Novel “Predictor Patch” Method for Adding Predictors Using Estimates From Outside Datasets ― A Proof-of-Concept Study Adding Kidney Measures to Cardiovascular Mortality Prediction ―

Abstract

Background: Cardiovascular guidelines include risk prediction models for decision making that lack the capacity to include novel predictors. 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 Delta c-statistic 0.006 [0.004-0.008]) to a similar degree as refitting these kidney measures in each base dataset. Conclusions: The addition of kidney measures using our new "predictor patch" approach based on estimates from outside datasets improved cardiovascular mortality prediction based on traditional predictors, providing an option to incorporate novel predictors to an existing prediction model.