Opioid-Related DisordersNCT02667197
Study to Validate Coded Medical Terms Used to Identify Opioid-Related Overdose in Databases Used for PMR Study 1B
An observational study of Opioid-Related Disorders and Opiate Addiction, run by Member Companies of the Opioid PMR Consortium.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Opioid-Related Disorders, Opiate Addiction, Narcotic Abuse
- What is being tested?
- Algorithm to determine overdose from opioid abuse
- How many people
- 2,701 participants
- Where
- No sites listed yet
- Study dates
- 7 Apr 2015to 17 May 2017
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Members of the KPNW integrated healthcare system located in Oregon and Washington prescribed opioid analgesics.
Who may be able to join
- 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
Who cannot join
- None
About this study
Official title: Study to Validate Coded Medical Terminologies Used to Identify Opioid-Related Overdose in the Postmarketing Databases Employed in PMR Study 1B
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.
Read the detailed description
As part of a series of post-marketing requirement (PMR) studies for extended-release (ER) and long-acting (LA) opioid analgesics, the Food and Drug Administration (FDA) is requiring New Drug Application (NDA) holders of ER/LA opioids to conduct studies to estimate the incidence of misuse, abuse, addiction, overdose, and death among patients with chronic pain using long-term opioid therapy, and to validate the measures used to estimate the incidence of these adverse events.
The purpose of this study is to validate the measurement of opioid overdose events using diagnostic codes and data extracted from notes written in the electronic medical record (EMR), accompanied by diagnostic algorithms, to be used in a study of the incidence and predictors of opioid overdose and death (PMR Study 1B) among patients prescribed opioid analgesics. Diagnostic codes, accompanied by diagnostic algorithms, will be compared against manually abstracted medical chart reviews.
Code-based algorithms will be useful for identifying opioid overdoses in claims-based systems that include only coded data and will also find applicability in systems with EMRs. Code-based algorithms will be improved with text search of EMR clinical notations using Natural Language Processing (NLP) to identify overdose events not identified by diagnostic codes and to differentiate between intentional and unintentional overdoses. Yield from the resulting EMR-based algorithm will again be compared against manually abstracted medical chart reviews.
This EMR-based algorithm will be useful for identifying opioid overdoses in systems with EMRs, and for further differentiating between the causes of different types of overdoses. For example, overdose events can be due to misuse (e.g., therapeutic use not as indicated by a clinician), medication errors by patients, medical errors made by prescribers, abuse by patients, abuse by non-patients feigning to be patients in order to receive medications; and suicides. Overdose events therefore differ in intentionality, that is whether the person was attempting suicide or not. Unintentional overdoses can occur as a result of various causes, including misuse (therapeutic use but not consistent with clinician orders), abuse, adverse reactions to medications, anesthesia, and medication errors-both patient and provider-based. In addition, the distinction between unintentional and intentional overdoses can sometimes be unclear. This validation study will attempt to differentiate overdose by intentionality using both code-based algorithms and NLP-enhanced algorithms.
Currently, administrative databases use ICD-9 codes for nonfatal diagnoses and ICD-10 codes for fatal events. In October of 2015, ICD-10 codes are scheduled to replace ICD-9 codes for nonfatal diagnoses in administrative databases. This study will validate existing ICD-9 codes so that the study can meet the FDA-required timeline for a final report by November 2015.
This study will not evaluate misuse since this will be captured by instruments in a prospective study of patients with chronic pain (PMR Study 1A) using a combination of adapted validated instruments, and new instruments that will be evaluated in PMR Study 2. This study will not include a formal validation for opioid-related deaths, since processes for coding deaths vary from state to state, but will include some verification of opioid-related deaths relative to medical records for events with available state and national death data (there is a 12-month to 2-year lag in state death records).
- Conditions
- Opioid-Related Disorders, Opiate Addiction, Narcotic Abuse, Drug Abuse
- Being tested
- Algorithm to determine overdose from opioid abuse
- Medical subject headings
- Opioid-Related Disorders, Narcotic-Related Disorders, Substance-Related Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Algorithm to determine overdose from opioid abuseOther
| Study group | Role | What they receive |
|---|---|---|
| Opioid overdose and poisoning | Not described Receives: Other: Algorithm to determine overdose from opioid abuse |
What is being measured
The primary outcome is the main question the study is designed to answer.
ICD-9 codes for opioid overdoses
- 965.0x Poisoning by opiates and related narcotics
- E850 Accidental poisoning by analgesics, antipyretics and anti-rheumatics
Measured: Retrospective review over four year period (January 2009 - December 2013)
Medical chart review by trained chart abstraction personnel and clinical diagnosticians.
Measured: 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
- Codes/procedures to rule out anesthetic-related overdose and poisonings, suicides, and serious adverse events
- Using coded medical records data, with or without Natural Language Processing (NLP) of clinical notations, to differentiate between suicides and unintentional overdoses.
- 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
- 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]).
- Conduct medical chart review to verify probable cases detected by text search/NLP.
Measured: Retrospective review over four year period (January 2009 - December 2013)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jan 2016 (estimated)
- Study start7 Apr 2015
- Primary completion17 May 2017
- Study completion17 May 2017
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Opioid-Related DisordersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 2,701 participants (actual)
Sponsor and investigators
- Lead sponsor
- Member Companies of the Opioid PMR Consortium
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Kaiser Permanente, World Health Information Science Consultants, LLC
- Responsible party
- Sponsor
- Investigators
- Paul Coplan, MS, ScD, MBA, Study chair, Purdue Pharma LP
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02667197
- Sponsor's protocol ID
- Observational Study 3033-6
- Other IDs
- 3033-6
- Status last verified
- Apr 2020
- First submitted
- 18 Jan 2016
- Last updated
- 15 Apr 2020
- Results posted
- No
Publications
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.
