A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers
A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers
复制标题
基于生物标志物纵向测量的 COVID-19 预后预测范例。
DOI：
10.1093/bib/bbab206
复制
发表时间：
2021-11-01
影响因子：
9.5
通讯作者：
Zhao, Yang
中科院分区：
文献类型：
--
作者：
Chen, Xin;Gao, Wei;Zhao, Yang
新型冠状病毒病2019 （COVID-19）是一种新兴的、快速演变的危机，预测个体COVID-19患者预后的能力对指导治疗非常重要。在COVID-19患者住院期间反复测量实验室检查，为个性化早期预测预后提供可能。然而，以往的研究主要集中在基于一个时间点的实验室测量的风险预测，忽略了疾病的进展和生物标志物随时间的变化。通过历史回归树（HTREEs）和联合建模技术，我们对1997年COVID-19患者的实验室生物标志物的纵向轨迹进行了建模，并对个体预后进行了动态预测。在发现阶段，基于2020年1月10日至2月18日在同济医院入院的358名COVID-19患者，HTREE模型确定了一组重要变量，包括14个预后生物标志物。利用这些生物标志物在5天、10天和15天的轨迹，联合模型在区分存活和死亡的COVID-19患者方面表现良好（发现集的平均auc为88.81、84.81和85.62%）。预测模型在两个独立的数据集上成功验证（第一个数据集（包括112例患者）验证的平均auc分别为87.61、87.55和87.03%，第二个数据集（包括1527例患者）验证的平均auc分别为94.97、95.78和94.63%）。总之，我们的研究确定了与COVID-19患者预后相关的重要生物标志物，表征了时间到事件的过程，并在个体水平上获得了动态预测。
Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.