Detecting clinically meaningful biomarkers with repeated measurements: An illustration with electronic health records.

Published

Journal Article

Data sources with repeated measurements are an appealing resource to understand the relationship between changes in biological markers and risk of a clinical event. While longitudinal data present opportunities to observe changing risk over time, these analyses can be complicated if the measurement of clinical metrics is sparse and/or irregular, making typical statistical methods unsuitable. In this article, we use electronic health record (EHR) data as an example to present an analytic procedure to both create an analytic sample and analyze the data to detect clinically meaningful markers of acute myocardial infarction (MI). Using an EHR from a large national dialysis organization we abstracted the records of 64,318 individuals and identified 4769 people that had an MI during the study period. We describe a nested case-control design to sample appropriate controls and an analytic approach using regression splines. Fitting a mixed-model with truncated power splines we perform a series of goodness-of-fit tests to determine whether any of 11 regularly collected laboratory markers are useful clinical predictors. We test the clinical utility of each marker using an independent test set. The results suggest that EHR data can be easily used to detect markers of clinically acute events. Special software or analytic tools are not needed, even with irregular EHR data.

Full Text

Duke Authors

Cited Authors

  • Goldstein, BA; Assimes, T; Winkelmayer, WC; Hastie, T

Published Date

  • June 2015

Published In

Volume / Issue

  • 71 / 2

Start / End Page

  • 478 - 486

PubMed ID

  • 25652566

Pubmed Central ID

  • 25652566

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/biom.12283

Language

  • eng