P22. Prognostic Predictive Model for the Development of Osteoarthritis using Electronic Medical Record Data

P22. Prognostic Predictive Model for the Development of Osteoarthritis using Electronic Medical Record Data

Background: As the most common joint disorder worldwide (1), osteoarthritis represents a growing concern for older adults. Prognostic predictive models (statistical models used to predict future disease development (2)) may enable the identification of patients at high risk of developing osteoarthritis, allowing for health and lifestyle modifications aimed at reducing the risk of disease development (3,4).

Methods: For our project, we accessed the DELPHI (Deliver Primary Healthcare Information) database which contains de-identified electronic medical records of more than 60,000 primary care patients in Ontario (5,6). From these data, we constructed a retrospective cohort examining patients’ risk factors and followed them over time to observe incident cases of osteoarthritis. This retrospective cohort was used to develop and test prognostic predictive models, using methods such as logistic regression, to determine the models’ ability to predict development of osteoarthritis. Models were evaluated, examining both discrimination (AUC) and calibration (calibration plots), using a reserved portion of patient data.

Results: A logistic regression model was built that predicts the incidence of osteoarthritis based on patient age, sex, Body Mass Index (BMI), osteoporosis status, and leg injury status (AUC: 0.73).

Discussion & Conclusion: By creating a prognostic predictive model for osteoarthritis, we aim to support primary health care practitioners in estimating an individual patient’s risk of osteoarthritis; thereby allowing practitioners and patients to create unique plans to address the patient’s personal risk factors.

Interdisciplinary Reflection: This project is highly interdisciplinary as it spans the fields of epidemiology, statistics, health informatics, primary health care, and computer science.

References:

1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJL. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet (London, England) [Internet]. 2006 May 27 [cited 2016 Feb 13];367(9524):1747–57. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16731270

2. Hendriksen JMT, Geersing GJ, Moons KGM, de Groot JAH. Diagnostic and prognostic prediction models. J Thromb Haemost [Internet]. 2013 Jun [cited 2016 Aug 10];11 Suppl 1:129–41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23809117

3. Felson DT, Zhang Y, Anthony JM, Naimark A, Anderson JJ. Weight loss reduces the risk for symptomatic knee osteoarthritis in women. The Framingham Study. Ann Intern Med [Internet]. 1992 Apr 1 [cited 2016 Jun 23];116(7):535–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1543306

4. Felson DT. Weight and osteoarthritis. Am J Clin Nutr [Internet]. 1996 Mar [cited 2016 Jun 23];63(3 Suppl):430S–432S. Available from: http://www.ncbi.nlm.nih.gov/pubmed/8615335

5. CPCSSN. DELPHI (Deliver Primary Healthcare Information) Project [Internet]. 2013. Available from: http://cpcssn.ca/regional-networks/delphi-deliver-primary-healthcare-information-project/

6. Birtwhistle R, Keshavjee K, Lambert-Lanning A, Godwin M, Greiver M, Manca D, et al. Building a pan-Canadian primary care sentinel surveillance network: initial development and moving forward. J Am Board Fam Med [Internet]. 2009 Jan [cited 2016 May 19];22(4):412–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19587256