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Objective: There are several efforts to re-learn the 2013 ACC/AHA pooledcohort equations (PCE) for patients with specific comorbidities and geographiclocations. With over 363 customized risk models in the literature, we aim toevaluate such revised models to determine if the performance improvementstranslate to gains in clinical utility.  Methods: We re-train a baseline PCE using the ACC/AHA PCE variables andrevise it to incorporate subject-level geographic location and comorbidityinformation. We apply fixed effects, random effects, and extreme gradientboosting models to handle the correlation and heterogeneity induced bylocations. Models are trained using 2,464,522 claims records from OptumClinformatics Data Mart and validated in the hold-out set (N=1,056,224). Weevaluate models' performance overall and across subgroups defined by thepresence or absence of chronic kidney disease (CKD) or rheumatoid arthritis(RA) and geographic locations. We evaluate models' expected net benefit usingdecision curve analysis and models' statistical properties using severaldiscrimination and calibration metrics.  Results: The baseline PCE is miscalibrated overall, in patients with CKD orRA, and locations with small populations. Our revised models improved both theoverall (GND P-value=0.41) and subgroup calibration but only enhanced netbenefit in the underrepresented subgroups. The gains are larger in thesubgroups with comorbidities and heterogeneous across geographic locations.  Conclusions: Revising the PCE with comorbidity and location informationsignificantly enhanced models' calibration; however, such improvements do notnecessarily translate to clinical gains. Thus, we recommend future works toquantify the consequences from using risk calculators to guide clinicaldecisions.

Clinical Utility Gains from Incorporating Comorbidity and Geographic  Location Information into Risk Estimation Equations for Atherosclerotic  Cardiovascular Disease