Fig. 5

Discrimination (a) and calibration (b) of the DOATS score, discrimination (c) and calibration (d) of the DOAT score, and decision curve analyses for both scores (e) in the validation cohort. In the validation cohort, the area under the ROC curves (AUROCs) for (a) the DOATS score and (c) the DOAT score predicting the deterioration of COVID-19 patients during hospitalization were both 0.76 (95% CI 0.69–0.83). The calibration curve analysis revealed that the calibration slope and calibration-in-the-large were 0.80 and 0.002 for the DOATS score (b) and 0.80 and 0.04 for the DOAT score (d), respectively. (e) The graph illustrates the net benefit relative to no treatment in any patient (‘Treat none’) using different treatment approaches. The gray line represents the scenario where no patients are treated, resulting in a net benefit of zero (no true-positive and no false-positive classifications). The black line represents the scenario where all patients are treated. The colored lines correspond to different treatment thresholds based on the predictions of the DOATS score (blue line) and the DOAT score (red line) for the risk of deterioration. This graph demonstrates the expected net benefit when treatment decisions are made based on these different approaches. The decision curve analysis confirmed the clinical practicability of both scores in the validation cohort