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.
Highlights
Sepsis is a lethal disease caused by a dysregulated host response in an infected state [1]
The exclusion criteria were as follows: [1] patients with disseminated intravascular coagulation (DIC) onset within 24 h of admission; [2] patients were affected by hematologic tumors; [3] patients suffering from cirrhosis, acute liver failure, with liver function up to Child C; [4] patients treated with radiotherapy or chemotherapy; [5] patients with admission diagnosis of combat trauma, traumatic coagulopathy; [6] patients with pregnancy or perinatal complications
We developed several state-of-the-art models that are widely used as follows: [1] Classic machine learning models: Logistic regression (LR) [24] and support vector machines (SVM) [25], which are the most commonly used algorithms in existing research; [2] Enhanced machine learning models: gradient boosting machine (LightGBM) [26] and XGBoost [27], which are widely regarded as the best algorithm for data prediction and are adopted by many competition winning models in the field of machine learning; [3] Classic deep learning models: Recurrent Neural Network (RNN) [21] and long short-term memory network (LSTM) [28], which are the most commonly chosen deep learning models in time-series data, which have shown excellent performance in several time series studies; [4] Improved deep learning models: RNN-Decay and Ordinary Differential Equations-Recurrent Neural Networks (ODE-RNN)
Summary
Sepsis is a lethal disease caused by a dysregulated host response in an infected state [1]. Sepsis-induced coagulopathy (SIC) mortality reaches to 23.1% [4], while the mortality rate of sepsis-associated DIC is more than twice that of simple sepsis patients [5, 6]. Sepsis-associated DIC was diagnosed by the two-step sequential approach of SIC and ISTH overt-DIC criteria, which is a late-phase coagulation disorder that should be detected early [8]. Studies have shown that anticoagulation is ineffective in both sepsis and SIC patients, but effective in sepsis-induced DIC patients [10,11,12]. Early recognition of sepsis-associated DIC is more important than SIC, while there are currently no studies on sequential prediction of sepsis-associated DIC after SIC alerts.
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