Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
Bioinformatics. 2022 Jun 24;38(Suppl 1):i101-i108. doi: 10.1093/bioinformatics/btac229.
Sepsis is a leading cause of death and disability in children globally, accounting for ∼3 million childhood deaths per year. In pediatric sepsis patients, the multiple organ dysfunction syndrome (MODS) is considered a significant risk factor for adverse clinical outcomes characterized by high mortality and morbidity in the pediatric intensive care unit. The recent rapidly growing availability of electronic health records (EHRs) has allowed researchers to vastly develop data-driven approaches like machine learning in healthcare and achieved great successes. However, effective machine learning models which could make the accurate early prediction of the recovery in pediatric sepsis patients from MODS to a mild state and thus assist the clinicians in the decision-making process is still lacking.
This study develops a machine learning-based approach to predict the recovery from MODS to zero or single organ dysfunction by 1 week in advance in the Swiss Pediatric Sepsis Study cohort of children with blood-culture confirmed bacteremia. Our model achieves internal validation performance on the SPSS cohort with an area under the receiver operating characteristic (AUROC) of 79.1% and area under the precision-recall curve (AUPRC) of 73.6%, and it was also externally validated on another pediatric sepsis patients cohort collected in the USA, yielding an AUROC of 76.4% and AUPRC of 72.4%. These results indicate that our model has the potential to be included into the EHRs system and contribute to patient assessment and triage in pediatric sepsis patient care.
Code available at https://github.com/BorgwardtLab/MODS-recovery. The data underlying this article is not publicly available for the privacy of individuals that participated in the study.
Supplementary data are available at Bioinformatics online.
败血症是全球儿童死亡和残疾的主要原因，每年导致约 300 万儿童死亡。在儿科败血症患者中，多器官功能障碍综合征（MODS）被认为是一个重要的危险因素，其特征是儿科重症监护病房的死亡率和发病率高。最近，电子健康记录（EHRs）的快速普及使得研究人员能够在医疗保健中极大地发展数据驱动的方法，如机器学习，并取得了巨大的成功。然而，仍然缺乏能够准确预测儿科败血症患者从 MODS 恢复到轻度状态的有效机器学习模型，从而帮助临床医生做出决策。
本研究开发了一种基于机器学习的方法，用于预测瑞士儿科败血症研究队列中血培养确诊菌血症儿童在 1 周内从 MODS 恢复到零个或单个器官功能障碍的情况。我们的模型在 SPSS 队列中进行了内部验证，其接收者操作特征曲线下面积（AUROC）为 79.1%，精确召回曲线下面积（AUPRC）为 73.6%，并在另一个在美国收集的儿科败血症患者队列中进行了外部验证，AUROC 为 76.4%，AUPRC 为 72.4%。这些结果表明，我们的模型有可能被纳入电子健康记录系统，并为儿科败血症患者的评估和分诊做出贡献。
代码可在 https://github.com/BorgwardtLab/MODS-recovery 上获得。本文所依据的数据由于参与研究的个人的隐私原因尚未公开。
补充数据可在生物信息学在线获得。
