Dataset Information

Identifying and classifying opioid-related overdoses: A validation study.

ABSTRACT: PURPOSE:The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP). METHODS:Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonfatal diagnoses, International Classification of Diseases, Tenth Revision (ICD-10) codes for fatal events, clinical notes, and prescription medication records. We assessed sensitivity, specificity, positive predictive value, and negative predictive value for algorithms relative to medical chart review and conducted assessments of algorithm portability in Kaiser Permanente Washington, Tennessee State Medicaid, and Optum. RESULTS:Code-based algorithm performance was excellent for opioid-related overdoses (sensitivity = 97.2%, specificity = 84.6%) and classification of heroin-involved overdoses (sensitivity = 91.8%, specificity = 99.0%). Performance was acceptable for code-based suicide/suicide attempt classifications (sensitivity = 70.7%, specificity = 90.5%); sensitivity improved with NLP (sensitivity = 78.7%, specificity = 91.0%). Performance was acceptable for the code-based substance abuse-involved classification (sensitivity = 75.3%, specificity = 79.5%); sensitivity improved with the NLP-enhanced algorithm (sensitivity = 80.5%, specificity = 76.3%). The opioid-related overdose algorithm performed well across portability assessment sites, with sensitivity greater than 96% and specificity greater than 84%. Cross-site sensitivity for heroin-involved overdose was greater than 87%, specificity greater than or equal to 99%. CONCLUSIONS:Code-based algorithms developed to detect opioid-related overdoses and classify them according to heroin involvement perform well. Algorithms for classifying suicides/attempts and abuse-related opioid overdoses perform adequately for use for research, particularly given the complexity of classifying such overdoses. The NLP-enhanced algorithms for suicides/suicide attempts and abuse-related overdoses perform significantly better than code-based algorithms and are appropriate for use in settings that have data and capacity to use NLP.

SUBMITTER: Green CA

PROVIDER: S-EPMC6767606 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Identifying and classifying opioid-related overdoses: A validation study.

Green Carla A CA Perrin Nancy A NA Hazlehurst Brian B Janoff Shannon L SL DeVeaugh-Geiss Angela A Carrell David S DS Grijalva Carlos G CG Liang Caihua C Liang Caihua C Enger Cheryl L CL Coplan Paul M PM

Pharmacoepidemiology and drug safety 20190424 8

<h4>Purpose</h4>The study aims to develop and validate algorithms to identify and classify opioid overdoses using claims and other coded data, and clinical text extracted from electronic health records using natural language processing (NLP).<h4>Methods</h4>Primary data were derived from Kaiser Permanente Northwest (2008-2014), an integrated health care system (~n > 475 000 unique individuals per year). Data included International Classification of Diseases, Ninth Revision (ICD-9) codes for nonf ...[more]

PMID: 31020755

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Project description:AimTo understand the potential harmful effects of dose escalation among patients with chronic, non-cancer pain (CNCP) on chronic opioid therapy.DesignRetrospective cohort study.SettingUnited States Veterans Healthcare Administration.ParticipantsVeterans with CNCP and on chronic opioid therapy were identified using data from fiscal years 2008-15. The Veteran sample was approximately 90% male and 70% white.MeasurementsDose escalators [increase of > 20% average morphine milligram equivalent (MME) daily dose] were compared with dose maintainers (change of ±20% average MME daily dose). A composite measure of subsequent substance use disorders (SUDs: opioid, non-opioid and alcohol use disorders) and opioid-related adverse outcomes (AOs: accidents resulting in wounds/injuries, opioid-related and alcohol and non-opioid medication-related accidents and overdoses, self-inflicted injuries) as well as the individual SUDs and AOs was examined. The primary analyses were conducted among a 1 : 1 matched sample of escalators and maintainers matched on propensity score and index date. Propensity scores were generated using demographic characteristics, medical comorbidities, medication and health-care utilization characteristics. Subgroup analyses were conducted by quartile of the propensity score. Sensitivity analyses were conducted using adjusted logistic regression, logistic regression using stabilized inverse probability of treatment weighting (SIPTW) and instrumental variable (IV) models using geographic variation in opioid dose escalation as the IV.FindingsThere were 32 420 maintainers and 20 767 escalators resulting in 19 358 (93.2%) matched pairs. Composite AOs [odds ratio (OR) = 1.31, 95% confidence interval (CI) = 1.23, 1.40], composite SUDs (OR = 1.31, 95% CI = 1.22, 1.41) and individual SUD and AO subtypes were higher among dose escalators, except for opioid-related accidents and overdoses and violence-related injuries. Subgroup analyses within the propensity score quartiles found similar results. Sensitivity analyses with the adjusted and SIPTW logistic regressions found similar results to the primary analyses for all outcomes except for opioid-related accidents and overdoses, which were found to be significantly higher among escalators. Sensitivity analyses with IV models provided mixed results with SUDs and the individual types of AOs.ConclusionEscalating the opioid dose for those with chronic, non-cancer pain is associated with increased risks of substance use disorder and opioid-related adverse outcomes.

