Coronary Artery Disease Phenotype Detection in an Academic Hospital System Setting

Abstract

Background The United States, and especially West Virginia, have a tremendous burden of coronary artery disease (CAD). Undiagnosed familial hypercholesterolemia (FH) is an important factor for CAD in the U.S. Identification of a CAD phenotype is an initial step to find families with FH. Objective We hypothesized that a CAD phenotype detection algorithm that uses discrete data elements from electronic health records (EHRs) can be validated from EHR information housed in a data repository. Methods We developed an algorithm to detect a CAD phenotype which searched through discrete data elements, such as diagnosis, problem lists, medical history, billing, and procedure (International Classification of Diseases [ICD]-9/10 and Current Procedural Terminology [CPT]) codes. The algorithm was applied to two cohorts of 500 patients, each with varying characteristics. The second (younger) cohort consisted of parents from a school child screening program. We then determined which patients had CAD by systematic, blinded review of EHRs. Following this, we revised the algorithm by refining the acceptable diagnoses and procedures. We ran the second algorithm on the same cohorts and determined the accuracy of the modification. Results CAD phenotype Algorithm I was 89.6% accurate, 94.6% sensitive, and 85.6% specific for group 1. After revising the algorithm (denoted CAD Algorithm II) and applying it to the same groups 1 and 2, sensitivity 98.2%, specificity 87.8%, and accuracy 92.4; accuracy 93% for group 2. Group 1 F1 score was 92.4%. Specific ICD-10 and CPT codes such as “coronary angiography through a vein graft” were more useful than generic terms. Conclusion We have created an algorithm, CAD Algorithm II, that detects CAD on a large scale with high accuracy and sensitivity (recall). It has proven useful among varied patient populations. Use of this algorithm can extend to monitor a registry of patients in an EHR and/or to identify a group such as those with likely FH.

Designing and Implementing an IT Architecture for a Digital Multicenter Dementia Registry: digiDEM Bayern

“…Implementation as an electronic health record-based registry, where data can be derived directly from clinical data and electronic health records, 65 66 is not purposeful in the early dementia setting. A previous study showed that PWD received their initial diagnosis at an advanced stage of the disease in many cases (16 months after the perception of the first symptoms in median 67 ).…”

Disparity in risk factors of ischemic stroke in four coastal-area hospitals in China

“…Diabetes mellitus was defined as (i) fasting blood glucose ≥7.0 mM as measured at least twice, (ii) blood glucose ≥11.1 mM at 2 h after oral administration of 75-g glucose, and/or (iii) current use of oral glucose-lowering drugs [ 8 ]. Coronary artery disease was defined as (i) a coronary artery stenosis rate of ≥50 % and/or (ii) current use of oral medication for coronary artery disease [ 10 ]. Hyperlipidemia was defined as (i) total cholesterol ≥6.2 mM, (ii) low-density lipoprotein–cholesterol ≥4.2 mM, (iii) triglyceride ≥2.3 mM, and/or (iv) current use of oral lipid-lowering drugs [ 11 ].…”

Rate and Risk Factors for Contralateral Slippage in Adolescents Treated for Slipped Capital Femoral Epiphysis

“…Data used in this study were based directly on the electronic medical record used in clinical practice. Furthermore, concurrent availability of Current Procedural Terminology (CPT) and International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) codes allows for querying of concurrent codes, which increases the sensitivity of searches [20][21][22] .…”