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

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Faria, SP (Author) Other; Carpinteiro, C (Author) Other; Pinto, V (Author) Other; Rodrigues, SM (Author) Other; Alves, J (Author) Other; Marques, F (Author) Other; Lourenco, M (Author) Other; Santos, PH (Author) Other; Ramos, A (Author) Other; Cardoso, MJ (Author) Other; guimaraes, jt (Author) FMUP; Rocha, S (Author) Other; Sampaio, P (Author) Other; Clifton, DA (Author) Other; Mumtaz, M (Author) Other; Paiva, JS (Author) Other

Journal

Title: Diagnostics

Vol. 9

Final page: 1309

Publisher: MDPI

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00V-72T

DOI: 10.3390/diagnostics11081309

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16