Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models

Alzeer, Abdullah; Jones, Josette

doi:10.1093/pm/pnx149

Cited by 13 publications

(23 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recognition that various comorbidities can influence general healthcare utilization, a number of comorbidities were identified based on a medical visit with a corresponding ICD-9 code. A recent systematic review identified nine predictive models for determining opioid abuse, and the majority were based on diagnosis codes (ICD-9) (Alzeer et al 2018 ). Specific details for how these variables were extracted and their relevance to prognosis in individuals with musculoskeletal outcomes have been published (Rhon et al 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

et al. 2018

View full text Add to dashboard Cite

BackgroundPrescription opioid use at high doses or over extended periods of time is associated with adverse outcomes, including dependency and abuse. The aim of this study was to identify mediating variables that predict chronic opioid use, defined as three or more prescriptions after orthopedic surgery.MethodsIndividuals were ages between 18 and 50 years and undergoing arthroscopic hip surgery between 2004 and 2013. Two categories of chronic opioid use were calculated based on individuals (1) having three or more unique opioid prescriptions within 2 years and (2) still receiving opioid prescriptions > 1 year after surgery. Univariate elationships were identified for each predictor variable, then significant variables (P > 0.15) were entered into a multivariate logistic regression model to identify the most parsimonious group of predictor variables for each chronic opioid use classification. Likelihood ratios were derived from the most robust groups of variables.ResultsThere were 1642 participants (mean age 32.5 years, SD 8.2, 54.1% male). Nine predictor variables met the criteria after bivariate analysis for potential inclusion in each multivariate model. Eight variables: socioeconomic status (from enlisted rank family), prior use of opioid medication, prior use of non-opioid pain medication, high health-seeking behavior before surgery, a preoperative diagnosis of insomnia, mental health disorder, or substance abuse were all predictive of chronic opioid use in the final model (seven variables for three or more opioid prescriptions; four variables for opioid use still at 1 year; all< 0.05). Post-test probability of having three or more opioid prescriptions was 93.7% if five of seven variables were present, and the probability of still using opioids after 1 year was 69.6% if three of four variables were present.ConclusionA combination of variables significantly predicted chronic opioid use in this cohort. Most of these variables were mediators, indicating that modifying them may be feasible, and the potential focus of interventions to decrease the risk of chronic opioid use, or at minimum better inform opioid prescribing decisions. This clinical prediction rule needs further validation.

show abstract

Section: Methodsmentioning

confidence: 99%

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

et al. 2018

View full text Add to dashboard Cite

show abstract

“…25,26 Reviews also examined predictors of opioid misuse and abuse, and overdose, but predictors may differ for LTOT. [27][28][29][30][31][32] Little is known about the criteria used to define LTOT for research purposes, and there has been no synthesis of the risk factors associated with transition from short-term opioid therapy to LTOT. Without an understanding of the criteria or the predictors of LTOT, it is difficult to understand the strengths and limitations regarding the evidence on the risk of LTOT, distinguish between episodic and longterm opioid use, and monitor the prevalence and incidence of LTOT.…”

Section: Korff and Colleagues On Ltot Clinical Guidelines Define Ltomentioning

confidence: 99%

“…However, in these studies, arthritis, chronic pain, tobacco use, drug disorders, and mental health disorders were consistently identified as risk factors for LTOT, many of which are similar to the risk factors associated with misuse, abuse, and overdose. [27][28][29][30][31][32] Finally, only three predictors examined prior opioid prescriptions, suggesting limited evidence on how initial prescribing may lead to LTOT. In a study not included in this review because it included pediatric patients, the number of fills and total dose during the first month of opioid use were associated with greater risk of LTOT.…”

Section: /1002 = 57%mentioning

confidence: 99%

“…Previous systematic reviews of pain medication use (covering studies published before 2014) for CNCP, examined constructs relating to adherence such as overuse, underuse, and misuse of opioid and non‐opioid medications, but did not specifically examine LTOT . Reviews also examined predictors of opioid misuse and abuse, and overdose, but predictors may differ for LTOT …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Long‐term opioid therapy definitions and predictors: A systematic review

