In previous work we have defined our trend template epistemology for clinically significant trends and we have illustrated and tested a program TrenDx that monitors time-ordered process data by matching the data to trend templates. Our initial application domain was pediatric growth monitoring. For this symposium we have explored monitoring hemodynamic and respiratory parameters of intensive care unit patients. This application has highlighted the needs for advances in our representation and monitoring algorithms. In particular, we have added reasoning with uncertainty to the trend template epistemology, and a new control structure allowing numerical ranking of competing trend templates. We also have added persistence heuristics to reduce branching of the different chronologies for how data matches a trend template. We demonstrate these techniques on a set of trend templates that monitor if oxygen handbagging is adequate or compromises venous return.