Molly McCann-Pineo1, Julia Ruskin2, Rehana Rasul3, Eugene Vortsman4, Kristin Bevilacqua5, Samantha S Corley6, Rebecca M Schwartz7. 1. Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; The Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY 11030, USA; Joint Center for Disaster Health, Trauma and Resilience at Mount Sinai, Stony Brook University and Northwell Health, New York, USA. Electronic address: Mmccann1@northwell.edu. 2. Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA. Electronic address: Jruskin@princeton.edu. 3. Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; The Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY 11030, USA; Joint Center for Disaster Health, Trauma and Resilience at Mount Sinai, Stony Brook University and Northwell Health, New York, USA. Electronic address: Rrasul@northwell.edu. 4. Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; Department of Emergency Medicine, Long Island Jewish Medical Center, Northwell Health, 270-05 76th Ave, Queens, NY 11040, USA,. Electronic address: Evortsman1@northwell.edu. 5. Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA. Electronic address: Kbevila1@jhmi.edu. 6. Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; The Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY 11030, USA; Joint Center for Disaster Health, Trauma and Resilience at Mount Sinai, Stony Brook University and Northwell Health, New York, USA. Electronic address: Sschneide5@northwell.edu. 7. Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; Department of Occupational Medicine, Epidemiology and Prevention, Northwell Health, 175 Community Drive, 2nd floor, Great Neck, NY 11021, USA; The Feinstein Institutes for Medical Research, Northwell Health, 350 Community Drive, Manhasset, NY 11030, USA; Joint Center for Disaster Health, Trauma and Resilience at Mount Sinai, Stony Brook University and Northwell Health, New York, USA; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, NY 11549, USA; Institute for Translational Epidemiology and Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, Room 2-70A, New York, NY 10029, USA. Electronic address: Rschwartz3@northwell.edu.
Abstract
INTRODUCTION: The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emergency Department (ED) setting, where providers are now confronted with balancing pain management and potential abuse. This study aims to examine patient sociodemographic and ED clinical characteristics to comprehensively determine predictors of opioid administration during an ED visit (ED-RX) and prescribing upon discharge (DC-RX). METHODS: ED visit data of patients ≥18 years old from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2014 to 2017 were used. Opioid prescriptions were determined utilizing Lexicon narcotic drug classifications. Visit characteristics studied included sociodemographic variables, and ED clinical variables, such as chief complaint, and discharge diagnosis. Machine learning methods were used to determine predictors of ED-RX and DC-RX and weighted logistic regressions were performed using selected predictors. RESULTS: Of the 44,227 ED visits identified, patients tended to be female (57.4%), and White (74.2%) with an average age of 46.4 years (SE = 0.3). Weighted proportions of ED-RX and DC-RX were 23.2% and 18.9%, respectively. The strongest predictors of ED-RX were CT scan ordered (OR = 2.18, 95% CI = 1.84-2.58), abdominal pain (OR = 1.93, 95% CI:1.59-2.34) and back pain (OR = 1.81, 95% CI:1.45-2.27). Tooth pain (OR = 6.94, 95% CI = 4.40-10.94) and fracture injury diagnoses (OR = 3.76, 95% CI = 2.72-5.19) were the strongest predictors of DC-RX. CONCLUSIONS: These findings demonstrate the utility of machine learning for understanding clinical predictors of opioid administration and prescribing in the ED, and its potential in informing standardized prescribing recommendations and guidelines.
INTRODUCTION: The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emergency Department (ED) setting, where providers are now confronted with balancing pain management and potential abuse. This study aims to examine patient sociodemographic and ED clinical characteristics to comprehensively determine predictors of opioid administration during an ED visit (ED-RX) and prescribing upon discharge (DC-RX). METHODS: ED visit data of patients ≥18 years old from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2014 to 2017 were used. Opioid prescriptions were determined utilizing Lexicon narcotic drug classifications. Visit characteristics studied included sociodemographic variables, and ED clinical variables, such as chief complaint, and discharge diagnosis. Machine learning methods were used to determine predictors of ED-RX and DC-RX and weighted logistic regressions were performed using selected predictors. RESULTS: Of the 44,227 ED visits identified, patients tended to be female (57.4%), and White (74.2%) with an average age of 46.4 years (SE = 0.3). Weighted proportions of ED-RX and DC-RX were 23.2% and 18.9%, respectively. The strongest predictors of ED-RX were CT scan ordered (OR = 2.18, 95% CI = 1.84-2.58), abdominal pain (OR = 1.93, 95% CI:1.59-2.34) and back pain (OR = 1.81, 95% CI:1.45-2.27). Tooth pain (OR = 6.94, 95% CI = 4.40-10.94) and fracture injury diagnoses (OR = 3.76, 95% CI = 2.72-5.19) were the strongest predictors of DC-RX. CONCLUSIONS: These findings demonstrate the utility of machine learning for understanding clinical predictors of opioid administration and prescribing in the ED, and its potential in informing standardized prescribing recommendations and guidelines.