Performance of a computable phenotype for identification of patients with diabetes within PCORnet: The Patient‐Centered Clinical Research Network

Abstract

Purpose PCORnet, the National Patient‐Centered Clinical Research Network, represents an innovative system for the conduct of observational and pragmatic studies. We describe the identification and validation of a retrospective cohort of patients with type 2 diabetes (T2DM) from four PCORnet sites. Methods We adapted existing computable phenotypes (CP) for the identification of patients with T2DM and evaluated their performance across four PCORnet sites (2012‐2016). Patients entered the cohort on the earliest date they met one of three CP categories: (CP1) coded T2DM diagnosis (ICD‐9/ICD‐10) and an antidiabetic prescription, (CP2) diagnosis and glycosylated hemoglobin (HbA1c) ≥6.5%, or (CP3) an antidiabetic prescription and HbA1c ≥6.5%. We required evidence of health care utilization in each of the 2 prior years for each patient, as we also developed an incident T2DM CP to identify the subset of patients without documentation of T2DM in the 365 days before t0. Among a systematic sample of patients, we calculated the positive predictive value (PPV) for the T2DM CP and incident‐T2DM CP using electronic health record (EHR) review as reference. Results The CP identified 50 657 patients with T2DM. The PPV of patients randomly selected for validation was 96.2% (n = 1572; CI:95.1‐97.0) and was consistently high across sites. The PPV for the incident‐T2DM CP was 5.8% (CI:4.5‐7.5). Conclusions The T2DM CP accurately and efficiently identified patients with T2DM across multiple sites that participate in PCORnet, although the incident T2DM CP requires further study. PCORnet is a valuable data source for future epidemiological and comparative effectiveness research among patients with T2DM.

Diabetes medication regimens and patient clinical characteristics in the national patient‐centered clinical research network, PCORnet

“…We used a previously validated computable phenotype (positive predictive value: 96.2% (CI 95.1%‐97.0%) to assemble a retrospective cohort of patients with T2D using data from PCORnet 15 . PCORnet is composed of multiple research networks encompassing one or multiple datamarts (a collection of data that can be queried and return output) across the United States.…”

“…We queried 44 datamarts from across the United States to create a cohort of adult patients (≥18 years of age) with T2D and a minimum of 1 healthcare encounter (inpatient, outpatient, or emergency department visits) in each of the 2 years prior to the date of cohort entry (t0). The t0 was the earliest date that a patient fulfilled one of three previously validated computable phenotypes (CP) for T2D between 1 January 2012 and 31 December 2017 15 : (CP1) a T2D diagnosis code (ICD9 or ICD10) as an inpatient or outpatient, and an outpatient prescription for a diabetes medication (Table ) within 90 days after the first instance of a T2D diagnosis code, (CP2) T2D diagnosis code and Hemoglobin A1c (HbA1c) level >6.5% within 90 days before or after the diagnosis date, or (CP3) outpatient diabetes medication prescription within 90 days before or after a HbA1c level >6.5%. Patients with evidence of gestational diabetes, Type 1 diabetes, prediabetes, or a positive pregnancy test within 90 days of t0 were excluded.…”

Clinical Trial Generalizability Assessment in the Big Data Era: A Review

“…Automated generalizability assessment requires computable phenotypes. 63 With a computable eligibility criteria (CEC) infrastructure, 64 the study population of a trial can be readily identified and compared with the target population.…”

Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets

“…For example, a patient with type 2 diabetes may have normal laboratory values because their disease is controlled with medication (eg, if blood glucose levels are wellcontrolled in a patient with diabetes the hemoglobin A1c may be normal). 19,20 Requiring the presence of treatment as a marker for disease can limit the cohort to treated patients. Furthermore, with off-label usage, the data scientist needs to be careful about assuming the appearance of a medication means the patient has the expected condition.…”