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

IF 3.4 3区医学 Q2 HEMATOLOGY Research and Practice in Thrombosis and Haemostasis Pub Date : 2024-07-01 DOI:10.1016/j.rpth.2024.102519

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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.

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一种机器学习模型，用于在弥散性血管内凝血发展到显性阶段之前对其进行早期准确预测

背景最近的研究表明，抗凝疗法治疗脓毒症可望使弥散性血管内凝血（DIC）患者的生存获益。然而，由于显性 DIC 是一种晚期凝血障碍，因此不再认为针对显性 DIC 的抗凝疗法能调节病理进展。因此，在脓毒症诱发的 DIC 发展到显性阶段之前，我们亟需对其进行早期预测的方法。目标变量是根据国际血栓与止血学会（ISTH）显性 DIC 标准对患者是否发展为显性 DIC 进行二元分类。解释变量为脓毒症确诊后 7 天内的基线和时间序列数据。采用光梯度提升机方法构建预测模型。对于对照组，我们评估了日本急症医学协会 DIC 标准和 ISTH 败血症诱发凝血病标准对随后发生明显 DIC 的敏感性和特异性。预测 7 天内出现明显 DIC 的灵敏度、特异性和接收器操作特征曲线下面积在测试队列中分别为 84.4%、87.5% 和 0.867，在验证队列中分别为 95.0%、75.9% 和 0.851。按诊断阈值计算，日本急症医学协会 DIC 标准的灵敏度和特异度分别为 54.7% 和 74.9%，ISTH 败血症诱发凝血病标准的灵敏度和特异度分别为 63.3% 和 71.9%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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