首站-论文投稿智能助手
典型文献
Risk factors and a prediction model for sepsis: A multicenter retrospective study in China ☆
文献摘要:
Background::Sepsis is typically associated with poor outcomes. There are various risk factors and predictive models for sepsis based on clinical indicators. However, these models are usually predictive of all critical patients. This study explored the risk factors for 28-day outcomes of patients with sepsis and developed a prognosis prediction model.Methods::This was a multicenter retrospective analysis of sepsis patients hospitalized in three intensive care units (ICUs) from September 1st 2015, to June 30th 2020. Demographic, clinical history, and laboratory test data were extracted from patient records. Investigators explored the risk factors affecting 28-day sepsis prognosis by univariate analysis. The effects of confounding factors were excluded by multivariate logistic regression analysis, and new joint predictive factors were calculated. A model predicting 28-day sepsis prognosis was constructed through data processing analysis.Results::A total of 545 patients with sepsis were included. The 28-day mortality rate was 32.3%. Risk factors including age, D-dimer, albumin, creatinine, and prothrombin time (PT) were predictive of death from sepsis. The goodness-of-fit value for this prediction model was 0.534, and the area under the receiver operating characteristic curve was 0.7207. Further analysis of the immune subgroups ( n=140) revealed a significant decrease in CD3+, CD4+CD8-, and CD4+CD29+ memory effector T lymphocytes and an increase in CD56+ natural killer (NK) cells in the hypoalbuminemia group compared with the normal albumin group (65.5 vs. 58.3, P=0.005; 41.2 vs. 32.4, P=0.005; 21.8 vs. 17.1, P=0.029; 12.6 vs. 17.6, P=0.004). Conclusions::Risk factors for 28-day sepsis mortality include age, D-dimer, creatinine, PT, and albumin. A decrease in albumin level may exacerbate immunosuppression in patients with sepsis. This study establishes a prediction model based on these indicators, which shows a good degree of calibration and differentiation. This model may provide good predictive value for clinical sepsis prognosis.
文献关键词:
Sepsis;Risk factors;Model;Immunosuppression;Hypoalbuminemia
作者姓名:
Li Ming;Huang Peijie;Xu Weiwei;Zhou Zhigang;Xie Yun;Chen Cheng;Jiang Yihan;Cui Guangqing;Zhao Qi;Wang Ruilan
作者机构:
Department of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Songjiang, Shanghai 201600, China;Department of Critical Care Medicine, Dongtai Hospital Affiliated to Nantong University, Dongtai, Jiangsu 224200, China
引用格式:
[1]Li Ming;Huang Peijie;Xu Weiwei;Zhou Zhigang;Xie Yun;Chen Cheng;Jiang Yihan;Cui Guangqing;Zhao Qi;Wang Ruilan-.Risk factors and a prediction model for sepsis: A multicenter retrospective study in China ☆)[J].重症医学(英文),2022(03):183-188
A类:
Investigators,CD4+CD29+,hypoalbuminemia,Hypoalbuminemia
B类:
Risk,factors,prediction,sepsis,multicenter,retrospective,study,China ,Background,Sepsis,typically,associated,poor,outcomes,There,various,risk,predictive,models,clinical,indicators,However,these,usually,critical,patients,This,explored,day,developed,prognosis,Methods,was,analysis,hospitalized,three,intensive,care,units,ICUs,from,September,1st,June,30th,Demographic,history,laboratory,test,data,were,extracted,records,affecting,by,univariate,effects,confounding,excluded,multivariate,logistic,regression,new,joint,calculated,predicting,constructed,through,processing,Results,total,included,mortality,rate,including,age,dimer,creatinine,prothrombin,PT,death,goodness,fit,value,this,area,under,receiver,operating,characteristic,curve,Further,immune,subgroups,revealed,significant,decrease,CD3+,CD4+CD8,memory,effector,lymphocytes,increase,CD56+,natural,killer,NK,cells,compared,normal,Conclusions,level,may,exacerbate,immunosuppression,establishes,which,shows,degree,calibration,differentiation,provide,Model,Immunosuppression
AB值:
0.495161
相似文献
Development and evaluation of a predictive nomogram for survival in heat stroke patients: a retrospective cohort study
Fei Shao;Xian Shi;Shu-hua Huo;Qing-yu Liu;Ji-xue Shi;Jian Kang;Ping Gong;Sheng-tao Yan;Guo-xingWang;Li-jie Qin;Fei Wang;Ke Feng;Feng-ying Chen;Yong-jie Yin;Tao Ma;Yan Li;Yang Wu;Hao Cui;Chang-xiao Yu;Song Yang;Wei Gan;Sai Wang;Liu-ye-zi Du;Ming-chen Zhao;Zi-ren Tang;Shen Zhao-Department of Emergency Medicine,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China;Department of Emergency Medicine,Hebei Yanda Hospital,Langfang 065201,China;Department of Emergency Medicine,Beijing Huairou Hospital,Beijing 101400,China;Department of Emergency Medicine,the Second Hospital of Hebei Medical University,Shijiazhuang 050000,China;Department of Emergency Medicine,the Second Affiliated Hospital of Shandong First Medical University,Tai'an 271000,China;Department of Emergency Medicine,the First Affiliated Hospital of Dalian Medical University,Dalian 116011,China;Department of Emergency Medicine,China-Japan Friendship Hospital,Beijing 100029,China;Department of Emergency Medicine,Beijing Friendship Hospital,Beijing 100050,China;Department of Emergency Medicine,Henan Provincial People's Hospital,Zhengzhou 450003,China;Department of Emergency Medicine,Beijing Tsinghua Changung Hospital,Beijing 102218,China;Department of Emergency Medicine,General Hospital of Ningxia Medical University,Yinchuan 750004,China;Department of Emergency Medicine,the Affiliated Hospital of Inner Mongolia Medical University,Hohhot 010050,China;Department of Emergency Medicine,the Second Hospital of Jilin University,Changchun 130021,China;Department of Emergency Medicine,the First Hospital of China Medical University,Shenyang 110001,China;Department of Emergency Medicine,the Second Hospital of Shanxi Medical University,Taiyuan 030001,China;Department of Big Data Research,Goodwill Hessian Health Technology Co.,Ltd.,Beijing 100085,China;School of Public Health,Peking University,Beijing 100083,China;Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation,Beijing 100020,China;Department of Critical Care Medicine,Beijing Friendship Hospital,Capital Medical University,Beijing 100050,China
