An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

Ruixia Cui; Wenbo Hua; Kai Qu; Heran Yang; Yingmu Tong; Qinglin Li; Hai Wang; Yanfen Ma; Sinan Liu; Ting Lin; Jingyao Zhang; Jian Sun; Chang Liu

doi:10.3389/fmed.2021.775047

An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

Front Med (Lausanne). 2021 Dec 3:8:775047. doi: 10.3389/fmed.2021.775047. eCollection 2021.

Authors

Ruixia Cui^{1

2}, Wenbo Hua³, Kai Qu¹, Heran Yang³, Yingmu Tong^{1

2}, Qinglin Li^{1

2}, Hai Wang^{1

2}, Yanfen Ma⁴, Sinan Liu^{1

2}, Ting Lin^{1

2}, Jingyao Zhang^{1

2

5}, Jian Sun³, Chang Liu^{1

2

5}

Affiliations

¹ Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
² Department of SICU, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
³ School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
⁴ Department of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
⁵ Biobank, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Abstract

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (DIC), we developed an interpretable real-time sequential warning model toward real-world irregular data. Eight machine learning models including novel algorithms were devised to detect SIC and sepsis-associated DIC 8n (1 ≤ n ≤ 6) hours prior to its onset. Models were developed on Xi'an Jiaotong University Medical College (XJTUMC) and verified on Beth Israel Deaconess Medical Center (BIDMC). A total of 12,154 SIC and 7,878 International Society on Thrombosis and Haemostasis (ISTH) overt-DIC labels were annotated according to the SIC and ISTH overt-DIC scoring systems in train set. The area under the receiver operating characteristic curve (AUROC) were used as model evaluation metrics. The eXtreme Gradient Boosting (XGBoost) model can predict SIC and sepsis-associated DIC events up to 48 h earlier with an AUROC of 0.929 and 0.910, respectively, and even reached 0.973 and 0.955 at 8 h earlier, achieving the highest performance to date. The novel ODE-RNN model achieved continuous prediction at arbitrary time points, and with an AUROC of 0.962 and 0.936 for SIC and DIC predicted 8 h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48 h in advance, which helps maximize the time window for early management by physicians.

Keywords: SIC; early real-time prediction; irregular time-series data; machine learning; sepsis-associated DIC.