A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients.
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ABSTRACT:
SUBMITTER: Chang HY
PROVIDER: S-EPMC6713520 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
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A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients.
Chang Hsien-Yen HY Krawczyk Noa N Schneider Kristin E KE Ferris Lindsey L Eisenberg Matthew M Richards Tom M TM Lyons B Casey BC Jackson Kate K Weiner Jonathan P JP Saloner Brendan B
Drug and alcohol dependence 20190607
<h4>Background</h4>Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences.<h4>Methods</h4>We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among ...[more]
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Project description:ImportanceA recent increase in patients presenting with nonfatal opioid overdoses has focused clinical attention on characterizing their risks of premature mortality.ObjectiveTo describe all-cause mortality rates, selected cause-specific mortality rates, and standardized mortality rate ratios (SMRs) of adults during their first year after nonfatal opioid overdose.Design, setting, and participantsThis US national longitudinal study assesses a cohort of patients aged 18 to 64 years who were Medicaid beneficiaries and experienced nonfatal opioid overdoses. The Medicaid data set included the years 2001 through 2007. Death record information was obtained from the National Death Index. Data analysis occurred from October 2017 to January 2018.Main outcomes and measuresCrude mortality rates per 100 000 person-years were determined in the first year after nonfatal opioid overdose. Standardized mortality rate ratios (SMR) were estimated for all-cause and selected cause-specific mortality standardized to the general population with respect to age, sex, and race/ethnicity.ResultsThe primary cohort included 76 325 adults and 66 736 person-years of follow-up. During the first year after nonfatal opioid overdose, there were 5194 deaths, the crude death rate was 778.3 per 10 000 person-years, and the all-cause SMR was 24.2 (95% CI, 23.6-24.9). The most common immediate causes of death were substance use-associated diseases (26.2%), diseases of the circulatory system (13.2%), and cancer (10.3%). For every cause examined, SMRs were significantly elevated, especially with respect to drug use-associated diseases (SMR, 132.1; 95% CI, 125.6-140.0), HIV (SMR, 45.9; 95% CI, 39.5-53.0), chronic respiratory diseases (SMR, 41.1; 95% CI, 36.0-46.8), viral hepatitis (SMR, 30.6; 95% CI, 22.9-40.2), and suicide (SMR, 25.9; 95% CI, 22.6-29.6), particularly including suicide among females (SMR, 47.9; 95% CI, 39.8-52.3).Conclusions and relevanceIn a US national cohort of adults who had experienced a nonfatal opioid overdose, a marked excess of deaths was attributable to a wide range of substance use-associated, mental health, and medical conditions, underscoring the importance of closely coordinating the substance use, mental health, and medical care of this patient population.
| S-EPMC6143082 | biostudies-literature
Project description:ImportanceResponding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems.ObjectiveTo develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data.Design, setting, and participantsA cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019.ExposuresControlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters.Main outcomes and measuresFatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016.ResultsThere were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85.Conclusions and relevanceIn this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.
| S-EPMC7315388 | biostudies-literature
Project description:ObjectivesOpioid-related overdose is a public health emergency in the United States. Meanwhile, high-deductible health plans (HDHPs) have become more prevalent in the United States over the last 2 decades, raising concern about their potential for discouraging high-need populations, like those with opioid use disorder (OUD), from engaging in care that may mitigate the probability of overdose. This study assesses the impact of an employer offering an HDHP on nonfatal opioid overdose among commercially insured individuals with OUD in the United States.Research designWe used deidentified insurance claims data from 2007 to 2017 with 97,788 person-years. We used an intent-to-treat, difference-in-differences regression framework to estimate the change in the probability of a nonfatal opioid overdose among enrollees with OUD whose employers began offering an HDHP insurance option during the study period compared with the change among those whose employer never offered an HDHP. We also used an event-study model to account for dynamic time-varying treatment effects.ResultsAcross both comparison and treatment groups, 2% of the sample experienced a nonfatal opioid overdose during the study period. Our primary model and robustness checks revealed no impact of HDHP offer on the probability of a nonfatal overdose.ConclusionsOur study suggests that HDHP offer was not associated with an observed increase in the probability of nonfatal opioid overdose among commercially insured person-years with OUD. However, given the strong evidence that medications for OUD (MOUD) can reduce the risk of overdose, research should explore which facets of insurance design may impact MOUD use.
