The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis

Neeraj Chhabra, Dale L. Smith, Caitlin M. Maloney, Joseph Archer, Brihat Sharma, Hale M. Thompson, Majid Afshar and Niranjan S. Karnik
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Neeraj Chhabra: Division of Medical Toxicology, Department of Emergency Medicine, Cook County Health, Chicago, IL 60612, USA
Dale L. Smith: Addiction Data Science Laboratory, Department of Psychiatry & Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA
Caitlin M. Maloney: Doctor of Medicine Program, Rush Medical College, Rush University, Chicago, IL 60612, USA
Joseph Archer: School of Medicine and Public Health, University of Wisconsin, Madison, WI 53715, USA
Brihat Sharma: Addiction Data Science Laboratory, Department of Psychiatry & Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA
Hale M. Thompson: Addiction Data Science Laboratory, Department of Psychiatry & Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA
Majid Afshar: Department of Medicine, University of Wisconsin-Madison, Madison, WI 53715, USA
Niranjan S. Karnik: Addiction Data Science Laboratory, Department of Psychiatry & Behavioral Science, Rush University Medical Center, Chicago, IL 60612, USA

IJERPH, 2022, vol. 19, issue 14, 1-12

Abstract: The emergency department (ED) is a critical setting for the treatment of patients with opioid misuse. Detecting relevant clinical profiles allows for tailored treatment approaches. We sought to identify and characterize subphenotypes of ED patients with opioid-related encounters. A latent class analysis was conducted using 14,057,302 opioid-related encounters from 2016 through 2017 using the National Emergency Department Sample (NEDS), the largest all-payer ED database in the United States. The optimal model was determined by face validity and information criteria-based metrics. A three-step approach assessed class structure, assigned individuals to classes, and examined characteristics between classes. Class associations were determined for hospitalization, in-hospital death, and ED charges. The final five-class model consisted of the following subphenotypes: Chronic pain (class 1); Alcohol use (class 2); Depression and pain (class 3); Psychosis, liver disease, and polysubstance use (class 4); and Pregnancy (class 5). Using class 1 as the reference, the greatest odds for hospitalization occurred in classes 3 and 4 (Ors 5.24 and 5.33, p < 0.001) and for in-hospital death in class 4 (OR 3.44, p < 0.001). Median ED charges ranged from USD 2177 (class 1) to USD 2881 (class 4). These subphenotypes provide a basis for examining patient-tailored approaches for this patient population.

Keywords: opioid misuse; emergency department; latent class analysis; opioid epidemic (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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