Guan Yuanfang, Wang Xueqing, Chen Xianghao, Yi Daiyao, Chen Luyao, Jiang Xiaoqian
Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Department of Internal Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
iScience. 2021 Jan 26;24(2):102106. doi: 10.1016/j.isci.2021.102106. eCollection 2021 Feb 19.
Sepsis is a leading cause of death among inpatients at hospitals. However, with early detection, death rate can drop substantially. In this study, we present the top-performing algorithm for Sepsis II prediction in the DII National Data Science Challenge using the Cerner Health Facts data involving more than 100,000 adult patients. This large sample size allowed us to dissect the predictability by age-groups, race, genders, and care settings and up to 192 hr of sepsis onset. This large data collection also allowed us to conclude that the last six biometric records on average are informative to the prediction of sepsis. We identified biomarkers that are common across the treatment time and novel biomarkers that are uniquely presented for early prediction. The algorithms showed meaningful signals days ahead of sepsis onset, supporting the potential of reducing death rate by focusing on high-risk populations identified from heterogeneous data integration.
脓毒症是医院住院患者死亡的主要原因。然而，通过早期检测，死亡率可大幅下降。在本研究中，我们展示了在DII国家数据科学挑战赛中使用涉及超过10万名成年患者的Cerner健康事实数据进行脓毒症II预测的最佳算法。如此大的样本量使我们能够剖析按年龄组、种族、性别和护理环境以及脓毒症发作后长达192小时的可预测性。这个大型数据集还使我们得出结论，平均而言，最后六项生物特征记录对脓毒症的预测具有参考价值。我们识别出了在整个治疗期间都常见的生物标志物以及专门用于早期预测的新型生物标志物。这些算法在脓毒症发作前数天就显示出有意义的信号，支持了通过关注从异构数据整合中识别出的高危人群来降低死亡率的潜力。
