A machine learning model for early and accurate prediction of overt disseminated intravascular coagulation before its progression to an overt stage

Abstract

Background

Recent studies suggested an expected survival benefit associated with anticoagulant therapies for sepsis in patients with disseminated intravascular coagulation (DIC). However, anticoagulant therapies for overt DIC are no longer assumed to regulate pathologic progression as overt DIC is a late-phase coagulation disorder. Therefore, methods for early prediction of sepsis-induced DIC before its progression to an overt stage are strongly required.

Objectives

We aimed to develop a prediction model for overt DIC using machine learning.

Methods

This retrospective, observational study included adult septic patients without overt DIC. The objective variable was binary classification of whether patients developed overt DIC based on International Society on Thrombosis and Haemostasis (ISTH) overt DIC criteria. Explanatory variables were the baseline and time series data within 7 days from sepsis diagnosis. Light Gradient Boosted Machine method was used to construct the prediction model. For controls, we assessed sensitivity and specificity of Japanese Association for Acute Medicine DIC criteria and ISTH sepsis-induced coagulopathy criteria for subsequent onset of overt DIC.

Results

Among 912 patients with sepsis, 139 patients developed overt DIC within 7 days from diagnosis of sepsis. Sensitivity, specificity, and area under the receiver operating characteristic curve for predicting onset of overt DIC within 7 days were 84.4%, 87.5%, and 0.867 in the test cohort and 95.0%, 75.9%, and 0.851 in the validation cohort, respectively. Sensitivity and specificity by the diagnostic thresholds were 54.7% and 74.9% for Japanese Association for Acute Medicine DIC criteria and 63.3% and 71.9% for ISTH sepsis-induced coagulopathy criteria, respectively.

Conclusion

Compared with conventional DIC scoring systems, a machine learning model might exhibit higher prediction accuracy.