Use of disease risk score (DRS)-based confounding adjustment when estimating treatment effects on multiple outcomes is not well studied. Using an empirical example comparing dabigatran versus warfarin on ischemic stroke and major bleeding risk in 12 sequential monitoring periods (90 days each) using the Truven Marketscan database, we compared two approaches for combining DRS for multiple outcomes: (1) 1:1 matching on prognostic propensity scores (PPS), created using DRS for bleeding and stroke as independent variables in a propensity score (PS) model; and (2) simultaneous 1:1 matching on DRS for bleeding and stroke using Mahalanobis (M)-distance, against traditional PS-matching. M-distance matching appeared to produce more stable results in the early marketing period compared to both PPS and traditional PS-matching; hazard ratios (95% confidence intervals) for unadjusted, traditional PS-matching, PPS-matching, and M-distance matching after 4 periods were 0.72 (0.51-1.03), 0.61 (0.31-1.09), 0.55 (0.33-0.91), and 0.78 (0.45-1.34) for stroke, and 0.65 (0.53-0.80), 0.78 (0.60-1.01), 0.75 (0.59-0.96), and 0.78 (0.64-0.95) for bleeding. In later periods, estimates were similar for traditional PS-matching and M-distance matching, but suggested potential residual confounding with PPS-matching. These results suggest that M-distance matching may be a valid approach for extension of DRS-based confounding adjustments for multiple outcomes of interest.