title: Use of an electronic health record to identify prevalent and incident cardiovascular disease in type 2 diabetes according to treatment strategy.
abstract: BACKGROUND
  The increasing use of electronic health records (EHRs) in clinical practice offers the potential to investigate cardiovascular outcomes over time in patients with type 2 diabetes (T2D).
  
  
  OBJECTIVE
  To develop a methodology for identifying prevalent and incident cardiovascular disease (CVD) in patients with T2D who are candidates for therapeutic intensification of glucose-lowering therapy.
  
  
  METHODS
  Patients with glycated hemoglobin (HbA1c) ≥7% (53 mmol/mol) while receiving 1-2 oral diabetes medications (ODMs) were identified from an EHR (2005-2011) and grouped according to intensification with insulin (INS) (n=372), a different class of ODM (n=833), a glucagon-like peptide receptor 1 agonist (GLP-1RA) (n=59), or no additional therapy (NAT) (n=2017). Baseline prevalence of CVD was defined by documented International Classification of Diseases Ninth Edition (ICD-9) codes for coronary artery disease, cerebrovascular disease, or other CVD with first HbA1c ≥7% (53 mmol/mol). Incident CVD was defined as a new ICD-9 code different from existing codes over 4 years of follow-up. ICD-9 codes were validated by a chart review in a subset of patients.
  
  
  RESULTS
  Sensitivity of ICD-9 codes for CVD ranged from 0.83 to 0.89 and specificity from 0.90 to 0.96. Baseline prevalent (INS vs ODM vs GLP-1RA vs NAT: 65% vs 39% vs 54% vs 59%, p<0.001) and incident CVD (Kaplan-Meier estimates: 58%, 31%, 52%, and 54%, p=0.002) were greater in INS group after controlling for differences in baseline HbA1c (9.2±2.0% vs 8.3±1.2% vs 8.2±1.3% vs 7.7±1.1% (77 vs 67 vs 66 vs 61 mmol/mol), p<0.001) and creatinine (1.15±0.96 vs 1.10±0.36 vs 1.01±0.35 vs 1.07±0.45 mg/dL, p=0.001).
  
  
  CONCLUSIONS
  An EHR can be an effective method for identifying prevalent and incident CVD in patients with T2D.
authors.affiliation: Division of Endocrinology , University of Pittsburgh , Pittsburgh, Pennsylvania , USA.
authors.forename: Mary T
authors.initials: MT
authors.lastname: Korytkowski
authors.affiliation: Department of Medicine , NYU Langone Trinity Center , New York, New York , USA.
authors.forename: Esra
authors.initials: E
authors.lastname: Karslioglu French
authors.affiliation: University of Pittsburgh, Graduate School of Public Health , Pittsburgh, Pennsylvania , USA.
authors.forename: Maria
authors.initials: M
authors.lastname: Brooks
authors.affiliation: Department of Health Information Management , University of Pittsburgh , Pittsburgh, Pennsylvania , USA.
authors.forename: Dilhari
authors.initials: D
authors.lastname: DeAlmeida
authors.affiliation: University of Pittsburgh Medical Center (UPMC) , Pittsburgh, Pennsylvania , USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Kanter
authors.affiliation: Department of Epidemiology , University of Pittsburgh, Graduate School of Public Health , Pittsburgh, Pennsylvania , USA.
authors.forename: Manuel
authors.initials: M
authors.lastname: Lombardero
authors.affiliation: Lehigh Valley Health Network, Diabetes and Endocrinology , Lehigh Valley, Pennsylvania , USA.
authors.forename: Vasudev
authors.initials: V
authors.lastname: Magaji
authors.affiliation: University of Pittsburgh, Graduate School of Public Health , Pittsburgh, Pennsylvania , USA.
authors.forename: Trevor
authors.initials: T
authors.lastname: Orchard
authors.affiliation: Division of Endocrinology and Metabolism , University of Pittsburgh , Pittsburgh, Pennsylvania , USA.
authors.forename: Linda
authors.initials: L
authors.lastname: Siminerio
keywords: Cardiovacsular Disease(s); Electronic Medical Records; Hypoglycemic Agents; Type 2 Diabetes