The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis
Bibliographic Data
| ID | 15480327 |
|---|---|
| Authors | Neeraj Chhabra (0000-0002-0945-1233, Cook County Health and Hospitals System, corresponding author), Dale Smith (0000-0002-9881-8067, Olivet Nazarene University), Dale L Smith (0000-0003-0854-9973, Rush University Medical Center), Caitlin M Maloney (Rush University), Joseph Archer (University of Wisconsin–Madison), Brihat Sharma (0000-0003-0417-4553, Rush University Medical Center), Hale M Thompson (0000-0002-9704-934X, Rush University Medical Center), Majid Afshar (0000-0002-6368-4652, University of Wisconsin–Madison), Niranjan S Karnik (0000-0001-7650-3008, Rush University Medical Center) |
| Year | 2022 |
| Volume | 19 |
| Issue | 14 |
| Pages | 8882-8882 |
| Publication date | 2022-07-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19148882 |
| PMID | 35886733 |
| OpenAlex | W4286433315 |
| Language | EN |
| Citations received | 1 |
| References cited | 40 |
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
Emergency department · Identification (biology · Latent class model · Opioid · Psychiatry · Medicine · Opioid Use Disorder Treatment · Prenatal Substance Exposure Effects · Psychology · Substance Abuse Treatment and Outcomes · Internal Medicine
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| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |