Literature DB >> 27763703

Performance Measures of Diagnostic Codes for Detecting Opioid Overdose in the Emergency Department.

Christopher Rowe1, Eric Vittinghoff2, Glenn-Milo Santos1,3, Emily Behar1,4, Caitlin Turner1, Phillip O Coffin1,5.   

Abstract

OBJECTIVES: Opioid overdose mortality has tripled in the United States since 2000 and opioids are responsible for more than half of all drug overdose deaths, which reached an all-time high in 2014. Opioid overdoses resulting in death, however, represent only a small fraction of all opioid overdose events and efforts to improve surveillance of this public health problem should include tracking nonfatal overdose events. International Classification of Disease (ICD) diagnosis codes, increasingly used for the surveillance of nonfatal drug overdose events, have not been rigorously assessed for validity in capturing overdose events. The present study aimed to validate the use of ICD, 9th revision, Clinical Modification (ICD-9-CM) codes in identifying opioid overdose events in the emergency department (ED) by examining multiple performance measures, including sensitivity and specificity.
METHODS: Data on ED visits from January 1, 2012, to December 31, 2014, including clinical determination of whether the visit constituted an opioid overdose event, were abstracted from electronic medical records for patients prescribed long-term opioids for pain from any of six safety net primary care clinics in San Francisco, California. Combinations of ICD-9-CM codes were validated in the detection of overdose events as determined by medical chart review. Both sensitivity and specificity of different combinations of ICD-9-CM codes were calculated. Unadjusted logistic regression models with robust standard errors and accounting for clustering by patient were used to explore whether overdose ED visits with certain characteristics were more or less likely to be assigned an opioid poisoning ICD-9-CM code by the documenting physician.
RESULTS: Forty-four (1.4%) of 3,203 ED visits among 804 patients were determined to be opioid overdose events. Opioid-poisoning ICD-9-CM codes (E850.2-E850.2, 965.00-965.09) identified overdose ED visits with a sensitivity of 25.0% (95% confidence interval [CI] = 13.6% to 37.8%) and specificity of 99.9% (95% CI = 99.8% to 100.0%). Expanding the ICD-9-CM codes to include both nonspecified and general (i.e., without a decimal modifier) drug poisoning and drug abuse codes identified overdose ED visits with a sensitivity of 56.8% (95% CI = 43.6%-72.7%) and specificity of 96.2% (95% CI = 94.8%-97.2%). Additional ICD-9-CM codes not explicitly relevant to opioid overdose were necessary to further enhance sensitivity. Among the 44 overdose ED visits, neither naloxone administration during the visit, whether the patient responded to the naloxone, nor the specific opioids involved were associated with the assignment of an opioid poisoning ICD-9-CM code (p ≥ 0.05).
CONCLUSIONS: Tracking opioid overdose ED visits by diagnostic coding is fairly specific but insensitive, and coding was not influenced by administration of naloxone or the specific opioids involved. The reason for the high rate of missed cases is uncertain, although these results suggest that a more clearly defined case definition for overdose may be necessary to ensure effective opioid overdose surveillance. Changes in coding practices under ICD-10 might help to address these deficiencies.
© 2016 by the Society for Academic Emergency Medicine.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 27763703     DOI: 10.1111/acem.13121

Source DB:  PubMed          Journal:  Acad Emerg Med        ISSN: 1069-6563            Impact factor:   3.451


  36 in total

1.  Features of prescription drug monitoring programs associated with reduced rates of prescription opioid-related poisonings.

Authors:  N J Pauly; S Slavova; C Delcher; P R Freeman; J Talbert
Journal:  Drug Alcohol Depend       Date:  2018-01-11       Impact factor: 4.492

2.  Opioid Overdose After Surgical Discharge.

Authors:  Karim S Ladha; Joshua J Gagne; Elisabetta Patorno; Krista F Huybrechts; James P Rathmell; Shirley V Wang; Brian T Bateman
Journal:  JAMA       Date:  2018-08-07       Impact factor: 56.272

3.  The Role of Primary Care in Improving Access to Medication-Assisted Treatment for Rural Medicaid Enrollees with Opioid Use Disorder.

Authors:  Evan S Cole; Ellen DiDomenico; Gerald Cochran; Adam J Gordon; Walid F Gellad; Janice Pringle; Jack Warwick; Chung-Chou H Chang; Joo Yeon Kim; Julie Kmiec; David Kelley; Julie M Donohue
Journal:  J Gen Intern Med       Date:  2019-03-18       Impact factor: 5.128

4.  Prescribing decisions at buprenorphine treatment initiation: Do they matter for treatment discontinuation and adverse opioid-related events?

Authors:  Angélica Meinhofer; Arthur Robin Williams; Phyllis Johnson; Bruce R Schackman; Yuhua Bao
Journal:  J Subst Abuse Treat       Date:  2019-07-24

5.  Mortality Quadrupled Among Opioid-Driven Hospitalizations, Notably Within Lower-Income And Disabled White Populations.

Authors:  Zirui Song
Journal:  Health Aff (Millwood)       Date:  2017-12       Impact factor: 6.301

6.  Association of Opioid Prescribing Patterns With Prescription Opioid Overdose in Adolescents and Young Adults.

Authors:  Kao-Ping Chua; Chad M Brummett; Rena M Conti; Amy Bohnert
Journal:  JAMA Pediatr       Date:  2020-02-01       Impact factor: 16.193

7.  ICD-10-CM-Based Definitions for Emergency Department Opioid Poisoning Surveillance: Electronic Health Record Case Confirmation Study.

Authors:  Svetla Slavova; Dana Quesinberry; Julia F Costich; Emilia Pasalic; Pedro Martinez; Julia Martin; Sarah Eustice; Peter Akpunonu; Terry L Bunn
Journal:  Public Health Rep       Date:  2020-02-10       Impact factor: 2.792

8.  Opioid Prescribing After Opioid-related Inpatient Hospitalizations by Diagnosis: A Cohort Study.

Authors:  Pooja A Lagisetty; Lewei A Lin; Dara Ganoczy; Rebecca L Haffajee; Theodore J Iwashyna; Amy S B Bohnert
Journal:  Med Care       Date:  2019-10       Impact factor: 2.983

9.  Predictors of Overdose Death Among High-Risk Emergency Department Patients With Substance-Related Encounters: A Data Linkage Cohort Study.

Authors:  Noa Krawczyk; Matthew Eisenberg; Kristin E Schneider; Tom M Richards; B Casey Lyons; Kate Jackson; Lindsey Ferris; Jonathan P Weiner; Brendan Saloner
Journal:  Ann Emerg Med       Date:  2019-09-09       Impact factor: 5.721

10.  Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.

Authors:  Suzanne V Blackley; Erin MacPhaul; Bianca Martin; Wenyu Song; Joji Suzuki; Li Zhou
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.