부산대학교 도서관

Methods and Results:We explored a new “predictor patch” approach to calibrating the predicted risk from a base model according to 2 components from outside datasets: (1) the difference in observed vs. expected values of novel predictors and (2) the hazard ratios (HRs) for novel predictors, in a scenario of adding kidney measures for cardiovascular mortality. Using 4 US cohorts (n=54,425) we alternately chose 1 as the base dataset and constructed a base prediction model with traditional predictors for cross-validation. In the 3 other “outside” datasets, we developed a linear regression model with traditional predictors for estimating expected values of glomerular filtration rate and albuminuria and obtained their adjusted HRs of cardiovascular mortality, together constituting a “patch” for adding kidney measures to the base model. The base model predicted cardiovascular mortality well in each cohort (c-statistic 0.78–0.91). The addition of kidney measures using a patch significantly improved discrimination (cross-validated ∆c-statistic 0.006 [0.004–0.008]) to a similar degree as refitting these kidney measures in each base dataset.