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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

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

Revista

Título: Diagnostics

Vol. 9

Página Final: 1309

Editora: MDPI

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 16