Predicting diabetes clinical outcomes using longitudinal risk factor trajectories

Gyorgy J. Simon, Kevin A. Peterson, M. Regina Castro, Michael S. Steinbach, Vipin Kumar, Pedro J. Caraballo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background: The ubiquity of electronic health records (EHR) offers an opportunity to observe trajectories of laboratory results and vital signs over long periods of time. This study assessed the value of risk factor trajectories available in the electronic health record to predict incident type 2 diabetes. Study design and methods: Analysis was based on a large 13-year retrospective cohort of 71,545 adult, non-diabetic patients with baseline in 2005 and median follow-up time of 8 years. The trajectories of fasting plasma glucose, lipids, BMI and blood pressure were computed over three time frames (2000-2001, 2002-2003, 2004) before baseline. A novel method, Cumulative Exposure (CE), was developed and evaluated using Cox proportional hazards regression to assess risk of incident type 2 diabetes. We used the Framingham Diabetes Risk Scoring (FDRS) Model as control. Results: The new model outperformed the FDRS Model (.802 vs.660; p-values <2e-16). Cumulative exposure measured over different periods showed that even short episodes of hyperglycemia increase the risk of developing diabetes. Returning to normoglycemia moderates the risk, but does not fully eliminate it. The longer an individual maintains glycemic control after a hyperglycemic episode, the lower the subsequent risk of diabetes. Conclusion: Incorporating risk factor trajectories substantially increases the ability of clinical decision support risk models to predict onset of type 2 diabetes and provides information about how risk changes over time.

Original languageEnglish (US)
Article number6
JournalBMC Medical Informatics and Decision Making
Issue number1
StatePublished - Jan 8 2020


  • Diabetes
  • Diabetes trajectories
  • Prediabetes
  • Risk assessment

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics
  • Computer Science Applications


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