Electronic health record phenotyping improves detection and screening of type 2 XXXX in the general United States population: A cross-sectional, unselected, retrospective study.

@entity1 @entity101 @entity283 @entity6 @entity1112 @entity5748 @entity4989 @entity2704 @entity943
OBJECTIVES: An estimated 25% of type two @entity6 ( @entity4989 ) @entity1 in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether @entity283 ( @entity283 ) phenotyping could improve @entity4989 screening compared to conventional models, even when records are incomplete and not recorded systematically across @entity1 and practice locations, as is typically seen in practice. METHODS: In this cross-sectional, retrospective study, @entity283 data from 9948 US @entity1 were used to develop a pre-screening tool to predict current @entity4989 , using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation. We compared (1) a full @entity283 model containing commonly prescribed medications, diagnoses, and conventional predictors, (2) a restricted @entity283 DX model which excluded medications, and (3) a conventional model containing basic predictors and their interactions (BMI, age, sex, smoking status, @entity101 ). RESULTS: Using a @entity1 's full @entity283 or restricted @entity283 was superior to using basic covariates alone for detecting individuals with @entity6 ( @entity1112 (2) test, p<0.001). @entity2704 and @entity943 were associated negatively with @entity4989 , while sexual and @entity5748 were associated positively. Adding @entity283 phenotypes improved classification; the AUC for the full @entity283 Model, @entity283 DX model, and conventional model using logistic regression, were 84.9%, 83.2%, and 75.0% respectively. For random forest out-of-sample prediction, accuracy also was improved when using @entity283 phenotypes; the AUC values were 81.3%, 79.6%, and 74.8%, respectively. Improved AUCs reflect better performance for most thresholds that balance sensitivity and specificity. CONCLUSIONS: @entity283 phenotyping resulted in markedly superior detection of @entity4989 , even in the face of missing and unsystematically recorded data, based on the ROC curves. @entity283 phenotypes could more efficiently identify which @entity1 do require, and don't require, further laboratory screening. When applied to the current number of undiagnosed individuals in the United States, we predict that incorporating @entity283 phenotype screening would identify an additional 400,000 @entity1 with active, untreated @entity6 compared to the conventional pre-screening models.
