Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples

Autor: Maria João Cardoso, Angélica Ramos, Paulo H. Santos, Sara Rocha, João Tiago Guimarães, Joana S. Paiva, José F. Alves, Marta Lourenço, Simão P. Faria, Filipe Marques, Cristiana Carpinteiro, Vanessa Pinto, David A. Clifton, Sandra M. Rodrigues, Paula Sampaio, Mehak Mumtaz
Přispěvatelé: Instituto de Saúde Pública da Universidade do Porto
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Diagnostics
Diagnostics, Vol 11, Iss 1309, p 1309 (2021)
Volume 11
Issue 8
ISSN: 2075-4418
Popis: Forecasting COVID-19 disease severity is key to supporting clinical decision making and assisting resource allocation, particularly in intensive care units (ICUs). Here, we investigated the utility of time- and frequency-related features of the backscattered signal of serum patient samples to predict COVID-19 disease severity immediately after diagnosis. ICU admission was the primary outcome used to define disease severity. We developed a stacking ensemble machine learning model including the backscattered signal features (optical fingerprint), patient comorbidities, and age (AUROC = 0.80), which significantly outperformed the predictive value of clinical and laboratory variables available at hospital admission (AUROC = 0.71). The information derived from patient optical fingerprints was not strongly correlated with any clinical/laboratory variable, suggesting that optical fingerprinting brings unique information for COVID-19 severity risk assessment. Optical fingerprinting is a label-free, real-time, and low-cost technology that can be easily integrated as a front-line tool to facilitate the triage and clinical management of COVID-19 patients. This work has received funding from the Portuguese Foundation for Science and Technology, FCT, through the Research4COVID19 project 156_596835053; and from the European Union’s Horizon 2020 Research and Innovation Programme through the INNO4COV-19 project (Grant Agreement No 101016203). This study is part of an activity that has received funding from EIT Health. EIT Health is supported by the European Institute of Innovation and Technology (EIT), a body of the European Union and receives support from the European Union´s Horizon Europe Research and innovation programme. D.A.C was funded by the NIHR Biomedical Research Centre Programme, Oxford.
Databáze: OpenAIRE
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