1.Construction of a risk warning model for evacuation associated pulmonary edema in patients with mechanical ventilation for cardiogenic respiratory failure
Hongwang HAO ; Lu XIANG ; Zhinan WANG ; Guangren HU ; Fulian ZHANG
Chinese Journal of Practical Nursing 2025;41(6):444-451
Objective:To explore the influencing factors of evacuation associated pulmonary edema (WIPE) in patients with mechanical ventilation of cardiogenic respiratory failure, and to build a risk warning model based on independent influencing factors.Methods:A total of 220 patients with cardiogenic respiratory failure who were treated and received mechanical ventilation in Chengbei Campus of Hangzhou First People′s Hospital from April 2021 to December 2023 were retrospectively selected by cross-sectional investigation method, and were divided into WIPE group (34 cases) and non WIPE group (186 cases) according to whether the patients had WIPE or not. Clinical data of the patients were analyzed using the hospital electronic medical record system. The influencing factors of WIPE were determined by univariate analysis and multivariate Logistic regression analysis, and the risk early warning model was constructed based on regression analysis. The corresponding nomogram was drawn by R language software, and the predictive efficiency of the model was tested by receiver operating characteristic curve and calibration curve.Results:WIPE group included 18 males and 16 females, aged (65.12±9.28) years. Non WIPE group included 107 males and 79 females, aged (60.25±8.40) years. Multivariate Logistic regression analysis showed that age ( OR=1.072), smoking history ( OR=3.412), acute physiology and chronic health evaluationⅡ( OR=1.184), cardiac function classification ( OR=4.043), shallow rapid breathing index ( OR=1.100), mechanical ventilation time ( OR=1.540), hypertension ( OR=4.903), left ventricular diastolic dysfunction ( OR=5.151) and chronic obstructive pulmonary disease ( OR= 5.536) were independent influencing factors (all P < 0.05). The area under the curve of the risk early warning model constructed based on the above 9 independent influencing factors was 0.938, and the sensitivity and specificity corresponding to the optimal cutoff value of 0.620 were 0.971 and 0.801, respectively, indicating good differentiation ability. The calibration curve results show that the average absolute error was 0.020, the calibration curve fits the ideal curve, and the model calibration performance was good. Conclusions:WIPE in patients with cardiogenic respiratory failure induced by mechanical ventilation is affected by cardiac function status, mechanical ventilation parameters and other factors. The risk early warning model based on the above 9 independent influencing factors has good predictive efficacy, and can provide reference for clinical prevention of WIPE.
2.Correlation analysis of oral cleanliness and secondary pulmonary infection in patients with severe COPD with mechanical ventilation
Hongwang HAO ; Lu XIANG ; Yuecheng GU ; Zhinan WANG ; Guangren HU ; Fulian ZHANG
Chinese Journal of Practical Nursing 2025;41(20):1566-1572
Objective:To investigate the correlation between oral cleanliness and secondary Pulmonary infection in patients with severe chronic obstructive pulmonary disease (COPD) in mechanical ventilation, and to investigate the predictive effect of oral cleanliness on the risk of secondary pulmonary infection.Methods:Using the cross-sectional survey method, the purposeful sampling method was adopted to select 216 patients with severe COPD who were hospitalized in Hangzhou First People′s Hospital from June 2020 to December 2023 and received mechanical ventilation. The oral cleanliness index and general clinical data of patients at admission were collected using the hospital electronic medical record system. The independent influencing factors of secondary lung infection were analyzed by univariate analysis and multivariate Logisitic regression. The predictive value of oral cleanliness index on secondary lung infection was analyzed by patient operating characteristic (ROC) curve.Results:216 patients with severe COPD who underwent mechanical ventilation were included.Patients aged 37-84 (66.81 ± 8.98) years were included, including 125 males and 91 females.Among them, 89 cases developed secondary pulmonary infection, with an infection rate of 41.20%.Univariate analysis and multivariate Logistic regression analysis showed that, Beck Oral Rating Scale (BOAS) score ( OR = 1.371), visual simulation score of oral odor ( OR = 1.405), gum index ( OR = 3.508), plaque index ( OR = 14.357), smoking history ( OR = 6.772), duration of disease ( OR = 1.391), COPD assessment test score ( OR = 1.269) and mechanical ventilation time ( OR = 1.302) were independent factors for secondary pulmonary infection (all P<0.05). ROC curve analysis showed that oral cleanliness index combined with infection prediction was effective (the area under the ROC curve was 0.833) . Conclusions:Oral cleanliness was closely related to secondary pulmonary infection in patients with severe COPD with mechanical ventilation. BOAS score, visual simulation score of oral odor, gingival index and plaque index could predict secondary pulmonary infection independently, and combined test could predict secondary pulmonary infection.
