Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

• Published in: Critical care (London, England) Vol. 25; no. 1; pp. 1 - 16
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 12.07.2021; BioMed Central; BMC
• Subjects: Algorithms; Blood pressure; Care and treatment; Clinical medicine; Clinical trials; Creatinine; Critical care; Diagnosis; Dopamine; Dynamic treatment regime; Fluid resuscitation; Fluid therapy; Hospitals; Infections; Intervention; Laboratories; Machine learning; Methods; Mortality; Respiratory failure; Resuscitation; Sepsis; Septic shock; China
• DOI: 10.1186/s13054-021-03682-7
• ISSN: 1364-8535; 1466-609X

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 (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) 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 (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. 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. Keywords: Sepsis, Dynamic treatment regime, Mortality, Fluid resuscitation