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Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients

Afshar, Majid ; Joyce, Cara ; Dligach, Dmitriy ; Sharma, Brihat ; Kania, Robert ; Xie, Meng ; Swope, Kristin ; Salisbury-Afshar, Elizabeth ; Karnik, Niranjan S Cerda, Magdalena

PloS one, 2019-07, Vol.14 (7), p.e0219717 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients
Autor: Afshar, Majid ; Joyce, Cara ; Dligach, Dmitriy ; Sharma, Brihat ; Kania, Robert ; Xie, Meng ; Swope, Kristin ; Salisbury-Afshar, Elizabeth ; Karnik, Niranjan S
Cerda, Magdalena
Assuntos: Addictive behaviors ; Adult ; Alcohol ; Alcoholic beverages ; Alcoholism ; Alcoholism - diagnosis ; Alcoholism - epidemiology ; Analgesics, Opioid - adverse effects ; Analysis ; Artificial intelligence ; Behavior ; Biology and Life Sciences ; Care and treatment ; Class analysis ; Clinical outcomes ; Complications ; Computational linguistics ; Computer science ; Data ; Domains ; Drinking (Alcoholic beverages) ; Drug abuse ; Drug administration ; Electronic Health Records ; Electronic medical records ; Electronic records ; Enrichment ; Factorial experiments ; Female ; Health aspects ; Health planning ; Health sciences ; Health services ; Homeless people ; Hospital admission and discharge ; Hospital patients ; Hospitalization ; Humans ; Informatics ; Inpatients ; Language processing ; Latent Class Analysis ; Latent structure analysis ; Machine Learning ; Male ; Medical care utilization ; Medical prognosis ; Medical records ; Medicine ; Medicine and Health Sciences ; Mental disorders ; Methods ; Middle Aged ; Models, Theoretical ; Narcotics ; Natural language interfaces ; Natural Language Processing ; Observational studies ; Opiates ; Opioid abuse ; Opioid-Related Disorders - classification ; Opioid-Related Disorders - diagnosis ; Opioid-Related Disorders - epidemiology ; Opioids ; Patient Discharge ; Patients ; Physician and patient ; Physician-patient relations ; Precision Medicine ; Prescription Drug Misuse - classification ; Prescription Drug Misuse - statistics & numerical data ; Prognosis ; Psychosis ; Psychotic disorders ; Public health ; Research and Analysis Methods ; Social class ; Social classes ; Socioeconomic status ; Socioeconomics ; Statistical analysis ; Strategies ; Strategy ; Substance abuse ; Substance abuse treatment ; Substance use ; Tertiary Care Centers ; Treatment Outcome ; Treatment outcomes ; Urine ; Utilization ; Variables ; Young Adult
É parte de: PloS one, 2019-07, Vol.14 (7), p.e0219717
Notas: Competing Interests: The authors have declared that no competing interests exist.
Descrição: Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes. This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes. The analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 (13.9% vs. 10.5%, p<0.01); discharge against medical advice for class 2 (12.3% vs. 5.3%, p<0.01); and in-hospital death for classes 3 and 4 (3.2% vs. 1.9%, p<0.01). A 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions.
Editor: United States: Public Library of Science
Idioma: Inglês

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Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients

Afshar, Majid ; Joyce, Cara ; Dligach, Dmitriy ; Sharma, Brihat ; Kania, Robert ; Xie, Meng ; Swope, Kristin ; Salisbury-Afshar, Elizabeth ; Karnik, Niranjan S Cerda, Magdalena

United States: Public Library of Science

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