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.
【저자키워드】 COVID-19, machine learning, predictive biomarker, photonics, optical fingerprinting, 【초록키워드】 disease severity, Comorbidities, intensive care unit, COVID-19 severity, Diagnosis, risk, Laboratory, Forecasting, serum, Patient, ICU admission, Clinical management, age, optical, Hospital admission, utility, information, resource, predict, COVID-19 patients, Predictive, Primary outcome, COVID-19 disease severity, clinical decision, ICUs, AUROC, variable, feature, significantly, investigated, facilitate, unique, correlated, to define, 【제목키워드】 Forecasting, Sample,