Detecting clinically meaningful biomarkers with repeated measurements in an Electronic Health Record

TL;DR

This study develops a statistical method to identify meaningful biomarkers for acute myocardial infarction using irregular and sparse electronic health record data, demonstrating its practical utility with real-world data.

Contribution

Introduces a regression spline-based analytic approach for analyzing irregular EHR data to detect clinical biomarkers of acute events, without requiring specialized software.

Findings

EHR data can effectively identify biomarkers for acute MI.

The method works with irregular, sparse measurements without specialized tools.

Several laboratory markers showed significant predictive utility.

Abstract

Electronic health record (EHR) data are becoming an increasingly common data source for understanding clinical risk of acute events. While their longitudinal nature presents opportunities to observe changing risk over time, these analyses are complicated by the sparse and irregular measurements of many of the clinical metrics making typical statistical methods unsuitable for these data. In this paper, we present an analytic procedure to both sample from an EHR 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 5,314 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.