An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning - Cui, Ruixia; Hua, Wenbo; Qu, Kai; Yang, Heran; Tong, Yingmu; Li, Qinglin; Wang, Hai; Ma, Yanfen; Liu, Sinan; Lin, Ting; Zhang, Jingyao; Sun, Jian; et al. (Frontiers in Medicine, 2021)
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 towards 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 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 XGBoost model can predict SIC and sepsis-associated DIC events up to 48h earlier with an AUROC of 0.929 and 0.910 respectively, and even reached 0.973 and 0.955 at 8h 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 8h earlier, respectively. In conclusion, our model can predict the sepsis-associated SIC and DIC onset up to 48h in advance, which helps maximize the time window for early management by physicians.