An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study
Frontiers in radiology
; 2, 2022.
Article
in English
| EuropePMC | ID: covidwho-2126153
ABSTRACT
Objective:
The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models—radiomics (MRM), clinical (MCM), and combined clinical–radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans.Methods:
We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training,Results:
The three out of the top five features identified usingConclusion:
The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.
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Collection:
Databases of international organizations
Database:
EuropePMC
Type of study:
Observational study
/
Prognostic study
Language:
English
Journal:
Frontiers in radiology
Year:
2022
Document Type:
Article
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