The Relationship Between BMI and Clinical Factors in Heart Failure

BACKGROUND

According to the Centers for Disease Control and Prevention (CDC), chronic disease is the leading causes of death and disability in the United States (U.S.) and it accounts for most health care costs (CDC, 2015). One in four people in the U.S. die of heart disease, which is also the leading cause of death for both men and women and most ethnicities (CDC, 2015).  Heart failure (HF) is the final common pathway for all cardiovascular diseases, which leads to poor clinical outcomes. Half of the people diagnosed with HF die within five years of diagnosis (CDC, 2015). High body mass index (BMI) is considered one of the major risk factors for cardiovascular diseases (CVD) and greater than one-third of U.S. adults are obese (Ogden, et al., 2014). Despite the known risk factors associated with obesity, some studies have demonstrated that overweight patients with CVD have better prognosis than leaner patients with CVD (Lavie, et al., 2014).  In addition, HF patients with low BMI actually had poorer and unfavorable outcomes (Christensen, et al., 2013). This unanticipated outcome of obesity on CVD is known as obesity paradox. This phenomenon hypothesizes that there is a counter-intuitively protective effect against chronic disease among certain groups of patients (Kim, et al., 2015). Some studies have demonstrated a positive relationship between obesity and improved survival, but there is limited research regarding the relationship between BMI and Ejection Fraction (EF). The purpose of this study is to evaluate if there is an association between BMI, patient demographics, and clinical factors in heart failure.

 

METHODS

The study population consists of 1,837 patients, 991 males (54%) and 846 females (46%), with a primary diagnosis of HF who had been cared for in the Department of Medicine at UCLA Health. The age range of the population is 20-104 with a mean of 71.26. At the baseline study visit, all subjects provided a detailed medical history and underwent physical examination. In addition, demographic data was obtained and various clinical measures were collected including electrocardiography (i.e. EF), laboratory assay along with multiple pharmacological regimens and clinic visits.  Secondary data analysis was performed using SPSS software, Version 23.  Correlation and Multiple Regression was performed to analyze the relationship between variables. 

 

RESULTS

The overall aim of this study was to evaluate the relationship between BMI, patient demographics, and clinical factors in heart failure.  Results indicated that there was an association between EF and BMI (r = -.048, p =.042), as well as QRS duration (r = -.328, p =.000), ACE Inhibitors (r = -.134, p=.000), Beta Blockers (r = -.217, p =.000), Cardiology (r = -.114, p =.000), and PCP and Cardiology combined (r = -.107, p = .000).  These negative and positive associations in the results between the independent and dependent variables are all clinically appropriate.  The first regression test provided a model that demonstrated QRS, ACE, and Beta Blockers to be predictors of EF outcomes. In addition, two obesity categories (BMI 30-35 and BMI 35-40) also predicted EF when controlling for QRS, ACE, and Beta Blockers.  

 

CONCLUSION

Heart disease and obesity continue to be a significant problem in health care. Nurses play a key role in managing patient’s risk factors for heart disease and providing education and counseling, as well as developing new treatment approaches targeted at decreasing poor outcomes, such as hospital readmissions and mortality, and improving the quality of life for patients. Understanding the relationship between BMI, patient demographics, and clinical factors is in important step in managing heart failure.  The findings of this secondary data analysis demonstrate the importance of identifying clinical predictors that can affect EF outcomes. Particularly, in our study we did not find a positive association between BMI and EF, and therefore our data does not support the obesity paradox hypothesis.  The recommendation for nurses is to continue to educate and counsel patients about lifestyle changes that promote weight management.