3.Construction of a risk warning model for evacuation associated pulmonary edema in patients with mechanical ventilation for cardiogenic respiratory failure
Hongwang HAO ; Lu XIANG ; Zhinan WANG ; Guangren HU ; Fulian ZHANG
Chinese Journal of Practical Nursing 2025;41(6):444-451
Objective:To explore the influencing factors of evacuation associated pulmonary edema (WIPE) in patients with mechanical ventilation of cardiogenic respiratory failure, and to build a risk warning model based on independent influencing factors.Methods:A total of 220 patients with cardiogenic respiratory failure who were treated and received mechanical ventilation in Chengbei Campus of Hangzhou First People′s Hospital from April 2021 to December 2023 were retrospectively selected by cross-sectional investigation method, and were divided into WIPE group (34 cases) and non WIPE group (186 cases) according to whether the patients had WIPE or not. Clinical data of the patients were analyzed using the hospital electronic medical record system. The influencing factors of WIPE were determined by univariate analysis and multivariate Logistic regression analysis, and the risk early warning model was constructed based on regression analysis. The corresponding nomogram was drawn by R language software, and the predictive efficiency of the model was tested by receiver operating characteristic curve and calibration curve.Results:WIPE group included 18 males and 16 females, aged (65.12±9.28) years. Non WIPE group included 107 males and 79 females, aged (60.25±8.40) years. Multivariate Logistic regression analysis showed that age ( OR=1.072), smoking history ( OR=3.412), acute physiology and chronic health evaluationⅡ( OR=1.184), cardiac function classification ( OR=4.043), shallow rapid breathing index ( OR=1.100), mechanical ventilation time ( OR=1.540), hypertension ( OR=4.903), left ventricular diastolic dysfunction ( OR=5.151) and chronic obstructive pulmonary disease ( OR= 5.536) were independent influencing factors (all P < 0.05). The area under the curve of the risk early warning model constructed based on the above 9 independent influencing factors was 0.938, and the sensitivity and specificity corresponding to the optimal cutoff value of 0.620 were 0.971 and 0.801, respectively, indicating good differentiation ability. The calibration curve results show that the average absolute error was 0.020, the calibration curve fits the ideal curve, and the model calibration performance was good. Conclusions:WIPE in patients with cardiogenic respiratory failure induced by mechanical ventilation is affected by cardiac function status, mechanical ventilation parameters and other factors. The risk early warning model based on the above 9 independent influencing factors has good predictive efficacy, and can provide reference for clinical prevention of WIPE.
4.Correlation analysis of oral cleanliness and secondary pulmonary infection in patients with severe COPD with mechanical ventilation
Hongwang HAO ; Lu XIANG ; Yuecheng GU ; Zhinan WANG ; Guangren HU ; Fulian ZHANG
Chinese Journal of Practical Nursing 2025;41(20):1566-1572
Objective:To investigate the correlation between oral cleanliness and secondary Pulmonary infection in patients with severe chronic obstructive pulmonary disease (COPD) in mechanical ventilation, and to investigate the predictive effect of oral cleanliness on the risk of secondary pulmonary infection.Methods:Using the cross-sectional survey method, the purposeful sampling method was adopted to select 216 patients with severe COPD who were hospitalized in Hangzhou First People′s Hospital from June 2020 to December 2023 and received mechanical ventilation. The oral cleanliness index and general clinical data of patients at admission were collected using the hospital electronic medical record system. The independent influencing factors of secondary lung infection were analyzed by univariate analysis and multivariate Logisitic regression. The predictive value of oral cleanliness index on secondary lung infection was analyzed by patient operating characteristic (ROC) curve.Results:216 patients with severe COPD who underwent mechanical ventilation were included.Patients aged 37-84 (66.81 ± 8.98) years were included, including 125 males and 91 females.Among them, 89 cases developed secondary pulmonary infection, with an infection rate of 41.20%.Univariate analysis and multivariate Logistic regression analysis showed that, Beck Oral Rating Scale (BOAS) score ( OR = 1.371), visual simulation score of oral odor ( OR = 1.405), gum index ( OR = 3.508), plaque index ( OR = 14.357), smoking history ( OR = 6.772), duration of disease ( OR = 1.391), COPD assessment test score ( OR = 1.269) and mechanical ventilation time ( OR = 1.302) were independent factors for secondary pulmonary infection (all P<0.05). ROC curve analysis showed that oral cleanliness index combined with infection prediction was effective (the area under the ROC curve was 0.833) . Conclusions:Oral cleanliness was closely related to secondary pulmonary infection in patients with severe COPD with mechanical ventilation. BOAS score, visual simulation score of oral odor, gingival index and plaque index could predict secondary pulmonary infection independently, and combined test could predict secondary pulmonary infection.
5.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.

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