Figure 3A |Classification and regression tree (CART) shows that hFwe-Lose expression in patients' nasal swab sample, as well as patients' age are the main predictors of the outcome. The classification tree was generated using all relevant information about patients (hFwe-Lose expression, age, sex, presence of comorbidities (diabetes, COPD, obesity, cardiomyopathy, heart failure, hypertension). All patients were included in the CART analysis (n = 283). The CART algorithm selected hFwe-Lose expression and age as sole factors to determine the patients' outcome. The split cut-offs, which produce tree branches, are aimed to maximize the information gain (decrease of entropy) with each split, and in this way the coefficient of determination increases, and the relative error decreases with each split (Barlin et al., 2013). B | The line plots show saturation of the coefficient of determination, as well as the plateau in X relative error. p-value of the classification and regression tree analysis (for all splits) < 0.01. The first split (hFwe-Lose > 2.45) increased the coefficient of determination by ~45% and reduced the relative error by ~65%. The impact of all following splits on the relative error was irrelevant. The error bars (plot on the right) represent ± SE. C | The Random Forest analysis shows the highest impact of the hFwe-Lose biomarker in the multivariate analysis of outcome prediction. The plot shows the Mean Decrease in Gini coefficients for the factors that were incorporated in the statistical model for the multivariate CART analysis. The Gini coefficient is a measure of the miss-classification rate. The importance of a predictor is assessed by how much the predictor reduced this mis-classification rate. The hFwe-Lose expression has the highest score, followed by age and blood biomarkers. Comorbidities show the least impact on reducing the misclassification rate. D | hFwe-Lose is a sensitive and specific biomarker that predicts poor COVID-19 outcome. The ROC curves illustrate the high sensitivity and (1 - specificity) of FC > 3.17 (for hospitalisation; TPR = 0.77, FPR = 0.03) and FC > 4.44 (for death; TPR = 0.1, FPR = 0.08) threshold levels (AUC = 0.89 and 0.98, respectively) in prediction patients' hospitalisation and death. CI 95% (hospitalisation) - [0.84-0.93]; CI (death) - [0.92-1]. Only retrospective (training, n = 203) patients' cohort was used for the creation of ROC curves. E |Elevated hFwe-Lose expression in nasal swab probes predicts patients' disease outcome. Box plots show an increased expression of hFwe-Lose in nasal swabs of patients, who were hospitalized or died, versus patients who were not hospitalised, for retrospective (training; n = 203) and prospective (validation; n = 80) patients' cohorts. Two-sided Student's t-test was performed, and p-values are presented on the plot. The vertical axis represents hFwe-Lose expression normalized to the mean of non-hospitalized patients. The color refers to the COVID-19 disease outcome prediction: gray for not hospitalised, blue for hospitalised and red for deceased patients. In the retrospective cohort, the outcome prediction was 84% correct in predicting non-hospitalisation; 63% correct in prediction of hospitalisation; 100% correct in death prediction. In the prospective cohort, the outcome prediction was 100% correct in predicting non-hospitalisation; 72% correct in prediction of hospitalisation; 55% correct in death prediction (45% of deceased patients were predicted to be "only" hospitalised; none of deceased patients had "not hospitalised" prediction). Two-sided Student's t-tests were performed (compared to non-hospitalised patients), and p-values are presented on the plot. The central band shows the median, the box indicates the interquartile range, the whiskers extend to the most extreme points within the 1.5-fold distance of the interquartile range above and below the box. F |The confusion matrices and heatmaps visualize the classification performance of hFwe-Lose expression at the selected cut-offs (FC > 3.17 for hospitalisation and FC > 4.44 for death). In the retrospective (training) cohort, 72 out of the total 86 not hospitalised patients were correctly predicted; out of 107 hospitalised patients, 67 were correctly predicted, 19 were predicted to die instead, and 21 were not predicted to be hospitalised; all deceased patients were correctly predicted. In the prospective (validation) cohort, all patients (19) who were not hospitalised patients were correctly predicted; out of 50 hospitalised patients, 36 were correctly predicted, 14 were predicted to be non-hospitalised; out of 10 deceased patients, 6 were correctly predicted, and 5 patients were predicted to be "only" hospitalised. For hospitalisation prediction, positive predictive value (PPV) for retrospective (training) cohort is 83.7%; for prospective (validation) cohort is 87.8%. The negative predictive value (NPV) for retrospective (training) cohort is 67.2%; for prospective (validation) cohort is 64.1%. For death prediction, positive predictive value (PPV) for retrospective (training) cohort is 34.5%; for prospective (validation) cohort is 100%. The negative predictive value (NPV) for retrospective (training) cohort is 100%; for prospective (validation) cohort is 93.2%.