Internal Dataset

Self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients

UID: 10415
* Corresponding Author

Description
We developed a framework for continuously monitoring and updating prognostic models and apply it to predict 28-day survival in COVID-19 patients. We use demographic, laboratory, and clinical data from electronic health records of 34912 hospitalized COVID-19 patients from March 2020 until May 2022 and compare three modeling methods. Model calibration performance drift is immediately detected with minor fluctuations in discrimination. The overall calibration on the prospective validation cohort is significantly improved when comparing the dynamically updated models against their static counterparts.
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Model code
Accession #: 10.5281/zenodo.7249622

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Accession #: 10.7303/syn39792658.1

Associated Publications
Data Type
Software Used
MatLab
R
Dataset Format(s)
CSV, Microsoft Word, M (MatLab)
Grant Support
ME-1606-35555/Patient-Centered Outcomes Research Institute
Other Resources
NOCOS Calculator

Web calculator implementation of model