Development of reusable logic for determination of statin exposure-time from electronic health records

Aaron W. Miller, Catherine A. McCarty, Ulrich Broeckel, Vangelis Hytopoulos, Deanna S. Cross

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Objective: We aim to quantify HMG-CoA reductase inhibitor (statin) prescriber-intended exposure-time using a generalizable algorithm that interrogates data stored in the electronic health record (EHR). Materials and methods: This study was conducted using the Marshfield Clinic (MC) Personalized Medicine Research Project (PMRP) a central Wisconsin-based population and biobank with, on average, 30. years of electronic health data available in the independently-developed MC Cattails MD EHR. Individuals with evidence of statin exposure were identified from the electronic records, and manual chart abstraction of all mentions of prescribed statins was completed. We then performed electronic chart abstraction of prescriber-intended exposure time for statins, using previously identified logic to capture pill-splitting events, normalizing dosages to atorvastatin-equivalent dose. Four models using iterative training sets were tested to capture statin end-dates. Calculated cumulative provider-intended exposures were compared to manually abstracted gold-standard measures of ordered statin prescriptions, and aggregate model results (totals) for training and validation populations were compared. The most successful model was the one with the smallest discordance between modeled and manually abstracted Atorvastatin 10. mg/year Equivalents (AEs). Results: Of the approximately 20,000 patients enrolled in the PMRP, 6243 were identified with statin exposure during the study period (1997-2011), 59.8% of whom had been prescribed multiple statins over an average of approximately 11. years. When the best-fit algorithm was implemented and validated by manual chart review for the statin-ordered population, it was found to capture 95.9% of the correlation between calculated and expected statin provider-intended exposure time for a random validation set, and the best-fit model was able to predict intended statin exposure to within a standard deviation of 2.6 AEs, with a standard error of +0.23 AEs. Conclusion: We demonstrate that normalized provider-intended statin exposure time can be estimated using a combination of structured clinical data sources, including a medications ordering system and a clinical appointment coordination system, supplemented with text data from clinical notes.

Original languageEnglish (US)
Pages (from-to)206-212
Number of pages7
JournalJournal of Biomedical Informatics
StatePublished - Jun 2014

Bibliographical note

Funding Information:
The project described was supported by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS) , Grant UL1TR000427 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Funding Information:
This study was funded in part by Grant Number 1U01HG004608 from the National Human Genome Research Institute, United States.

Funding Information:
This study was funded in part by Grant Number 5 R01HL089655 from the National Heart Lung Blood Institute, United States.

Funding Information:
This study was also funded in part by support from the Aviir Corporation, who had no input into study design, methodological approach or data analysis.


  • Algorithm
  • Anticholesteremic agents
  • Drug dosage calculations
  • Electronic health records
  • HMG-CoA
  • Statins


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