Typical progression patterns-sequences and timing of the conditions that patients progress through from a healthy state to a complication of diabetes or hypertension- can represent distinct disease mechanisms, knowledge that would be tremendously useful in optimizing care and in understanding the etiology of diabetes and hypertension. Patient coverage and follow-up times of EHR data available to most institutes do not allow for observing patients from the onset of the disease to the complications.
We aim to reconstruct the progression patterns from a unique combinations of two data sets: the University of Minnesota clinical data repository of 2,000,000 patients with relatively short follow-up times and the Mayo Clinic's exceptionally clean and complete Rochester Epidemiology Project (REP) data set covering 100,000 patients with long follow-up. First, we extract the individual patients' trajectories. From these trajectories, we extract all progression pairs, sequences of two directly or indirectly subsequent conditions. We also estimate and the risks of complications this progression confers upon the patient, as well as the progression time distribution between the pair of conditions. We represent these pairs as a typical progression and a catalog of exceptions (atypical pairs between the same two conditions that differ in history, medication or other details and have significantly different outcomes). Finally, using the Mayo Clinic data with its long follow- up times as scaffolding, we reconstruct the progression patterns from the progression pairs.

Public Health Relevance

Typical progression patterns?sequences and timing of the conditions that patients progress through from a healthy state to a complication of diabetes or hypertension? can represent distinct disease mechanisms, knowledge that would be tremendously useful in optimizing care and in understanding the etiology of diabetes and hypertension. Patient coverage and follow-up times of EHR data available to most institutes do not allow for observing patients from the onset of the disease to the complications. We aim to reconstruct the progression patterns from statistically significant partial progression patterns discovered from a large clinical data repository at the UMN with short follow-up using the Rochester Epidemiology Project (REP) data set at Mayo Clinic with its long follow-up times as scaffolding.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
1R01LM011972-01A1
Application #
8884195
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2015-07-15
Project End
2016-06-30
Budget Start
2015-07-15
Budget End
2016-06-30
Support Year
1
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Oh, Wonsuk; Kim, Era; Castro, M Regina et al. (2016) Type 2 Diabetes Mellitus Trajectories and Associated Risks. Big Data 4:25-30