous ambulatory peritoneal dialysis
Aihua LI ; Lizhen DENG ; Aijun LAI ; Wanling ZHUO ; Xiushan DENG ; Yinghui DENG ; Mengjun LIANG ; Zongpei JIANG
Chinese Journal of Nephrology 2021;37(12):956-966
Objective:To explore the difference of blood pressure compliance rate in patients with continuous ambulatory peritoneal dialysis (CAPD) in the internet of things (IoT) follow-up and conventional care.Methods:CAPD patients from 3 peritoneal dialysis centers from May 2019 to October 2019 were included in this retrospective cohort study. They were divided into IoT group and conventional care group according to the way of follow-up. The difference in blood pressure compliance rate during 1 year of follow-up between the two groups was observed. The primary outcome was defined as the proportion of patients with blood pressure compliance rate≥85%.Results:A total of 75 patients were included in this study, in during 32 patients in IoT group and 43 patients in conventional care group. The comparison of baseline data between the two groups showed that the dialysis age of patients in IoT group was shorter ( P<0.01). After a median of 9(9, 12) months follow-up, the median blood pressure compliance rate was 85.2% (65.2%, 95.1%), and 25 patients (65.6%) in IoT group had met the target of blood pressure compliance rate≥85%, which was significantly higher than that in the conventional care group (17 cases, 39.5%) ( χ2=4.996, P=0.025). The cumulative probability of the target of blood pressure compliance rate≥85% was 97%, 90%, 90% and 52%, respectively in IoT group, while 95%, 86%, 55% and 34%, respectively in conventional care group after 3, 6, 9 and 12 months of follow-up, and the different between the two groups was significant (Log-rank χ2=4.774, P=0.029). Adjusted for age, sex and dialysis age, the multivariate Cox proportional risk regression model showed that serum creatinine level(for every 1 μmol/L increase, HR=1.002, 95% CI 1.000-1.003, P=0.033), follow-up mode (IoT follow-up vs conventional care, HR=0.023, 95% CI 0.003-0.210, P=0.001), follow-up times (for each additional time, HR=0.879, 95% CI 0.823-0.939, P<0.001) and the rate of weight compliance (for each increase of 1%, HR=0.964, 95% CI 0.939-0.991, P=0.008) was the independent influencing factors for the blood pressure compliance rate<85%. The results of subgroup analysis showed that patients with shorter dialysis age (<10 months) and in the centers where the nurses finished the PD follow-up work as part-time job had better blood pressure control in IoT follow-up. Conclusions:IoT follow-up is helpful to improve CAPD patients' blood pressure compliance rate. Elevated serum creatinine level at baseline is the independent risk factor associated with poor blood pressure compliance. However, IoT follow-up, more follow-up times and the elevated rate of weight compliance are the protective factors for blood pressure compliance. IoT follow-up mode is more recommended for patients with short dialysis age and for dialysis centers where most of the nurses are part-time.
2.Intervention of best possible self for mental health in new recruits during intensive training
Zihao JIN ; Han LAI ; Gongjin CHEN ; Wen HAO ; Aijun ZHAO ; Xuanyun YAN ; Bo LIU ; Li PENG ; Min LI
Journal of Army Medical University 2024;46(8):912-918,封3
Objective To investigate the intervention efficacy of best possible self (BPS)on the mental health of new recruits (including state optimism and pessimism,perceived stress and subjective well-being).Methods A non-randomized controlled trial was conducted on 212 new recruits subjected with cluster sampling from an army unit in a training base for new recruits in September 2023.Based on their organizational structure,they were divided into a study group (n=100,receiving BPS intervention 15 min/d,for 2 consecutive weeks)and a control group[n=112,typical day (TD)intervention,15 min/d,same period].Future Expectancy Scale (FEX),Chinese Perceived Stress Scale (CPSS),Positive and Negative Affect Scale (PANAS ) and Satisfaction with Life Scale (SWLS ) were used to measure the 2 groups of participants at T0 (baseline),T1 (end of the first week of intervention),T2 (end of the second week of intervention)and T3 (1 week after the end of intervention)in order to evaluate the intervention efficacy on above mentioned mental health indicators.Results There were no significant differences in demographic and baseline psychological variables listed above between the 2 groups.However,as the training progressed,obvious differences were observed in the training effects on state pessimism,perceived stress and subjective well-being (including affective and cognitive well-being)between them.When compared with the baseline data (T0),the study group had notably reduced state pessimism (P<0.01)and elevated affective (P<0.001) and cognitive well-being (P<0.001)during T1 and T3,and decreased perceived stress at T1 (P<0.05)and T3 (P<0.001).However,no such changes of above indicators were observed in the control group before and after training.Conclusion A 2-week BPS intervention can effectively reduce state pessimism and perceived stress,promote subjective well-being,and improve mental health in new recruits during new recruit training.
3.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.