NCT02667197

Brief Summary

The purpose of this study is to determine reliability of codes and data from electronic medical records to predict and measure overdose and death in patients prescribed opioid analgesics. The study will compare this electronic data to data manually obtained from medical charts.

Trial Health

100
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
2,701

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Apr 2015

Typical duration for all trials

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

April 7, 2015

Completed
10 months until next milestone

First Submitted

Initial submission to the registry

January 18, 2016

Completed
10 days until next milestone

First Posted

Study publicly available on registry

January 28, 2016

Completed
1.3 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

May 17, 2017

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

May 17, 2017

Completed
Last Updated

April 15, 2020

Status Verified

April 1, 2020

Enrollment Period

2.1 years

First QC Date

January 18, 2016

Last Update Submit

April 14, 2020

Conditions

Opioid-Related DisordersOpiate AddictionNarcotic AbuseDrug Abuse

Outcome Measures

Primary Outcomes (3)

  • ICD-9 codes for opioid overdoses

    1. 965.0x Poisoning by opiates and related narcotics 2. E850 Accidental poisoning by analgesics, antipyretics and anti-rheumatics

    Retrospective review over four year period (January 2009 - December 2013)

  • Medical chart review by trained chart abstraction personnel and clinical diagnosticians.

    Retrospective review over four year period (January 2009 - December 2013)

  • Algorithms to improve the sensitivity and specificity of ICD-9 diagnosis codes for detecting opioid overdoses

    1. Codes/procedures to rule out anesthetic-related overdose and poisonings, suicides, and serious adverse events 2. Using coded medical records data, with or without Natural Language Processing (NLP) of clinical notations, to differentiate between suicides and unintentional overdoses. 3. Using coded medical records data, with or without NLP of clinical notations, to identify abuse-related overdoses not coded as opioid poisonings but noted as such in patients' medical charts 4. Identifying combinations of diagnostic, procedural, and prescription codes that, as a combination, are indicative of overdose (e.g., an ER visit at which injectable naloxone is administered followed within a few days by a prescription of buprenorphine-naloxone sublingual tablets \[Suboxone\]). 5. Conduct medical chart review to verify probable cases detected by text search/NLP.

    Retrospective review over four year period (January 2009 - December 2013)

Study Arms (1)

Opioid overdose and poisoning

Other: Algorithm to determine overdose from opioid abuse

Interventions

Algorithm to determine overdose from opioid abuseOTHER
Opioid overdose and poisoning

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Members of the KPNW integrated healthcare system located in Oregon and Washington prescribed opioid analgesics.

You may qualify if:

  • Members of the KPNW integrated healthcare system located in the states of Oregon and southwestern Washington, between August 2008 and December 2014
  • Approximately 1,200 events identified based on ICD diagnostic codes for opioid poisoning, overdose or opioid-related cause of death
  • A random sample of approximately 1,250 individuals at increased risk of opioid overdose identified based on ICD diagnoses for opioid-related adverse effects, pain, mental health, or substance abuse

You may not qualify if:

  • None

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (3)

  • Hazlehurst B, Green CA, Perrin NA, Brandes J, Carrell DS, Baer A, DeVeaugh-Geiss A, Coplan PM. Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data. Pharmacoepidemiol Drug Saf. 2019 Aug;28(8):1143-1151. doi: 10.1002/pds.4810. Epub 2019 Jun 19.

    PMID: 31218780DERIVED

  • Green CA, Hazlehurst B, Brandes J, Sapp DS, Janoff SL, Coplan PM, DeVeaugh-Geiss A. Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data. Pharmacoepidemiol Drug Saf. 2019 Aug;28(8):1138-1142. doi: 10.1002/pds.4797. Epub 2019 May 16.

    PMID: 31095831DERIVED

  • Green CA, Perrin NA, Hazlehurst B, Janoff SL, DeVeaugh-Geiss A, Carrell DS, Grijalva CG, Liang C, Enger CL, Coplan PM. Identifying and classifying opioid-related overdoses: A validation study. Pharmacoepidemiol Drug Saf. 2019 Aug;28(8):1127-1137. doi: 10.1002/pds.4772. Epub 2019 Apr 24.

    PMID: 31020755DERIVED

MeSH Terms

Conditions

Opioid-Related DisordersNarcotic-Related DisordersSubstance-Related Disorders

Condition Hierarchy (Ancestors)

Chemically-Induced DisordersMental Disorders

Study Officials

  • Paul Coplan, MS, ScD, MBA

    Purdue Pharma LP

    STUDY CHAIR

Study Design

Study Type
observational
Observational Model
OTHER
Time Perspective
RETROSPECTIVE
Sponsor Type
INDUSTRY
Responsible Party
SPONSOR

Study Record Dates

First Submitted

January 18, 2016

First Posted

January 28, 2016

Study Start

April 7, 2015

Primary Completion

May 17, 2017

Study Completion

May 17, 2017

Last Updated

April 15, 2020

Record last verified: 2020-04