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

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.

Predictive models for COVID-19 detection using routine blood tests and machine learning

“…Synthetic Minority Over-sampling Technique allows generating additional data samples through variations of each blood feature 28 , 31 , 33 . A game theory-based Shapley value method can provide a reliable, feature extraction 83 , 84 An ensemble learning based on combining several classification algorithms and generating a final prediction, for instance, by the voting procedure allows improving a predictive model performance 38 , 54 , 85 , 86 , 87 , 88 .…”

“…Their concentration is low in severe COVID-19. [][ []4, 19, 26 []] [] [] [] [][ []28, 29, 30 []] [] [] [] [][ []31, 33, 34 []] [] [] [] [][ []35, 36, 38 []] [] [] [] [][ []39, 40, 41 []] [] [] [] [][ []42, 44, 45 []] [] [] [] 46 , 47 , 49 50 , 51 , 52 54 , 55 , 56 [ 57 ] [][ []34, 39 []] [] [] [] [][ []41, 44 []] [] [] [] [][ []50, 51 []] [] [] [] [][ []52, 55 []] [] [] [] [][ []56, 57 []] [] [] [] MCH Mean Corpuscular Hemoglobin The mean corpuscular hemoglobin is the average mass of HGB per a RBC in a blood sample. [][ []19, 26, 27 []] [] [] [] [][ []28, 29, 30 []] [] [] [] [][ []31, 32, 33 []] [] [] [] [][ []34, 35, 36 []] [] [] [] [][ []37, 46, 52 []] [] [] [], [ 53 ] MCHC Mean Corpuscular Hemoglobin Concentration The mean corpuscular hemoglobin concentration characterizes an average concentration of HGB in a RBC.…”

“…Therefore, it is an early marker of an acute phase of inflammatory response. [][ []4, 19, 26 []] [] [] [] [][ []28, 29, 30 []] [] [] [] [][ []31, 32, 34 []] [] [] [] [][ []35, 37, 39 []] [] [] [] [][ []40, 43, 44 []] [] [] [] [][ []45, 46, 47 []] [] [] [] [][ []49, 50, 51 []] [] [] [] [][ []53, 54, 57 []] [] [] [] [ 58 ] [][ []4, 30 []] [] [] [] [][ []34, 35 []] [] [] [] [][ []37, 39 []] [] [] [] [][ []40, 43 []] [] [] [], [] [] [], 40 , 49 [] [] [] [][ []51, 54 []] [] [] [] [][ []57, 58 []] [] [] [] CREAT Creatinine Creatinine is a chemical waste molecule resulting from muscle metabolism. It is formed mainly in the liver and excreted by the kidneys.…”

“…Higher than normal levels of bilirubin may be associated with liver, bile duct or gallbladder problems. [][ []19, 23, 26 []] [] [] [] [][ []27, 29, 30 []] [] [] [] [][ []31, 32, 34 []] [] [] [] [][ []40, 43, 44 []] [] [] [] [][ []45, 46, 51 []] [] [] [] [][ []54, 59 []] [] [] [] [][ []23, 29 []] [] [] [] [][ []34, 51 []] [] [] [] XDP D-dimer D-dimer is a plasmatic protein essential for dissolving blood clots. D-dimer is considered as a thrombosis marker, its high level indicates increased blood clotting.…”

In-depth biological analysis of alteration in Plasmodium knowlesi-infected red blood cells using a noninvasive optical imaging technique

“…It also was reported that it could be used as an intrinsic marker for cancer diagnosis [ 19 ]. Differences in values from abnormal cells are used to standardize the level of severity by integrating it into the machine learning model [ 20 ]. Thus this technique may become a key clinical decision support tool.…”