Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples
Open Access
- 21 July 2021
- journal article
- research article
- Published by MDPI AG in Diagnostics
- Vol. 11 (8), 1309
- https://doi.org/10.3390/diagnostics11081309
Abstract
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.Funding Information
- Fundação para a Ciência e a Tecnologia (156_596835053)
- Horizon 2020 (101016203)
This publication has 48 references indexed in Scilit:
- Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A reviewComputer Methods and Programs in Biomedicine, 2016
- New Trends on Optical Fiber TweezersJournal of Lightwave Technology, 2015
- Optical Fiber Tweezers Fabricated by Guided Wave Photo-PolymerizationPhotonics, 2015
- Light scattering: A review of particle characterization applicationsParticuology, 2015
- Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signalComputer Methods and Programs in Biomedicine, 2013
- Dynamic analysis of a diffusing particle in a trapping potentialPhysical Review E, 2013
- Generalization of Spectral Flatness Measure for Non-Gaussian Linear ProcessesIEEE Signal Processing Letters, 2004
- Stacked generalizationNeural Networks, 1992
- Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric ApproachBiometrics, 1988
- THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIALBiometrika, 1934