Clinical trial NCT02667197: Study to Validate Coded Medical Terms Used to Identify Opioid-Related Overdose in Databases Used for PMR Study 1B
Official title: Study to Validate Coded Medical Terminologies Used to Identify Opioid-Related Overdose in the Postmarketing Databases Employed in PMR Study 1B
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Opioid-Related Disorders
Opiate Addiction
Narcotic Abuse
Drug Abuse
Lead sponsor: Member Companies of the Opioid PMR Consortium
Sponsor class: INDUSTRY
Collaborators: name: Kaiser Permanente
name: World Health Information Science Consultants, LLC
First posted: 2016-01-28
Enrollment: 2701
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Members of the KPNW integrated healthcare system located in Oregon and Washington prescribed opioid analgesics.

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.

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).

Eligibility criteria: Inclusion:

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

Exclusion:

None

Primary outcomes:
measure: ICD-9 codes for opioid overdoses
timeFrame: Retrospective review over four year period (January 2009 - December 2013)
measure: Medical chart review by trained chart abstraction personnel and clinical diagnosticians.
timeFrame: Retrospective review over four year period (January 2009 - December 2013)
measure: Algorithms to improve the sensitivity and specificity of ICD-9 diagnosis codes for detecting opioid overdoses
timeFrame: Retrospective review over four year period (January 2009 - December 2013)
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Purdue Pharma LP
name: Paul Coplan, MS, ScD, MBA
role: STUDY_CHAIR
References:
citation: 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.
citation: 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.
citation: 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.