11�C0.15). Table 8 Calculated reproducibility values for selected groups The use of MBT as a tool to avoid the need for liver biopsy Necroinflammation By using a proprietary algorithm that includes breath-test parameters, age and other patient data to differentiate intrahepatic inflammation more (HAIa + HAIb + HAIc + HAId �� 4 vs > 4) for chronic HCV patients with NALT, an AUC of 0.90 was achieved (Fig. 2). Setting a threshold on the point of best agreement (at 83%) results in sensitivity of 82% and specificity of 84%. At the dataset��s prevalence of 68%, the positive predictive value (PPV) was 92% and the negative predictive value (NPV) was 69%. Assuming a prevalence of 45.5%, this would lead to a PPV of 82% and an NPV of 85%. Fig.

2 Model to differentiate between HAIa + HAIb + HAIc + HAId �� 4 vs HAIA + HAIB + HAIC + HAID > 4: A proprietary algorithm that includes breath-test parameters, age and other patient data to differentiate intrahepatic inflammation (HAIa + … Fibrosis By using an algorithm that includes breath-test parameters, age and other patient data, 67% of liver biopsies performed in the patient group could have been avoided (Fig. 3). This algorithm achieved an AUC of 0.92, with a sensitivity of 91% and a specificity of 88%, a PPV of 88% and an NPV of 91%. Thirty-four patients were identified as having significant fibrosis, including four false positives: two with a HAI fibrosis score of 2, and an additional two with a score of 1. Thirty-three patients were identified as having nonsignificant fibrosis, including three false negatives: two with a HAI fibrosis score of 3 and one with a score of 5.

There was no correlation between age or BMI and MBT scores for patients with the same histological score. Fig. 3 Receiver operating characteristic (ROC) curve describing performance of the 67% patients where significant/nonsignificant fibrosis was determined: Using Charles E. Metz ROCKIT 1.1B2 provides the following results: binormal parameters and area under the … Applying the same proprietary algorithm developed to differentiate significant from nonsignificant fibrosis on the healthy volunteer group combined with the significant fibrosis group (n = 150), 67% of the tested subjects (n = 98) would get an answer (Fig. 4). This algorithm achieved an AUC of 0.92, with a sensitivity of 91% and a specificity of 88%, a PPV of 79% and NPV of 95%.

Thirty-eight subjects were identified as having significant fibrosis, including eight false positives. Sixty subjects were identified as having nonsignificant fibrosis including three AV-951 false negatives; two with a HAI fibrosis score of 3 and one with a score of 5. Fig. 4 By using the same proprietary algorithm developed to differentiate significant from nonsignificant fibrosis, 65% of the tested subjects would get an answer. Using Charles E. Metz ROCKIT 1.1B2 provides the following results: binormal parameters and area …