We are all individuals with unique characteristics. The applies not only to our appearance, but also to our biomarker levels. Most biomarker studies compare the levels of a particular marker in diseased and healthy cases, then separate the two populations through use of an absolute cut-off level. Often this results in an overlap between disease levels and healthy states because some healthy people have a naturally high level and some with the disease could be naturally low. What actually matters in detecting disease is spotting a change from a healthy baseline as the disease starts to form.
Abcodia’s approach is to assess biomarker levels over time within an individual. This allows the natural level of the person to be set as their own baseline and for a change from that baseline to be detected. In this way, sensitivity is improved by detecting those cases that would not normally reach a population cut off, specificity is increased by discounting outliers that are not changing and the disease is detected earlier through frequent testing (eg annually) and spotting the inflection point.
The ROCA Test is an example of a longitudinal algorithm-based test that significantly improves the performance of CA-125 for ovarian cancer screening.