| S-EPMC10527154 | biostudies-literature
Project description:BackgroundLittle is known about risk factors for repeated opioid overdose and fatal opioid overdose in the first year following nonfatal opioid overdose.MethodsWe identified a national retrospective longitudinal cohort of patients aged 18-64 years in the Medicaid program who received a clinical diagnosis of nonfatal opioid overdose. Repeated overdoses and fatal opioid overdoses were measured with the Medicaid record and the National Death Index. Rates of repeat overdose per 1000 person-years and fatal overdose per 100,000 person-years were determined. Hazard ratios of repeated opioid overdose and fatal opioid overdose were estimated by Cox proportional hazards.ResultsNearly two-thirds (64.8%) of the patients with nonfatal overdoses (total n = 75,556) had filled opioid prescriptions in the 180 days before initial overdose. During the 12 months after nonfatal overdose, the rate of repeat overdose was 295.0 per 1000 person-years and that of fatal opioid overdose was 1154 per 100,000 person-years. After controlling for age, sex, race/ethnicity, and region, the hazard of fatal opioid overdose was increased for patients who had filled a benzodiazepine prescription in the 180 days prior to their initial overdose (HR = 1.71, 95%CI: 1.46-1.99), whose initial overdose involved heroin (HR = 1.57, 95%CI:1.30-1.89), or who required mechanical ventilation at the initial overdose (HR = 1.86, 95%CI = 1.50-2.31).ConclusionsAdults treated for opioid overdose frequently have repeated opioid overdoses in the following year. They are also at high risk of fatal opioid overdose throughout this period, which underscores the importance of efforts to engage and maintain patients in evidence-based opioid treatments following nonfatal overdose.
| S-EPMC10398609 | biostudies-literature
Project description:IntroductionOpioid and sedative/hypnotic drug overdoses are major causes of morbidity in the U.S. This study compares 12-month incidence of fatal unintentional drug overdose, suicide, and other mortality among emergency department patients presenting with nonfatal opioid or sedative/hypnotic overdose.MethodsThis is a retrospective cohort study using statewide, longitudinally linked emergency department patient record and mortality data from California. Participants comprised all residents presenting to a licensed emergency department at least once in 2009-2011 with nonfatal unintentional opioid overdose, sedative/hypnotic overdose, or neither (a 5% random sample). Participants were followed for 1 year after index emergency department presentation to assess death from unintentional overdose, suicide, or other causes, ascertained using ICD-10 codes. Absolute death rates per 100,000 person years and standardized mortality ratios relative to the general population were calculated. Data were analyzed February-August 2019.ResultsFollowing the index emergency department visit, unintentional overdose death rates per 100,000 person years were 1,863 following opioid overdose, 342 following sedative/hypnotic overdose, and 31 for reference patients without an index overdose (respective standardized mortality ratios of 106.1, 95% CI=95.2, 116.9; 24.5, 95% CI=21.3, 27.6; and 2.6, 95% CI=2.2, 3.0). Suicide mortality rates per 100,000 were 319, 174, and 32 following opioid overdose, sedative/hypnotic overdose, and reference visits, respectively. Natural causes mortality rates per 100,000 were 8,058 (opioid overdose patients), 17,301 (sedative/hypnotic overdose patients), and 3,097 (reference patients).ConclusionsEmergency department patients with nonfatal opioid or sedative/hypnotic drug overdose have exceptionally high risks of death from unintentional overdose, suicide, and other causes. Emergency department-based interventions offer potential for reducing these patients' overdose and other mortality risks.
| S-EPMC7311279 | biostudies-literature
Project description:BackgroundSurvivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose.ObjectiveTo predict risk of death after a nonfatal opioid overdose.Design and participantsThis retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period.Exposures, main outcome, and measuresPredictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup.Key resultsOf eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001).ConclusionsA risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.
| S-EPMC8041978 | biostudies-literature
Project description:Importance:Timely initiation and referral to treatment for patients with opioid use disorder seen in the emergency department is associated with reduced mortality. It is not known how often commercially insured adults obtain follow-up treatment after nonfatal opioid overdose. Objective:To investigate the incidence of follow-up treatment following emergency department discharge after nonfatal opioid overdose and patient characteristics associated with receipt of follow-up treatment. Design, Setting, and Participants:A retrospective cohort study was conducted using an administrative claims database for a large US commercial insurer, from October 1, 2011, to September 30, 2016. Data analysis was performed from May 1, 2019, to September 26, 2019. Adult patients discharged from the emergency department after an index opioid overdose (no overdose in the preceding 90 days) were included. Patients with cancer and without continuous insurance enrollment were excluded. Main Outcomes and Measures:The primary outcome was follow-up treatment in the 90 days following overdose, defined as a combined outcome of claims for treatment encounters or medications for opioid use disorder (buprenorphine and naltrexone). Analysis was stratified by whether patients received treatment for opioid use disorder in the 90 days before the overdose. Logistic regression models were used to identify patient characteristics associated with receipt of follow-up treatment. Marginal effects were used to report the average adjusted probability and absolute risk differences (ARDs) in follow-up for different patient characteristics. Results:A total of 6451 patients were identified with nonfatal opioid overdose; the mean (SD) age was 45.0 (19.3) years, 3267 were women (50.6%), and 4676 patients (72.5%) reported their race as non-Hispanic white. A total of 1069 patients (16.6%; 95% CI, 15.7%-17.5%) obtained follow-up treatment within 90 days after the overdose. In adjusted analysis of patients who did not receive treatment before the overdose, black patients were half as likely to obtain follow-up compared with non-Hispanic white patients (ARD, -5.9%; 95% CI, -8.6% to -3.6%). Women (ARD, -1.7%; 95% CI, -3.3% to -0.5%) and Hispanic patients (ARD, -3.5%; 95% CI, -6.1% to -0.9%) were also less likely to obtain follow-up. For each additional year of age, patients were 0.2% less likely to obtain follow-up (95% CI, -0.3% to -0.1%). Conclusions and Relevance:Efforts to improve the low rate of timely follow-up treatment following opioid overdose may seek to address sex, race/ethnicity, and age disparities.
