Development of a Risk Index for Serious Prescription Opioid-Induced Respiratory Depression or Overdose in Veterans' Health Administration Patients

Autor: Onur Baser, Lin Xie, Lenn Murrelle, Furaha Kariburyo, Catherine C. Vick, Li Wang, Janet Brigham, Barbara K. Zedler, Andrew Joyce
Rok vydání: 2015
Předmět:
Male
Comorbidity
Logistic regression
Health administration
Original Research Article
Depression (differential diagnoses)
Veterans
education.field_of_study
Methodology
Mechanisms & Translational Research Section
General Medicine
Middle Aged
Analgesics
Opioid
United States Department of Veterans Affairs
Female
Risk assessment
Risk
Adult
medicine.medical_specialty
Adolescent
Overdose
Population
Veterans Health
Opioid
Drug overdose
Risk Assessment
Young Adult
medicine
Humans
Medical prescription
Intensive care medicine
education
Aged
Retrospective Studies
Models
Statistical
Questionnaire
business.industry
Respiratory Depression
Opioid-Related Disorders
Respiration Disorders
medicine.disease
United States
Index
Anesthesiology and Pain Medicine
Socioeconomic Factors
Case-Control Studies
Emergency medicine
Neurology (clinical)
Drug Overdose
business
Zdroj: Pain Medicine: The Official Journal of the American Academy of Pain Medicine
ISSN: 1526-4637
1526-2375
DOI: 10.1111/pme.12777
Popis: Objective Develop a risk index to estimate the likelihood of life-threatening respiratory depression or overdose among medical users of prescription opioids. Subjects, Design, and Methods A case-control analysis of administrative health care data from the Veterans’ Health Administration identified 1,877,841 patients with a pharmacy record for an opioid prescription between October 1, 2010 and September 30, 2012. Overdose or serious opioid-induced respiratory depression (OSORD) occurred in 817. Ten controls were selected per case (n = 8,170). Items for an OSORD risk index (RIOSORD) were selected through logistic regression modeling, with point values assigned to each predictor. Modeling of risk index scores produced predicted probabilities of OSORD; risk classes were defined by the predicted probability distribution. Results Fifteen variables most highly associated with OSORD were retained as items, including mental health disorders and pharmacotherapy; impaired drug metabolism or excretion; pulmonary disorders; specific opioid characteristics; and recent hospital visits. The average predicted probability of experiencing OSORD ranged from 3% in the lowest risk decile to 94% in the highest, with excellent agreement between predicted and observed incidence across risk classes. The model's C-statistic was 0.88 and Hosmer–Lemeshow goodness-of-fit statistic 10.8 (P > 0.05). Conclusion RIOSORD performed well in identifying medical users of prescription opioids within the Veterans’ Health Administration at elevated risk of overdose or life-threatening respiratory depression, those most likely to benefit from preventive interventions. This novel, clinically practical, risk index is intended to provide clinical decision support for safer pain management. It should be assessed, and refined as necessary, in a more generalizable population, and prospectively evaluated.
Databáze: OpenAIRE
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