A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers
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A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers. / Chen, Xin; Gao, Wei; Li, Jie; You, Dongfang; Yu, Zhaolei; Zhang, Mingzhi; Shao, Fang; Wei, Yongyue; Zhang, Ruyang; Lange, Theis; Wang, Qianghu; Chen, Feng; Lu, Xiang; Zhao, Yang.
In: Briefings in Bioinformatics, Vol. 22, No. 6, 2021.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers
AU - Chen, Xin
AU - Gao, Wei
AU - Li, Jie
AU - You, Dongfang
AU - Yu, Zhaolei
AU - Zhang, Mingzhi
AU - Shao, Fang
AU - Wei, Yongyue
AU - Zhang, Ruyang
AU - Lange, Theis
AU - Wang, Qianghu
AU - Chen, Feng
AU - Lu, Xiang
AU - Zhao, Yang
N1 - Publisher Copyright: © The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - COVID-19
KW - dynamic risk prediction
KW - longitudinal data
KW - time-to-event
U2 - 10.1093/bib/bbab206
DO - 10.1093/bib/bbab206
M3 - Journal article
C2 - 34081102
AN - SCOPUS:85117793319
VL - 22
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
SN - 1467-5463
IS - 6
ER -
ID: 288667375