2      0

P06 - Factors Influencing the Mortality Rate of Patients Admitted with Chronic Disease in Urban Hospitals in DC Metropolitan Area: EHR Big Data Analysis

Background: Chronic disease is the leading cause of deaths worldwide. Lifestyle choices, such physical inactivity, poor nutrition, inadequate stress management, alcohol abuse, and tobacco smoking, are the major contributors for chronic disease development. Approximately 75% of Americans aged 65 or older suffer from more than one chronic disease, with the most prevalent combination being hypertension (HTN) with type 2 diabetes mellitus (T2DM) or cardiovascular disease (CVD). However, the inability to effectively use electronic health records (EHR) to garner large clinical data on the prevalence and the factor that affects chronic diseases and their associated outcomes have been reported. The difficulties in accessing hospital datasets for extraction continue to be challenging. We collaborated with a research institution located in the Washington, DC, metropolitan area to gain access to urban hospitals’ datasets to extract unidentified information with diabetes, myocardial infarction, and the associated independent variables (age, gender, race, and the lab results) with an outcome variable (mortality rate).

Methods: Secondary data from inpatients with diabetes and myocardial infarction were selected from 2014-2015 urban hospitals data warehouse. For diabetes, we used the ICD-9 codes 25000 (ICD-9: 25000) and myocardial infarction (MI) was 41000 (ICD-9: 41000), and the data extraction design was purposely selected accordingly. We also accessed factors that critically affected the outcome of the inpatients.

Results: For the 2014 data, females and African-Americans were admitted more than their male counterpart across the DM and MI only categories. For the 2015 data, females and African-Americans were also admitted more than their male counterparts for DM and MI only. However, it important to note that more males with DM±MI were admitted than in 2015, while more females were admitted for females in the same category. There was no statistically significant among African-Americans across the disease categories in both years. A reduction in mortality rate (4%) was also noted from 2014 to 2015. BMI, DBP, PP, glucose, and AIC levels were all statistically significant across the disease categories (p < 0.001) with HDL significant (p < 0.05) for only 2014 but (p > 0.253) for SBP in 2015.

Conclusion: Access to enhance the use of readily available EHR data in future clinical research is new and involving. It has been confirmed that similar studies could be encouraged, as such studies would lead into using the EHR data to determine the relationships between the prevalent of chronic diseases such as obesity, heart diseases, diabetes, and the factors that impact the outcomes of patients admitted with such diseases. This innovative research method would enable future biomedical investigators to have access to available data rather than years of waiting for bedside data collection for pilot or population research.

Learning Objective:

  • After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and evaluate the potential of implementing the improvements into practice.


You must be logged in and own this session in order to post comments.