A Nomogram Model for Predicting Type-2 Myocardial Infarction Induced by Acute Upper Gastrointestinal Bleeding
Gui-jun JIANG;Ru-kai GAO;Min WANG;Tu-xiu XIE;Li-ying ZHAN;Jie WEI;Sheng-nan SUN;Pei-yu JI;Ding-yu TAN;Jing-jun LYU-Emergency Department,Renmin Hospital of Wuhan University,Wuhan 430060,China;Department of Critical Care Medicine,Renmin Hospital of Wuhan University,Wuhan 430060,China;Wuhan Britain-China School,Wuhan 430022,China;Medical College of Wuhan University of Science and Technology,Wuhan 430065,China;Department of General Practice,Renmin Hospital of Wuhan University,Wuhan 430060,China;Emergency Department,Qilu Hospital of Shandong University(Qingdao),Qingdao 266000,China;Emergency Department,Clinical Medical College of Yangzhou University,Northern Jiangsu People's Hospital,Yangzhou 225001,China
Prognostic nomogram incorporating radiological features for predicting overall survival in patients with AIDS-related non-Hodgkin lymphoma
Li Xueqin;Pan Ziang;Wang Xing;Hu Tianli;Ye Wen;Jiang Dongmei;Shen Wen;Liu Jinxin;Shi Yuxin;Xia Shuang;Li Hongjun-Radiological Department, Beijing You'an Hospital Affiliated of Capital Medical University, Beijing 100069, China;Neurological Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China;Radiological Department, The Eighth People's Hospital of Guangzhou, Guangzhou, Guangdong 510060, China;Radiological Department, Shanghai Public Health Clinical Center, Affiliated of Fudan University, Shanghai 201058, China;Radiation Department, Tianjin First Central Hospital, Tianjin 300170, China;Radiological Department, Tianjin First Central Hospital, Tianjin 300192, China
Risk of gestational diabetes recurrence and the development of type 2 diabetes among women with a history of gestational diabetes and risk factors: a study among 18 clinical centers in China
Wei Yumei;Juan Juan;Su Rina;Song Geng;Chen Xu;Shan Ruiqin;Li Ying;Cui Shihong;Fan Shangrong;Feng Ling;You Zishan;Meng Haixia;Cai Yan;Zhang Cuilin;Yang Huixia-Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing 100034, China;Department of Obstetrics, Tianjin Central Obstetrics and Gynecology Hospital, Tianjin 300199, China;Department of Obstetrics, Jinan Maternal and Child Health Hospital, Jinan, Shandong 250000, China;Department of Obstetrics, Dalian Maternity Hospital, Dalian, Liaoning 116033, China;Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China;Department of Obstetrics, Shenzhen Peking University Hospital, Shenzhen, Guangdong 518036, China;Department of Obstetrics and Gynecology, Tongji Hospital Affiliated to Huazhong University of Science and Technology, Wuhan, Hubei 430030, China;Department of Obstetrics and Gynecology, Suzhou Jiulong Hospital Affiliated to Shanghai Jiaotong University, Suzhou, Jiangsu 320571, China;Department of Obstetrics, Affiliated Hospital of Inner Mongolia Medical University, Huhhot, Inner Mongolia 010050, China;Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China;Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20817, USA
Quality metrics and outcomes among critically ill patients in China: results of the national clinical quality control indicators for critical care medicine survey 2015-2019
Rui Xi;Dong Fen;Ma Xudong;Su Longxiang;Shan Guangliang;Guo Yanhong;Long Yun;Liu Dawei;Zhou Xiang-Department of Critical Care Medicine, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100029, China;Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing 100029, China;Department of Medical Administration, National Health Commission of the People’s Republic of China, Beijing 100044, China;Department of Epidemiology and Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences (CAMS) & School of Basic Medicine, Peking Union Medical College, Beijing 100005, China
Establishment and Evaluation of a Prediction Model of BLR for Severity in Coronavirus Disease 2019
He Zebao;Rui Fajuan;Yang Hongli;Ge Zhengming;Huang Rui;Ying Lingjun;Zhao Haihong;Wu Chao;Li Jie-Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317099, China;Department of Infectious Diseases, Enze Hospital, Taizhou Enze Medical Center (Group), Taizhou, Zhejiang 318053, China;Department of Infectious Disease, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, Shandong 250021, China;Department of Infectious Disease, Cheeloo College of Medicine, Shandong University, Ji’nan, Shandong 250012, China;Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu 210008, China;Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, Jiangsu 210033, China
机标中图分类号,由域田数据科技根据网络公开资料自动分析生成,仅供学习研究参考。