Hospitalized Patients and Stimulant Use-Associated Heart Failure: Import of Ejection Fraction and Related Risk Factors

Akshat Agrawal¹Brooke Scardino²Diensn G Xing²Md. Shenuarin Bhuiyan^3,4Rick A Bevins⁵Kalgi Modi²Tarek Helmy²Steven A. Conrad^2,6Nicholas E Goeders⁷MD. MOSTAFIZUR RAHMAN BHUIYAN⁸John A Vanchiere⁶A. Wayne Orr^3,4Christopher G Kevil^3,4Mohammad Alfrad Nobel Bhuiyan^2,3,9*

• ¹Department of Public Health, Louisiana State University Health Shreveport, Shreveport, United States
• ²Department of Medicine, School of Medicine, Louisiana State University Health Shreveport, Shreveport, Louisiana, United States
• ³Department of Pathology, Louisiana State University Health Shreveport, Shreveport, Louisiana, United States
• ⁴Department of Molecular and Cellular Physiology, Louisiana State University Health Shreveport, Shreveport, United States
• ⁵Department of Psychology, University of Nebraska-Lincoln, Lincoln, Nebraska, United States
• ⁶Department of Pediatrics, Louisiana State University Health Shreveport, Shreveport, United States
• ⁷Department of Pharmacology, Toxicology and Neuroscience, Louisiana State University Health Shreveport, Shreveport, United States
• ⁸Department of Pediatric Cardiology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Dhaka, Bangladesh
• ⁹Louisiana Addiction Research Center, Louisiana State University Health Shreveport, Shreveport, United States

Background: Methamphetamine and cocaine use are known risk factors for heart failure (HF). Previous studies focused on HF cases identified as either methamphetamine or cocaine-induced HF with no study identifying the HF subtype most associated with stimulant use. Our study hypothesizes that stimulant users have a higher odds of developing HFrEF than HFpEF. Our study also compares demographic and comorbidities between the HF subtypes. Methods: National Inpatient Sample data from 2008 to 2020 were used to identify hospital admissions among stimulant users with HF. The chi-square test for categorical variables and t-test for continuous variables was used for the weighted sample. P-value was found by linear trend analysis. The trend stratified by age, sex, race, and United States region (defined by the US Census Bureau) was analyzed by the Cochran-Armitage trend test. A generalized linear model determined the HF subtype related to stimulant use adjusted for traditional risk factors, and another model estimated vulnerable patient characteristics. Results: Stimulant use was more likely to be associated with HFrEF (OR=1.97, CI 1.93-2.01), while less associated with HFpEF (OR=0.96, CI 0.94-0.98). HF among stimulant users was common (p<0.001) in males, those aged 41-64, Black patients, Medicaid users, those in the <50 percentile income, and the South or West regions. Stimulant-related HF hospitalizations increased significantly from 2008 to 2020 for all subcategories (p<0.001).   Conclusion: Stimulant use is positively associated with HFrEF, with the highest risk being in those middle-aged, male, Black, or covered by Medicaid. The higher likelihood of traditional risk factors for HF in stimulant-related HF supports the hypothesis that stimulants induce multifactorial damage to the cardiovascular system.