5%, neverless sensitivity is 95.2% (CI = 92.8�C96.8%). (B) ROC curve (sensitivity versus 1��specificity) … Table 3 Summary of tests results compared to clinical status. Effect of menopausal status To explore the effect of menopausal status on the algorithm, we divided the population according to menopausal status that was given by the women when the sample was obtained. Of the 238 women reported to be post-menopausal, 131 were ��patients��, and 107 were ��controls�� (total of 238). Using this subpopulation only, new models were created using 4 antigens and age (see Table S5 in the Supplementary Data online��List of the final models used for separation for post-menopausal women). Of the 238 samples in the data set, only 193 samples remained with non-missing values, and resulted in 96.2% sensitivity and 52.
8% specificity (Table 3). The total AUC for this sub-population was 84% (Fig. 4B). Final classification as well as the model used for each sample is shown in Table S6 (Supplementary Data online��Prediction given to each sample after applying the models for post-menopausal sub population). Using the method for clinical status prediction Our objective was to further validate the method in order to predict the status of blinded samples. To achieve this objective, we utilized the largest subset that contained the same antigens and had no missing values. A total of 252 samples, with 143 patients and 109 healthy controls, all shared the same 4 antigens (Antigens no. 016; 080; 095; 115). We divided the set into two separate groups, a training set containing 94 patients and 110 healthy controls, and a prediction set containing 15 patients and 33 healthy controls.
We used only one model to establish a cutoff point for separation between the groups in the training set and applied separating criteria on the prediction subset. The training set, tuned to 94.7% sensitivity and 61.8% specificity, resulted in a cutoff point of 0.4, above which the subject was considered as a patient. This cutoff criteria was applied to the prediction set, giving a sensitivity of 100% and specificity of 45.4%, as shown in Table 4 and in Table S8 (Supplementary Data online��Blinded samples prediction using a single model). The ROC for this subset is shown in Figure 5, for the training set (Fig. 5A) and the predictions set (Fig. 5B). Figure 5 (A) ROC curve (sensitivity versus 1��specificity) of the 152 samples in the training set.
The AUC is 84.5% (CI = 78.6�C89.0%). At specificity of 61.8%, sensitivity Batimastat is 94.7% (CI = 88.0�C98.3%). (B) ROC curve (sensitivity versus 1��specificity) … Table 4 Summary of tests results for blind predictions. Discussion In this study, we tested 546 samples using ratios between different AAbs to a panel of biomarkers for each sample rather than using traditional cut-off thresholds for AAbs.