单位：[1]Department of Critical Care Medicine, Guiqian International General Hospital,Guiyang, People’s Republic of China[2]Department of Critical Care Medicine,The 8th Medical Center of Chinese, PLA General Hospital, Beijing 100091, People’sRepublic of China[3]Department of Intensive Care Unit, Guizhou MedicalUniversity Affiliated Hospital, Guiyang, People’s Republic of China[4]Departmentof Critical Care Medicine, West China Hospital of Sichuan University,Chengdu, People’s Republic of China四川大学华西医院[5]Department of Intensive Care Unit,The First Hospital of Jilin University, Changchun, People’s Republic of China[6]Department of Critical Care Medicine, Hebei General Hospital, Shijiazhuang,People’s Republic of China[7]Department of Critical Care Medicine, The FirstAffiliated Hospital of Harbin Medical University, Harbin, People’s Republicof China[8]General Intensive Care Unit Department, The First Affiliated Hospitalof Fujian Medical University, Fuzhou, People’s Republic of China[9]Departmentof Critical Care Medicine, Fu Xing Hospital, Capital Medical University, Beijing,People’s Republic of China[10]General Intensive Care Unit, Second AffiliatedHospital of Zhejiang University, Hangzhou, People’s Republic of China[11]Department of Critical Care Medicine, Beijing Tongren Hospital, CapitalMedical University, Beijing, People’s Republic of China首都医科大学附属同仁医院[12]Department of CriticalCare Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing,People’s Republic of China首都医科大学附属北京友谊医院[13]Department of Critical Care Medicine, The FirstAffiliated Hospital of Nanchang University, Nanchang, People’s Republicof China[14]Department of Critical Care Medicine, The Third Xiangya Hospital,Central South University, Changsha, People’s Republic of China[15]Departmentof Critical Care Medicine, Chinese PLA General Hospital, Beijing, People’sRepublic of China[16]Surgical Intensive Care Unit, Beijing Chao‑Yang Hospital,Capital Medical University, Beijing, People’s Republic of China[17]The First AffiliatedHospital of Dalian Medical University, Dalian, People’s Republic of China[18]Department of Intensive Care, Peking University Third Hospital, Beijing,People’s Republic of China[19]Department of Emergency, Tongji Hospital,Tongji Medical College, Huazhong University of Science and Technology,Wuhan, People’s Republic of China华中科技大学同济医学院附属同济医院[20]Department of Critical Care Medicine,The 8th medical Center of Chinese, PLA General Hospital, Beijing, People’sRepublic of China[21]Department of Critical Care, Beijing PingGu Hospital,Capital Medical University, Beijing, People’s Republic of China[22]IntensiveCare Unit, The Hospital of Shunyi District, Beijing, People’s Republic of China[23]Department of Critical Care Medicine, The 4th Medical Center of Chinese,PLA General Hospital, Beijing, People’s Republic of China[24]Departmentof Critical Care Medicine, Shandong ProvincialHospital, Affiliated to ShandongFirst Medical University, Jinan, People’s Republic of China[25]Departmentof Critical Care, Beijing Luhe Hospital, Capital Medical University, Beijing, People’sRepublic of China[26]Department of Critical Care, Beijing Miyun Hospital,Beijing, People’s Republic of China[27]Intensive Care Unit, Beijing ChangpingDistrict Hospital, Beijing, People’s Republic of China[28]Department of CriticalCare Medicine, West China Hospital of Sichuan University, Chengdu, People’sRepublic of China四川大学华西医院[29]Department of Emergency Medicine, Sir Run Run ShawHospital, Zhejiang University School of Medicine, Hangzhou 310016, People’sRepublic of China
BackgroundSeptic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class.MethodsPatients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset.ResultsA total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion.ConclusionsSeptic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.
基金：
Yilu "Gexin"-Fluid Therapy Research Fund Project [YLGX-ZZ-2020005]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81901929]
第一作者单位：[1]Department of Critical Care Medicine, Guiqian International General Hospital,Guiyang, People’s Republic of China
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Ma Penglin,Liu Jingtao,Shen Feng,et al.Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen[J].CRITICAL CARE.2021,25(1):doi:10.1186/s13054-021-03682-7.
APA：
Ma Penglin,Liu Jingtao,Shen Feng,Liao Xuelian,Xiu Ming...&Zhang Zhongheng.(2021).Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen.CRITICAL CARE,25,(1)
MLA：
Ma Penglin,et al."Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen".CRITICAL CARE 25..1(2021)
医学