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Background-Prognostic predictive models are used in the delivery of primarycare to estimate a patients risk of future disease development. Electronicmedical record, EMR, data can be used for the construction of these models.Objectives- To provide a framework for those seeking to develop prognosticpredictive models using EMR data, and to illustrate these steps usingosteoarthritis risk estimation as an example. FRAMR-EMR-The FRAmework forModelling Risk from EMR data, FRAMR-EMR, was created, which outlinesstep-by-step guidance for the construction of a prognostic predictive modelusing EMR data. Throughout these steps, several potential pitfalls specific tousing EMR data for predictive purposes are described and methods for addressingthem are suggested. Case Study-We used the DELPHI, DELiver Primary HealthcareInformation, database to develop our prognostic predictive model for estimationof osteoarthritis risk. We constructed a retrospective cohort of 28447 eligibleprimary care patients. Patients were included if they had an encounter withtheir primary care practitioner between 1 January 2008 and 31 December 2009.Patients were excluded if they had a diagnosis of osteoarthritis prior tobaseline. Construction of a prognostic predictive model following FRAMR-EMRyielded a predictive model capable of estimating 5-year risk of osteoarthritisdiagnosis. Logistic regression was used to predict osteoarthritis based on age,sex, BMI, previous leg injury, and osteoporosis. Internal validation of themodels performance demonstrated good discrimination and moderate calibration.Conclusions-This study provides guidance to those interested in developingprognostic predictive models based on EMR data. The production of high qualityprognostic predictive models allows for practitioner communication ofaccurately estimated risks of developing future disease among primary carepatients.

FRAMR-EMR: Framework for Prognostic Predictive Model Development Using Electronic Medical Record Data with a Case Study in Osteoarthritis Risk