Evidence map›Paper›PMID 42032789›Full record

Observational studyTrials2026

Evaluating fitness-for-use of electronic health records in ascertaining baseline characteristics and clinical outcomes in clinical trials: insights from the VESALIUS EHR demonstration project.

Pishoy Gouda, Adam J Nelson, Sudha R Raman, Laura G Qualls, Bradley G Hammill, Jyotsna Garg, Catherine P Benziger, Rekha Divakaran, Ester Kim Nilles, Keith Marsolo and 1 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Pishoy GoudaDuke Clinical Research Institute, Durham, NC, USA.
Adam J NelsonDuke Clinical Research Institute, Durham, NC, USA.
Sudha R RamanDepartment of Population Health Sciences, Duke University School of Medicine, 215 Morris Street, Suite 210, Durham, NC, 27701, USA.
Laura G QuallsDuke Clinical Research Institute, Durham, NC, USA.
Bradley G HammillDuke Clinical Research Institute, Durham, NC, USA.
Jyotsna GargDuke Clinical Research Institute, Durham, NC, USA.
Catherine P BenzigerEssentia Health, Duluth, MN, USA.
Rekha DivakaranDuke Clinical Research Institute, Durham, NC, USA.
Ester Kim NillesDuke Clinical Research Institute, Durham, NC, USA.
Keith MarsoloDuke Clinical Research Institute, Durham, NC, USA.
Emily C O'BrienDuke Clinical Research Institute, Durham, NC, USA. emily.obrien@duke.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionA tremendous amount of clinical data is collected and stored in electronic health records (EHRs). Whether this data can be harnessed at scale to facilitate clinical trial conduct remains to be seen.

methodsThe Effect of Evolocumab in Patients at High Cardiovascular Risk Without Prior Myocardial Infarction or Stroke (VESALIUS-CV) trial included an embedded study evaluating the fitness-for-use of EHR data to ascertain baseline demographics (age, sex, race/ethnicity, history of coronary disease, cerebrovascular disease, peripheral artery disease, heart failure, diabetes, hypertension, and atrial fibrillation), laboratory (creatinine, lipid values), and outcomes (myocardial infarction, stroke, revascularization, and heart failure) data, compared to trial data collected through a study case report form (CRF). These are described using concordance statistics (overall agreement, sensitivity, specificity, negative predictive value, positive predictive value, kappa statistic and bias).

resultsThe VESALIUS-CV EHR substudy included 75 participants (9 sites). For categorical baseline variables, the overall agreement between EHR and CRF data ranged between 81.3 and 98.7%, sensitivity ranged between 42.9 and 100%, specificity ranged between 50.0 and 100%, and kappa statistic ranged between 0.2 and 0.8. Identification of a history of peripheral artery disease in EHR data was poor (positive predictive value 23.1%). Of 431 continuous variables, bias was generally low but was associated with moderate imprecision, with exact matches observed in 95.8% of cases. Of a total of 20 outcome events, the overall agreement ranged between 86.7 and 100%, sensitivity ranged between 50 and 100%, specificity ranged between 86.7 and 100%, and kappa statistic ranged between 0.7 and 1.0.

conclusionEHR data has the potential to facilitate and reduce the burden of data collection for clinical trials, but further work is required to optimize data extraction and improve accuracy.

Indexed as

Cardiovascular DiseasesElectronic Health RecordsAgedAnticholesteremic AgentsFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedRandomized Controlled Trials as TopicReproducibility of ResultsTreatment OutcomeAnticholesteremic Agents

Identifiers

PMID42032789
PMCPMC13248382

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.