Karmali

Bush

Raman

et al. 2019

Pharmacoepidemiology and Drug

View full text Add to dashboard Cite

Purpose This review sought to (a) describe definitions of long‐term opioid therapy (LTOT) outcome measures, and (b) identify the predictors associated with the transition from short‐term opioid use to LTOT for opioid‐naïve individuals. Methods We conducted a systematic review of the peer‐reviewed literature (January 2007 to July 2018). We included studies examining opioid use for more than 30 days. We classified operationalization of LTOT based on criteria used in the definitions. We extracted LTOT predictors from multivariate models in studies of opioid‐naïve individuals. Results The search retrieved 5,221 studies, and 34 studies were included. We extracted 41 unique variations of LTOT definitions. About 36% of definitions required a cumulative duration of opioid use of 3 months. Only 17% of definitions considered consecutive observation periods, 27% used days' supply, and no definitions considered dose. We extracted 76 unique predictors of LTOT from seven studies of opioid‐naïve patients. Common predictors included pre‐existing comorbidities (21.1%), non‐opioid prescription medication use (13.2%), substance use disorders (10.5%), and mental health disorders (10.5%). Conclusions Most LTOT definitions aligned with the chronic pain definition (pain more than 3 months), and used cumulative duration of opioid use as a criterion, although most did not account for consistent use. Definitions were varied and rarely accounted for prescription characteristics, such as days' supply. Predictors of LTOT were similar to known risk factors of opioid abuse, misuse, and overdose. As LTOT becomes a central component of quality improvement efforts, researchers should incorporate criteria to identify consistent opioid use to build the evidence for safe and appropriate use of prescription opioids.

show abstract

“…"opioid overdose" vs. "opioid abuse") and population inclusion criteria (minimum medication day's supply or number of opioid prescriptions) may alter the ability to achieve consensus on the problem definition and the population that requires intervention (15). Despite these differences, the International Classification of Diseases (ICD) is a standard method used by health care providers to diagnose opioid use disorder, overdose, abuse, and dependence (which we will refer to in this study as Opioid Use Problems or OUP).…”

Section: A Backgroundmentioning

confidence: 99%

A Comparison Of Text Mining Versus Diagnostic Codes To Identify Opioid Use Problem: A Retrospective Study

Alzeer

Jones

Liu

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background As opioid prescriptions have risen, there has also been a rise in opioid overdose deaths and substance use disorders. Public health systems have tried to improve their ability to detect and intervene in opioid use disorders to prevent death due to overdose. The objective of this study is to compare two approaches to identify opioid use problems (OUP) using electronic health record data- text mining versus diagnostic codes.Methods Our sample consisted of adults on long-term opioid therapy (LTOT), defined as at least ≥ 70 days of supply within 90 days, and who visited a large multi-hospital network within a two-year period, between 1 January 2013 and 31 December 2014. We excluded patients with active cancer or schizophrenia. Text mining results were validated by a semi-assisted human review process and positive predictive value and level of agreement was reported. Each algorithm sought to identify patients who visited a health care facility due to an opioid poisoning event, opioid abuse, or opioid dependence. Population characteristics for positive OUP identified by text mining and ICD cohorts were compared. Chi-square and Fishers exact test were used for categorical data analysis and independent t-test was used to compare means for continuous variables. We further compared the demographics of the cohorts identified by the two methods.Results We identified 14,298 eligible LTOT patients. Text mining of relevant electronic clinical notes yielded 127 positive OUP cases compared to 45 cases using International Classification of Disease (ICD)-9 codes for the same population. Just eight OUP patients were identified using both methods. The two cohorts differed significantly with respect to age, gender, and other characteristics.Conclusions Compared to diagnostic codes, text mining identified more OUP cases with distinct characteristics. Incorporating text-mining techniques into OUP surveillance methods may support better detection of OUP and more accurate estimates of prevalence.

show abstract

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models

Cited by 13 publications

References 45 publications

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

Long‐term opioid therapy definitions and predictors: A systematic review

A Comparison Of Text Mining Versus Diagnostic Codes To Identify Opioid Use Problem: A Retrospective Study

Contact Info

Product

Resources

About

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models

Cited by 13 publications

References 45 publications

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

Predictors of chronic prescription opioid use after orthopedic surgery: derivation of a clinical prediction rule.

Long‐term opioid therapy definitions and predictors: A systematic review

A Comparison Of Text Mining Versus Diagnostic Codes To Identify Opioid Use Problem: A Retrospective Study​

Contact Info

Product

Resources

About

A Comparison Of Text Mining Versus Diagnostic Codes To Identify Opioid Use Problem: A Retrospective Study