Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records

Green, Carla A.; Perrin, Nancy; Janoff, Shannon L.; Campbell, Cynthia I.; Chilcoat, Howard D.; Coplan, Paul

doi:10.1002/pds.4157

Cited by 101 publications

(90 citation statements)

References 36 publications

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“…Studies conducted using electronic health care data should validate or use outcome algorithms that have been validated in a variety of data environments in multiple relevant populations to enhance the validity and interpretability of the findings . Although there has been active research in this area of study, additional work is needed . Some investigators leveraged newly implemented PDMPs and other state‐mandated registries to overcome limitations in exposure ascertainment, identifying drug use independent of insurance coverage status or payment method.…”

Section: Discussionmentioning

confidence: 99%

Opioid analgesic dose and the risk of misuse, overdose, and death: A narrative review

Coyle

Pratt

Ocran-Appiah

et al. 2017

Pharmacoepidemiology and Drug

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Section: Discussionmentioning

confidence: 99%

Opioid analgesic dose and the risk of misuse, overdose, and death: A narrative review

Coyle

Pratt

Ocran-Appiah

et al. 2017

Pharmacoepidemiology and Drug

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“…10 Validations for these codes, with a focus on the detection of opioid overdoses, have demonstrated a high positive predictive value and high specificity across different cohorts and areas of the country. 11–14 …”

Section: Study Data and Methodsmentioning

confidence: 99%

Mortality Quadrupled Among Opioid-Driven Hospitalizations, Notably Within Lower-Income And Disabled White Populations

Song

2017

Health Affairs

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Hospitals play an important role in caring for patients in the current opioid crisis, but data on the outcomes and composition of opioid-driven hospitalizations in the United States have been lacking. Nationally representative all-payer data for the period 1993–2014 from the National Inpatient Sample were used to compare the mortality rates and composition of hospitalizations with opioid-related primary diagnoses and those of hospitalizations for other drugs and for all other causes. Mortality among opioid-driven hospitalizations increased from 0.43 percent before 2000 to 2.02 percent in 2014, an average increase of 0.12 percentage points per year relative to the mortality of hospitalizations due to other drugs—which was unchanged. While the total volume of opioid-driven hospitalizations remained relatively stable, it shifted from diagnoses mostly involving opioid dependence or abuse to those centered on opioid or heroin poisoning (the latter have higher case fatality rates). After 2000, hospitalizations for opioid/heroin poisoning grew by 0.01 per 1,000 people per year, while hospitalizations for opioid dependence or abuse declined by 0.01 per 1,000 people per year. Patients admitted for opioid/heroin poisoning were more likely to be white, ages 50–64, Medicare beneficiaries with disabilities, and residents of lower-income areas. As the United States combats the opioid epidemic, efforts to help hospitals respond to the increasing severity of opioid intoxication are needed, especially in vulnerable populations.

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“…To date, few studies have validated methods used to identify OODs . The purpose of this study was to improve upon and conduct a full validation of a previously developed algorithm to identify overdose events and to develop and validate algorithms that classify types of overdoses (eg, heroin‐related and suicides). The original study was limited in scope, excluding events that occurred within 3 days of surgery, and only assessed positive predictive value (PPV).…”

Section: Introductionmentioning

confidence: 99%

Identifying and classifying opioid‐related overdoses: A validation study

Green

Perrin

Hazlehurst

et al. 2019

Pharmacoepidemiology and Drug

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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.

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Assessing the accuracy of opioid overdose and poisoning codes in diagnostic information from electronic health records, claims data, and death records

Cited by 101 publications

References 36 publications

Opioid analgesic dose and the risk of misuse, overdose, and death: A narrative review

Opioid analgesic dose and the risk of misuse, overdose, and death: A narrative review

Mortality Quadrupled Among Opioid-Driven Hospitalizations, Notably Within Lower-Income And Disabled White Populations

Identifying and classifying opioid‐related overdoses: A validation study

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