University of Chinese Academy of Sciences, Beijing, China.
Eur Rev Med Pharmacol Sci. 2021 Jul;25(14):4693-4701. doi: 10.26355/eurrev_202107_26380.
The rapid onset of pediatric sepsis and the short optimal time for resuscitation pose a severe threat to children's health in the ICU. Timely diagnosis and intervention are essential to curing sepsis, but there is a lack of research on the prediction of sepsis at shorter time intervals. This study proposes a predictive model towards real-time diagnosis of sepsis to help reduce the time to first antibiotic treatment.
The dataset used in this paper was obtained from the pediatric intensive care unit of Shanghai Children's Medical Center and consisted of the initial examination records of patients admitted to the hospital. The data included six groups of laboratory tests: medical history, physical examination, blood gas analysis, routine blood tests, serological tests, and coagulation tests. We divided the admission examination into three stages and proposed a sepsis prediction model towards real-time diagnosis based on local information to shorten waiting time for treatment. The model extracts homogeneous features from patient groups in real-time using a graph neural network and uses the deep forest to learn from homogeneous features and laboratory data to give a comprehensive prediction at the current stage. Discriminative features of each stage are used as augmented information for the next phase, finally achieving self-optimization of global judgment, assisting in pre-allocation of medical resources and providing timely medical assistance to sepsis patients.
Based on the first stage, second stage, and full test, the AUCs of our model were 93.63%, 96.73%, and 97.58%, respectively, and the F1-scores were 77.35%, 85.71%, and 86.48%, respectively. The models gave relatively accurate predictions at each stage.
The prediction model toward a real-time diagnosis of sepsis shows more accurate predictions at each stage compared to other control methods. When the first two stages of data are obtained as input, the model accuracy is close to using complete test data, which can help compress the time to diagnosis to about an hour after the test and significantly reduce waiting time.
儿科脓毒症的发病迅速和复苏的最佳时间短暂，这对 ICU 中的儿童健康构成了严重威胁。及时诊断和干预对于治愈脓毒症至关重要，但对于更短时间间隔内脓毒症的预测研究还很缺乏。本研究提出了一种脓毒症实时诊断的预测模型，以帮助缩短首次抗生素治疗的时间。
本文使用的数据来自上海儿童医学中心的儿科重症监护病房，包括入院患者的初始检查记录。数据包括六组实验室检查：病史、体检、血气分析、常规血液检查、血清学检查和凝血检查。我们将入院检查分为三个阶段，并基于局部信息提出了一种实时脓毒症预测模型，以缩短治疗等待时间。该模型使用图神经网络实时从患者群体中提取同质特征，并使用深度森林从同质特征和实验室数据中学习，以给出当前阶段的综合预测。每个阶段的判别特征都用作下一阶段的增强信息，最终实现全局判断的自我优化，辅助医疗资源的预分配，并为脓毒症患者提供及时的医疗援助。
基于第一阶段、第二阶段和全测试，我们的模型的 AUC 分别为 93.63%、96.73%和 97.58%，F1 分数分别为 77.35%、85.71%和 86.48%。模型在每个阶段都给出了相对准确的预测。
与其他控制方法相比，脓毒症实时诊断的预测模型在每个阶段的预测都更加准确。当将前两个阶段的数据作为输入时，模型的准确性接近使用完整测试数据，这有助于将诊断时间压缩到测试后约 1 小时，并显著减少等待时间。