Project description:ImportancePrescription opioids are involved in 40% of all deaths from opioid overdose in the United States and are commonly the first opioids encountered by individuals with opioid use disorder. It is unclear whether the pharmaceutical industry marketing of opioids to physicians is associated with mortality from overdoses.ObjectiveTo identify the association between direct-to-physician marketing of opioid products by pharmaceutical companies and mortality from prescription opioid overdoses across US counties.Design, setting, and participantsThis population-based, county-level analysis of industry marketing information used data from the Centers for Medicare & Medicaid Services Open Payments database linked with data from the Centers for Disease Control and Prevention on opioid prescribing and mortality from overdoses. All US counties were included, with data on overdoses from August 1, 2014, to December 31, 2016, linked to marketing data from August 1, 2013, to December 31, 2015, using a 1-year lag. Statistical analyses were conducted between February 1 and June 1, 2018.Main outcomes and measuresCounty-level mortality from prescription opioid overdoses, total cost of marketing of opioid products to physicians, number of marketing interactions, opioid prescribing rates, and sociodemographic factors.ResultsBetween August 1, 2013, and December 31, 2015, there were 434 754 payments totaling $39.7 million in nonresearch-based opioid marketing distributed to 67 507 physicians across 2208 US counties. After adjustment for county-level sociodemographic factors, mortality from opioid overdoses increased with each 1-SD increase in marketing value in dollars per capita (adjusted relative risk, 1.09; 95% CI, 1.05-1.12), number of payments to physicians per capita (adjusted relative risk, 1.18; 95% CI, 1.14-1.21, and number of physicians receiving marketing per capita (adjusted relative risk, 1.12; 95% CI, 1.08-1.16). Opioid prescribing rates also increased with marketing and partially mediated the association between marketing and mortality.Conclusions and relevanceIn this study, across US counties, marketing of opioid products to physicians was associated with increased opioid prescribing and, subsequently, with elevated mortality from overdoses. Amid a national opioid overdose crisis, reexamining the influence of the pharmaceutical industry may be warranted.

Project description:BACKGROUND:North America is in the midst of an unabated opioid overdose epidemic due to the increasing non-medical use of fentanyl and ultra-potent opioids. Naloxone is an effective antidote to opioid toxicity, yet its optimal dosing in the context of fentanyl and ultra-potent opioid overdoses remains unknown. This review aims to determine the relationship between the first empiric dose of naloxone and reversal of toxicity, adverse events, and the total cumulative dose required among patients with undifferentiated opioid overdoses and those with suspected toxicity from ultra-potent opioids. Secondary objectives include evaluating the relationship between the cumulative naloxone dose and toxicity reversal and adverse events, among patients with undifferentiated opioid overdoses and those with suspected toxicity from ultra-potent opioids. METHODS:To identify studies, we will search MEDLINE, Embase, CENTRAL, DARE, CDAG, CINAHL, Science Citation Index, multiple trial registries, and the gray literature. Included studies will evaluate patients with suspected or confirmed opioid toxicity from undifferentiated opioids and ultra-potent opioids, who received an empiric and possibly additional doses of naloxone. The main outcomes of interest are the relationship between naloxone dose and toxicity reversal and adverse events. We will include controlled and non-controlled interventional studies, observational studies, case reports/series, and reports from poison control centers. We will extract data and assess study quality in duplicate with discrepancies resolved by consensus or a third party. We will use the Downs and Black and Cochrane risk of bias tools for observational and randomized controlled studies. If we find sufficient variation in dose, we will fit a random effects one-stage model to estimate a dose-response relationship. We will conduct multiple subgroup analyses, including by type of opioid used and by suspected high and low prevalence of ultra-potent opioid use based on geographic location and time of the original studies. DISCUSSION:Our review will include the most up-to-date available data including ultra-potent opioids to inform the current response to the opioid epidemic, addressing the limitations of recent reviews. We anticipate limitations relating to study heterogeneity. We will disseminate study results widely to update overdose treatment guidelines and naloxone dosing in Take Home Naloxone programs.

Project description:PURPOSE:To facilitate surveillance and evaluate interventions addressing opioid-related overdoses, algorithms are needed for use in large health care databases to identify and differentiate community-occurring opioid-related overdoses from inpatient-occurring opioid-related overdose/oversedation. METHODS:Data were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient overdose/oversedation in KPNW hospitals from 1 January 2008 to 31 December 2014. Chart audits assessed accuracy; data sources included administrative and clinical records. RESULTS:The best-performing algorithm used these rules: (1) Include events with opioids administered in an inpatient setting (including emergency department/urgent care) followed by naloxone administration within 275 hours of continuous inpatient stay; (2) exclude events with electroconvulsive therapy procedure codes; and (3) exclude events in which an opioid was administered prior to hospital discharge and followed by readmission with subsequent naloxone administration. Using this algorithm, we identified 870 suspect inpatient overdose/oversedation events and chart audited a random sample of 235. Of the random sample, 185 (78.7%) were deemed overdoses/oversedation, 37 (15.5%) were not, and 13 (5.5%) were possible cases. The number of hours between time of opioid and naloxone administration did not affect algorithm accuracy. When "possible" overdoses/oversedations were included with confirmed events, overall positive predictive value (PPV) was very good (PPV = 84.0%). Additionally, PPV was reasonable when evaluated specifically for hospital stays with emergency/urgent care admissions (PPV = 77.0%) and excellent for elective surgery admissions (PPV = 97.0%). CONCLUSIONS:Algorithm performance was reasonable for identifying inpatient overdose/oversedation with best performance among elective surgery patients.

