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.
Highlights
The coronavirus disease 2019 (COVID-19) pandemic has generated a surge in critically ill patients who progress rapidly to respiratory collapse, shock, and multiple organ dysfunction or failure [1–3]
A total of 88 patients admitted at Centro Hospitalar Universitário São João (CHUSJ) with confirmed COVID-19 disease were enrolled in this prospective study (Figure 1)
Serum samples analyzed by intelligent Lab on Fiber (iLoF) were collected at this moment, when the primary outcome of the study, i.e., patient admission to the intensive care unit (ICU), was still unknown
Summary
The coronavirus disease 2019 (COVID-19) pandemic has generated a surge in critically ill patients who progress rapidly to respiratory collapse, shock, and multiple organ dysfunction or failure [1–3]. Severe or critical illness develops approximately one week after the onset of symptoms in about 20% of the patients diagnosed with COVID-19 [4]. The risk for severe disease increases among elderly people, women, and people with underlying chronic health conditions, such as diabetes mellitus, immunosuppression, obesity, cardiovascular, or respiratory disease [5–8]. Young healthy people may become critically ill. Severe COVID-19 disease may be associated with clinical and biochemical signs of inflammation, namely high fever, thrombocytopenia, hyperferritinemia, and increased. There is no biomarker that can predict severe disease at the time of diagnosis
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