Yield and bias in defining a cohort study baseline from electronic health record data

Abstract

Aims Despite growing interest in using electronic health records (EHR) to create longitudinal cohort studies, the distribution and missingness of EHR data might introduce selection bias and information bias to such analyses. We aimed to examine the yield and potential for these healthcare process biases in defining a study baseline using EHR data, using the example of cholesterol and blood pressure (BP) measurements. Methods We created a virtual cohort study of cardiovascular disease (CVD) from patients with eligible cholesterol profiles in the New England (NE) and Southeast (SE) networks of the Veterans Health Administration in the United States. Using clinical data from the EHR, we plotted the yield of patients with BP measurements within an expanding timeframe around an index date of cholesterol testing. We compared three groups: 1) patients with BP from the exact index date; 2) patients with BP not on the index date but within the network-specific 90th percentile around the index date; and 3) patients with no BP within the network-specific 90th percentile. Results Among 589,361 total patients in the two networks, 146,636 (61.0%) of 240,479 patients from NE and 289,906 (83.1%) of 348,882 patients from SE had BP measurements on the index date. Ninety percent had BP measured within 11 days of the index date in NE and within 5 days of the index date in SE. Group 3 in both networks had fewer available race data, fewer comorbidities and CVD medications, and fewer health system encounters. Conclusions Requiring same-day risk factor measurement in the creation of a virtual CVD cohort study from EHR data might exclude 40% of eligible patients, but including patients with infrequent visits might introduce bias. Data visualization can inform study-specific strategies to address these challenges for the research use of EHR data.

Estimation of Atherosclerotic Cardiovascular Disease Risk Among Patients in the Veterans Affairs Health Care System

“…We created a prospective cohort study of incident ASCVD events from national VA data, using previously described methods. 11 In brief, this cohort included all VA patients aged at least 18 years with at least 1 primary care visit at a VA facility who had at least 1 outpatient lipid result between 2002 and 2007 and a blood pressure measurement within 30 days of this index lipid testing, a criterion based on our prior work demonstrating that such a restriction limits potential bias within this cohort. 11 The date of index lipid determination served as the baseline date of entry to the cohort.…”

“…11 In brief, this cohort included all VA patients aged at least 18 years with at least 1 primary care visit at a VA facility who had at least 1 outpatient lipid result between 2002 and 2007 and a blood pressure measurement within 30 days of this index lipid testing, a criterion based on our prior work demonstrating that such a restriction limits potential bias within this cohort. 11 The date of index lipid determination served as the baseline date of entry to the cohort. Individuals were excluded from the cohort if they had a history of HIV infection, cancer, significant kidney or liver disease, schizophrenia, or dementia at baseline (eTable 1 in the Supplement).…”

Association of 152 Biomarker Reference Intervals with All-Cause Mortality in Participants of a General United States Survey from 1999 to 2010

“…There are few data-driven approaches that search for multiple variables of diverse organ systems including the kidney, bone, and liver, that might be associated with mortality in a general and otherwise healthy population ( 5 , 6 ). Furthermore, investigations that utilize administrative data (e.g., electronic health records, insurance claims) may be fraught with selection bias (e.g., administrative samples may have a higher prevalence of unhealthy individuals than noninstitutionalized populations) ( 7 – 9 ). Over the past few decades, the challenges of “over testing” and screening in specific use-cases have rightly been considered ( 10 – 13 ).…”

The role of machine learning in clinical research: transforming the future of evidence generation

“…Some commercial entities offer similar services, including Mendel.AI and Deep6AI, though peer-reviewed evidence of their development and performance metrics is unavailable, raising questions about how these approaches perform [ 21 , 22 ]. A potential opportunity of this approach is that it allows trialists to avoid relying on the completeness of structured data fields for participant identification, which has been shown to significantly bias trial cohorts [ 23 , 24 ]. Unfortunately, to the extent that novel ML approaches to patient identification rely on EHRs, biases in the EHR data may affect the algorithms’ performances, leading to replacement of one source of bias (underlying the completeness of structured data) with another (underlying the generation of EHR documentation).…”