获取途径

作者

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 Zhang, Zhongheng

作者单位

⁴Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China ¹⁸Department of Intensive Care, Peking University Third Hospital, Beijing, China ⁷Department of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China ⁹Department of Critical Care Medicine, Fu Xing Hospital, Capital Medical University, Beijing, China ⁵Department of Intensive Care Unit, The First Hospital of Jilin University, Changchun, China ¹¹Department of Critical Care Medicine, Beijing Tongren Hospital, Capital Medical University, Beijing, China ²⁰Department of Critical Care Medicine, The 8th medical Center of Chinese, PLA General Hospital, Beijing, China ¹⁰General Intensive Care Unit, Second Affiliated Hospital of Zhejiang University, Hangzhou, China ²³Department of Critical Care Medicine, The 4th Medical Center of Chinese, PLA General Hospital, Beijing, China ²⁸Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu, China ¹²Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing, China ¹⁷The First Affiliated Hospital of Dalian Medical University, Dalian, China ²¹Department of Critical Care, Beijing PingGu Hospital, Capital Medical University, Beijing, China ⁶Department of Critical Care Medicine, Hebei General Hospital, Shijiazhuang, China ²⁶Department of Critical Care, Beijing Miyun Hospital, Beijing, China ¹⁶Surgical Intensive Care Unit, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China ²⁹Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China ²⁷Intensive Care Unit, Beijing Changping District Hospital, Beijing, China ¹⁹Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China ¹³Department of Critical Care Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China ²⁵Department of Critical Care, Beijing Luhe Hospital, Capital Medical University, Beijing, China ⁸General Intensive Care Unit Department, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China ²²Intensive Care Unit, The Hospital of Shunyi District, Beijing, China ¹⁴Department of Critical Care Medicine, The Third Xiangya Hospital, Central South University, Changsha, China ¹⁵Department of Critical Care Medicine, Chinese PLA General Hospital, Beijing, China ¹Department of Critical Care Medicine, Guiqian International General Hospital, Guiyang, China ³Department of Intensive Care Unit, Guizhou Medical University Affiliated Hospital, Guiyang, China ²⁴Department of Critical Care Medicine, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan, China ²Department of Critical Care Medicine, The 8th Medical Center of Chinese, PLA General Hospital, Beijing, 100091, China

刊名

Critical Care

年份

2021

卷号

Vol.25 No.1

ISSN

1364-8535

摘要

Background: Septic 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. Methods: Patients 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...更多

Background: Septic 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. Methods: Patients 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 model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database dataset. Results: A 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 accounted for the majority of patients over all days; class 2 had the highest severity of illness; class 3 was characterized by renal dysfunction; class 4 was characterized by respiratory failure; and class 5 was characterized by the lowest mortality rate . 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. Conclusions: Septic 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. © 2021, The Author.收起

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