Figure 1
Figure 1. Performance of the CSSCD model and total leukocyte count as predictors in the DNC. (A) Receiver operating characteristic (ROC) curve for the prediction of adverse events in the DNC by the multivariable CSSCD model. The x-axis indicates the false positive rate (1 − specificity). The y-axis indicates sensitivity (the proportion of patients who were correctly classified). The area under the ROC curve is 0.409 (95% CI, 0.308-0.510; P = .161). Therefore, the CSSCD model was not better than prediction by chance. (B) ROC curve for prediction of adverse events in the DNC by leukocyte count as a single predictor. The area under the ROC curve is 0.634 (95% CI, 0.517-0.752; P = .039). Diagonal segments indicate ties.

Performance of the CSSCD model and total leukocyte count as predictors in the DNC. (A) Receiver operating characteristic (ROC) curve for the prediction of adverse events in the DNC by the multivariable CSSCD model. The x-axis indicates the false positive rate (1 − specificity). The y-axis indicates sensitivity (the proportion of patients who were correctly classified). The area under the ROC curve is 0.409 (95% CI, 0.308-0.510; P = .161). Therefore, the CSSCD model was not better than prediction by chance. (B) ROC curve for prediction of adverse events in the DNC by leukocyte count as a single predictor. The area under the ROC curve is 0.634 (95% CI, 0.517-0.752; P = .039). Diagonal segments indicate ties.

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