Prediction of Chronic Obstructive Pulmonary Disease (COPD) in Asthma Patients Using Electronic Medical Records
- aHarvard-MIT Division of Health Sciences and Technology, Cambridge, MA
- bChildren's Hospital, Informatics Program Technology, Harvard Medical School, Boston, MA
- cPartners Healthcare Center for Personalized Genetic Medicine, Boston, MA
- dChanning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- eWellesley College, Wellesley, MA
- Correspondence: Blanca E. Himes, PhD, Channing Laboratory, 181 Longwood Ave, Boston, MA 02115; e-mail: <blanca_himes{at}hms.harvard.edu>
- Received 4 May 2008
- Accepted 30 January 2009
Abstract
Objective Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records.
Design Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing “Informatics for Integrating Biology to the Bedside” (i2b2).
Measurements A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks. The model's predictive accuracy was tested using it to predict COPD in a future independent set of asthma patients (992 patients; 46 cases, 946 controls), who had initial observation dates between 1999 and 2002.
Results A Bayesian network model composed of age, sex, race, smoking history, and 8 comorbidity variables is able to predict COPD in the independent set of patients with an accuracy of 83.3%, computed as the area under the Receiver Operating Characteristic curve (AUROC).
Conclusions Our results demonstrate that data extracted from electronic medical records can be used to create predictive models. With improvements in data extraction and inclusion of more variables, such models may prove to be clinically useful.
Footnotes
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Supported by the following NIH grants: 5U54LM008748-02 (National Centers for Biomedical Computing), 2U01HL065899 (National Heart, Lung, and Blood Institute) and 2T15LM007092-16 (NLM). The authors thank Shawn Murphy, MD, PhD and Vivian Gainer, MS of the Massachusetts General Hospital Laboratory of Computer Science at Harvard Medical School, for facilitating access to the i2b2 asthma data mart.
