  Background-Prognostic predictive models are used in the delivery of primary
care to estimate a patients risk of future disease development. Electronic
medical record, EMR, data can be used for the construction of these models.
Objectives- To provide a framework for those seeking to develop prognostic
predictive models using EMR data, and to illustrate these steps using
osteoarthritis risk estimation as an example. FRAMR-EMR-The FRAmework for
Modelling Risk from EMR data, FRAMR-EMR, was created, which outlines
step-by-step guidance for the construction of a prognostic predictive model
using EMR data. Throughout these steps, several potential pitfalls specific to
using EMR data for predictive purposes are described and methods for addressing
them are suggested. Case Study-We used the DELPHI, DELiver Primary Healthcare
Information, database to develop our prognostic predictive model for estimation
of osteoarthritis risk. We constructed a retrospective cohort of 28447 eligible
primary care patients. Patients were included if they had an encounter with
their primary care practitioner between 1 January 2008 and 31 December 2009.
Patients were excluded if they had a diagnosis of osteoarthritis prior to
baseline. Construction of a prognostic predictive model following FRAMR-EMR
yielded a predictive model capable of estimating 5-year risk of osteoarthritis
diagnosis. Logistic regression was used to predict osteoarthritis based on age,
sex, BMI, previous leg injury, and osteoporosis. Internal validation of the
models performance demonstrated good discrimination and moderate calibration.
Conclusions-This study provides guidance to those interested in developing
prognostic predictive models based on EMR data. The production of high quality
prognostic predictive models allows for practitioner communication of
accurately estimated risks of developing future disease among primary care
patients.