| S-EPMC7254182 | biostudies-literature
Project description:ImportanceOverdoses continue to increase in the US, but the contribution of methamphetamine use is understudied in rural communities.ObjectiveTo estimate the prevalence of methamphetamine use and its correlates among people who use drugs (PWUD) in rural US communities and to determine whether methamphetamine use is associated with increased nonfatal overdoses.Design, setting, and participantsFrom January 2018 through March 2020, the National Rural Opioid Initiative conducted cross-sectional surveys of PWUD in rural communities in 10 states (Illinois, Kentucky, New Hampshire, Massachusetts, North Carolina, Ohio, Oregon, Vermont, West Virginia, and Wisconsin). Participants included rural PWUD who reported any past-30-day injection drug use or noninjection opioid use to get high. A modified chain-referral sampling strategy identified seeds who referred others using drugs. Data analysis was performed from May 2021 to January 2022.ExposuresUse of methamphetamine alone, opioids alone, or both.Main outcomes and measuresUnweighted and weighted prevalence of methamphetamine use, any past-180-day nonfatal overdose, and number of lifetime nonfatal overdoses.ResultsAmong the 3048 participants, 1737 (57%) were male, 2576 (85%) were White, and 225 (7.4%) were American Indian; the mean (SD) age was 36 (10) years. Most participants (1878 of 2970 participants with any opioid or methamphetamine use [63%]) reported co-use of methamphetamine and opioids, followed by opioids alone (702 participants [24%]), and methamphetamine alone (390 participants [13%]). The estimated unweighted prevalence of methamphetamine use was 80% (95% CI, 64%-90%), and the estimated weighted prevalence was 79% (95% CI, 57%-91%). Nonfatal overdose was greatest in people using both methamphetamine and opioids (395 of 2854 participants with nonmissing overdose data [22%]) vs opioids alone (99 participants [14%]) or methamphetamine alone (23 participants [6%]). Co-use of methamphetamine and opioids was associated with greater nonfatal overdose compared with opioid use alone (adjusted odds ratio, 1.45; 95% CI, 1.08-1.94; P = .01) and methamphetamine use alone (adjusted odds ratio, 3.26; 95% CI, 2.06-5.14; P < .001). Those with co-use had a mean (SD) of 2.4 (4.2) (median [IQR], 1 [0-3]) lifetime overdoses compared with 1.7 (3.5) (median [IQR], 0 [0-2]) among those using opioids alone (adjusted rate ratio, 1.20; 95% CI, 1.01-1.43; P = .04), and 1.1 (2.9) (median [IQR], 0 [0-1]) among those using methamphetamine alone (adjusted rate ratio, 1.81; 95% CI, 1.45-2.27; P < .001). Participants with co-use most often reported having tried and failed to access substance use treatment: 827 participants (44%) for both, 117 participants (30%) for methamphetamine alone, and 252 participants (36%) for opioids alone (χ22 = 33.8; P < .001). Only 66 participants (17%) using methamphetamine alone had naloxone.Conclusions and relevanceThese findings suggest that harm reduction and substance use disorder treatment interventions must address both methamphetamine and opioids to decrease overdose in rural communities.
| S-EPMC9379740 | biostudies-literature
Project description:BackgroundOpioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem.ObjectiveThe objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD.MethodsWe performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR).ResultsThe strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes.ConclusionsThis is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.
| S-EPMC8663596 | biostudies-literature
Project description:BackgroundNaloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.ObjectiveTo develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.DesignRetrospective cohort.SettingDerivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.ParticipantsWe developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients.Main measuresPotential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed.Key resultsA five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic?=?0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic?=?0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.ConclusionsAmong patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.
| S-EPMC6153224 | biostudies-literature