Project description:BackgroundIn response to the opioid epidemic, many states have enacted policies limiting opioid prescriptions. There is a paucity of evidence of the impact of opioid prescribing interventions in primary care populations, including whether unintended consequences arise from limiting the availability of prescribed opioids.ObjectiveOur aim was to compare changes in opioid overdose and related adverse effects rate among primary care patients following the implementation of state-level prescribing policies.DesignA cohort of primary care patients within an interrupted time series model.ParticipantsElectronic medical record data for 62,776 adult (18+ years) primary care patients from a major medical center in Vermont from January 1, 2016, to June 30, 2018.InterventionsState-level opioid prescription policy changes limiting dose and duration.Main measuresChanges in (1) opioid overdose rate and (2) opioid-related adverse effects rate per 100,000 person-months following the July 1, 2017, prescription policy change.Key resultsAmong primary care patients, there was no change in opioid overdose rate following implementation of the prescribing policy (incidence rate ratio; IRR: 0.64, 95% confidence interval; CI: 0.22-1.88). There was a 78% decrease in the opioid-related adverse effects rate following the prescribing policy (IRR: 0.22, 95%CI: 0.09-0.51). This association was moderated by opioid prescription history, with decreases observed among opioid-naïve patients (IRR: 0.18, 95%CI: 0.06-0.59) and among patients receiving chronic opioid prescriptions (IRR: 0.17, 95%CI: 0.03-0.99), but not among those with intermittent opioid prescriptions (IRR: 0.51, 95%CI: 0.09-2.82).ConclusionsLimiting prescription opioids did not change the opioid overdose rate among primary care patients, but it reduced the rate of opioid-related adverse effects in the year following the state-level policy change, particularly among patients with chronic opioid prescription history and opioid-naïve patients. Limiting the quantity and duration of opioid prescriptions may have beneficial effects among primary care patients.

Project description:BackgroundHospital patients who use illicit opioids such as heroin may use drugs during an admission or leave the hospital in order to use drugs. There have been reports of patients found dead from drug poisoning on the hospital premises or shortly after leaving the hospital. This study examines whether hospital admission and discharge are associated with increased risk of opioid-related death.Methods and findingsWe conducted a case-crossover study of opioid-related deaths in England. Our study included 13,609 deaths between January 1, 2010 and December 31, 2019 among individuals aged 18 to 64. For each death, we sampled 5 control days from the period 730 to 28 days before death. We used data from the national Hospital Episode Statistics database to determine the time proximity of deaths and control days to hospital admissions. We estimated the association between hospital admission and opioid-related death using conditional logistic regression, with a reference category of time neither admitted to the hospital nor within 14 days of discharge. A total of 236/13,609 deaths (1.7%) occurred following drug use while admitted to the hospital. The risk during hospital admissions was similar or lower than periods neither admitted to the hospital nor recently discharged, with odds ratios 1.03 (95% CI 0.87 to 1.21; p = 0.75) for the first 14 days of an admission and 0.41 (95% CI 0.30 to 0.56; p < 0.001) for days 15 onwards. 1,088/13,609 deaths (8.0%) occurred in the 14 days after discharge. The risk of opioid-related death increased in this period, with odds ratios of 4.39 (95% CI 3.75 to 5.14; p < 0.001) on days 1 to 2 after discharge and 2.09 (95% CI 1.92 to 2.28; p < 0.001) on days 3 to 14. 11,629/13,609 deaths (85.5%) did not occur close to a hospital admission, and the remaining 656/13,609 deaths (4.8%) occurred in hospital following admission due to drug poisoning. Risk was greater for patients discharged from psychiatric admissions, those who left the hospital against medical advice, and those leaving the hospital after admissions of 7 days or more. The main limitation of the method is that it does not control for time-varying health or drug use within individuals; therefore, hospital admissions coinciding with high-risk periods may in part explain the results.ConclusionsDischarge from the hospital is associated with an acute increase in the risk of opioid-related death, and 1 in 14 opioid-related deaths in England happens in the 2 weeks after the hospital discharge. This supports interventions that prevent early discharge and improve linkage with community drug treatment and harm reduction services.

Dataset Information

Identifying and classifying opioid-related overdoses: A validation study.

Publications

Identifying and classifying opioid-related overdoses: A validation study.

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