Key result
A convolutional neural network model using time-series electronic health record data accurately predicted the risk of disseminated intravascular coagulation in ICU patients, achieving an AUC of 0.986.
Why the study?
Early prediction of DIC risk is an urgent clinical need to reduce adverse outcomes, but effective approaches and models to identify early DIC are still lacking.
Does a CNN-based time series model accurately predict the risk of DIC in ICU patients?
Observational (n=6,631)
No
Does a CNN-based time series model accurately predict the risk of DIC in ICU patients?
Absolute Event Rate: 0.986% vs 0.851%
p-value: p=<0.001
A CNN-based time series model using electronic health record data can accurately predict the risk of DIC in ICU patients, potentially enabling earlier intervention.
Disseminated intravascular coagulation (DIC) is a complex, life-threatening syndrome associated with the end-stage of different coagulation disorders. Early prediction of the risk of DIC development is an urgent clinical need to reduce adverse outcomes. However, effective approaches and models to identify early DIC are still lacking. In this study, a novel interpretable deep learning based time series is used to predict the risk of DIC. The study cohort included ICU patients from a 4300-bed academic hospital between January 1, 2019, and January 1, 2022. Experimental results show that our model achieves excellent performance (AUC: 0.986, Accuracy: 95.7%, and F1:0.935). Gradient-weighted Class Activation Mapping (Grad-CAM) was used to explain how predictive models identified patients with DIC. The decision basis of the model was displayed in the form of a heat map. The model can be used to identify high-risk patients with DIC early, which will help in the early intervention of DIC patients and improve the treatment effect.
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Yang et al. (2022) conducted an observational in Disseminated Intravascular Coagulation (DIC) (n=6,631). Convolutional neural network (CNN) model vs. XGBoost model was evaluated on Area under the receiver operating characteristic curve (AUC) for DIC prediction (p=<0.001). A convolutional neural network model using time-series electronic health record data accurately predicted the risk of disseminated intravascular coagulation in ICU patients, achieving an AUC of 0.986.
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