Dataset Information

Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data.

ABSTRACT: 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.

SUBMITTER: Green CA

PROVIDER: S-EPMC6767384 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data.

Green Carla A CA Hazlehurst Brian B Brandes John J Sapp Daniel S DS Janoff Shannon L SL Coplan Paul M PM DeVeaugh-Geiss Angela A

Pharmacoepidemiology and drug safety 20190516 8

<h4>Purpose</h4>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.<h4>Methods</h4>Data were from Kaiser Permanente Northwest (KPNW), a large integrated health plan. We iteratively developed and evaluated an algorithm for electronically identifying inpatient ove ...[more]

PMID: 31095831

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Project description:PurposeTo enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.MethodsWe developed a natural language processing (NLP) software application to code clinical text documentation of overdose, including identification of intention for self-harm, substances involved, substance abuse, and error in medication usage. Using datasets balanced with cases of suspected overdose and records of individuals at elevated risk for overdose, we developed and validated the application using Kaiser Permanente Northwest data, then tested portability of the application using Kaiser Permanente Washington data. Datasets were chart-reviewed to provide a gold standard for comparison and evaluation of the automated method.ResultsThe method performed well in identifying overdose (sensitivity = 0.80, specificity = 0.93), intentional overdose (sensitivity = 0.81, specificity = 0.98), and involvement of opioids (excluding heroin, sensitivity = 0.72, specificity = 0.96) and heroin (sensitivity = 0.84, specificity = 1.0). The method performed poorly at identifying adverse drug reactions and overdose due to patient error and fairly at identifying substance abuse in opioid-related unintentional overdose (sensitivity = 0.67, specificity = 0.96). Evaluation using validation datasets yielded significant reductions, in specificity and negative predictive values only, for many classifications mentioned above. However, these measures remained above 0.80, thus, performance observed during development was largely maintained during validation. Similar results were obtained when evaluating portability, although there was a significant reduction in sensitivity for unintentional overdose that was attributed to missing text clinical notes in the database.ConclusionsMethods that process text clinical notes show promise for improving accuracy and fidelity at identifying and classifying overdoses according to type using EHR data.

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: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.

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:BackgroundIdentification of people with HIV from electronic health record (EHR) data is an essential first step in the study of important HIV outcomes, such as risk assessment. This task has been historically performed via manual chart review, but the increased availability of large clinical data sets has led to the emergence of phenotyping algorithms to automate this process. Existing algorithms for identifying people with HIV rely on a combination of International Classification of Disease codes and laboratory tests or closely mimic clinical testing guidelines for HIV diagnosis. However, we found that existing algorithms in the literature missed a significant proportion of people with HIV in our data.ObjectiveThe aim of this study is to develop and evaluate HIV-Phen, an updated criteria-based HIV phenotyping algorithm.MethodsWe developed an algorithm using HIV-specific laboratory tests and medications and compared it with previously published algorithms in national and local data sets to identify cohorts of people with HIV. Cohort demographics were compared with those reported in the national and local surveillance data. Chart reviews were performed on a subsample of patients from the local database to calculate the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the algorithm.ResultsOur new algorithm identified substantially more people with HIV in both national (up to an 85.75% increase) and local (up to an 83.20% increase) EHR databases than the previously published algorithms. The demographic characteristics of people with HIV identified using our algorithm were similar to those reported in national and local HIV surveillance data. Our algorithm demonstrated improved sensitivity over existing algorithms (98% vs 56%-92%) while maintaining a similar overall accuracy (96% vs 80%-96%).ConclusionsWe developed and evaluated an updated criteria-based phenotyping algorithm for identifying people with HIV in EHR data that demonstrates improved sensitivity over existing algorithms.

Dataset Information

Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data.

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Development of an algorithm to identify inpatient opioid-related overdoses and oversedation using electronic data.

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