We propose database-aware regression methods for extrapolation from few measurements in the context of quantitative prognosis. The idea is to leverage a database of patients with similar conditions to increase the effective number of samples when we train a predictive model. Applying the proposed method to a database of glaucoma patients, we were able to predict the disease condition at a future time point significantly more accurately than the conventional patient-wise linear regression approach. In fact, our prediction was 50% more accurate than the conventional approach when three or less measurements were available and with only two measurements at least as accurate as the conventional approach with six measurements. Moreover, the proposed method can provide spatially localized prediction and also the (localized) speed of progression, which are valuable for doctors in making decisions.