Classifying individuals based on a densely captured sequence of vital signs: An example using repeated blood pressure measurements during hemodialysis treatment.
Electronic Health Records (EHRs) present the opportunity to observe serial measurements on patients. While potentially informative, analyzing these data can be challenging. In this work we present a means to classify individuals based on a series of measurements collected by an EHR. Using patients undergoing hemodialysis, we categorized people based on their intradialytic blood pressure. Our primary criteria were that the classifications were time dependent and independent of other subjects. We fit a curve of intradialytic blood pressure using regression splines and then calculated first and second derivatives to come up with four mutually exclusive classifications at different time points. We show that these classifications relate to near term risk of cardiac events and are moderately stable over a succeeding two-week period. This work has general application for analyzing dense EHR data.
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States; Center for Predictive Medicine, Duke Clinical Research Institute, Durham, NC, United States. Electronic address: ben.goldstein@duke.edu.
Benjamin A
BA
Goldstein
Division of Nephrology, Stanford University School of Medicine, Palo Alto, CA, United States.
Tara I
TI
Chang
Section of Nephrology, Baylor College of Medicine, Houston, TX, United States.
Wolfgang C
WC
Winkelmayer
D001794:Blood Pressure; D057286:Electronic Health Records; D006331:Heart Diseases; D006801:Humans; D006435:Renal Dialysis; D018570:Risk Assessment; D013223:Statistics as Topic
Classification; Electronic health records; Functional data analysis; Hemodialysis; Splines