PubMed 31218780Green 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.
PubMed 31095831Green 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.
PubMed 31020755
Study at a glance
From the registry record, last updated by the sponsor on 15 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Study to Validate Coded Medical Terminologies Used to Identify Opioid-Related Overdose in the Postmarketing Databases Employed in PMR Study 1B
- ClinicalTrials.gov ID
- NCT02667197
- Acronym / sponsor ID
- Observational Study 3033-6
- Conditions
- Opioid-Related Disorders, Opiate Addiction, Narcotic Abuse, Drug Abuse
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,701 participants (actual)
- Lead sponsor
- Member Companies of the Opioid PMR ConsortiumIndustry (pharma, biotech, devices)
- Collaborators
- Kaiser Permanente, World Health Information Science Consultants, LLC
- Study start
- 7 Apr 2015 (actual)
- Primary completion
- 17 May 2017 (actual)
- Study completion
- 17 May 2017 (actual)
- First posted
- 28 Jan 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
As part of a series of post-marketing requirement (PMR) studies for extended-release (ER) and long-acting (LA) opioid analgesics, the Food and Drug Administration (FDA) is requiring New Drug Application (NDA) holders of ER/LA opioids to conduct studies to estimate the incidence of misuse, abuse, addiction, overdose, and death among patients with chronic pain using long-term opioid therapy, and to validate the measures used to estimate the incidence of these adverse events.
The purpose of this study is to validate the measurement of opioid overdose events using diagnostic codes and data extracted from notes written in the electronic medical record (EMR), accompanied by diagnostic algorithms, to be used in a study of the incidence and predictors of opioid overdose and death (PMR Study 1B) among patients prescribed opioid analgesics. Diagnostic codes, accompanied by diagnostic algorithms, will be compared against manually abstracted medical chart reviews.
Code-based algorithms will be useful for identifying opioid overdoses in claims-based systems that include only coded data and will also find applicability in systems with EMRs. Code-based algorithms will be improved with text search of EMR clinical notations using Natural Language Processing (NLP) to identify overdose events not identified by diagnostic codes and to differentiate between intentional and unintentional overdoses. Yield from the resulting EMR-based algorithm will again be compared against manually abstracted medical chart reviews.
This EMR-based algorithm will be useful for identifying opioid overdoses in systems with EMRs, and for further differentiating between the causes of different types of overdoses. For example, overdose events can be due to misuse (e.g., therapeutic use not as indicated by a clinician), medication errors by patients, medical errors made by prescribers, abuse by patients, abuse by non-patients feigning to be patients in order to receive medications; and suicides. Overdose events therefore differ in intentionality, that is whether the person was attempting suicide or not. Unintentional overdoses can occur as a result of various causes, including misuse (therapeutic use but not consistent with clinician orders), abuse, adverse reactions to medications, anesthesia, and medication errors-both patient and provider-based. In addition, the distinction between unintentional and intentional overdoses can sometimes be unclear. This validation study will attempt to differentiate overdose by intentionality using both code-based algorithms and NLP-enhanced algorithms.
Currently, administrative databases use ICD-9 codes for nonfatal diagnoses and ICD-10 codes for fatal events. In October of 2015, ICD-10 codes are scheduled to replace ICD-9 codes for nonfatal diagnoses in administrative databases. This study will validate existing ICD-9 codes so that the study can meet the FDA-required timeline for a final report by November 2015.
This study will not evaluate misuse since this will be captured by instruments in a prospective study of patients with chronic pain (PMR Study 1A) using a combination of adapted validated instruments, and new instruments that will be evaluated in PMR Study 2. This study will not include a formal validation for opioid-related deaths, since processes for coding deaths vary from state to state, but will include some verification of opioid-related deaths relative to medical records for events with available state and national death data (there is a 12-month to 2-year lag in state death records).
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Opioid overdose and poisoning | – | Other: Algorithm to determine overdose from opioid abuse |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Members of the KPNW integrated healthcare system located in Oregon and Washington prescribed opioid analgesics.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (1 listed)
- None
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Algorithm to determine overdose from opioid abuse | Other | Opioid overdose and poisoning |
Primary outcome
ICD-9 codes for opioid overdoses
Time frame: Retrospective review over four year period (January 2009 - December 2013)
1. 965.0x Poisoning by opiates and related narcotics 2. E850 Accidental poisoning by analgesics, antipyretics and anti-rheumatics
Medical chart review by trained chart abstraction personnel and clinical diagnosticians.
Time frame: 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
Time frame: Retrospective review over four year period (January 2009 - December 2013)
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Apr 2020.
- Derived from this study: PubMed 31218780Hazlehurst 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.
- Derived from this study: PubMed 31095831Green 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.
- Derived from this study: PubMed 31020755Green 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Paul Coplan, MS, ScD, MBA study chair
Purdue Pharma LP
Questions about this trial
What is this clinical trial studying?
An observational study of Opioid-Related Disorders and Opiate Addiction, run by Member Companies of the Opioid PMR Consortium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Apr 2020. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ICD-9 codes for opioid overdoses (time frame: Retrospective review over four year period (January 2009 - December 2013)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT02667197, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Apr 2020.