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: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: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: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
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:Background:Opioid overdose survivors have an increased risk for death. Whether use of medications for opioid use disorder (MOUD) after overdose is associated with mortality is not known. Objective:To identify MOUD use after opioid overdose and its association with all-cause and opioid-related mortality. Design:Retrospective cohort study. Setting:7 individually linked data sets from Massachusetts government agencies. Participants:17 568 Massachusetts adults without cancer who survived an opioid overdose between 2012 and 2014. Measurements:Three types of MOUD were examined: methadone maintenance treatment (MMT), buprenorphine, and naltrexone. Exposure to MOUD was identified at monthly intervals, and persons were considered exposed through the month after last receipt. A multivariable Cox proportional hazards model was used to examine MOUD as a monthly time-varying exposure variable to predict time to all-cause and opioid-related mortality. Results:In the 12 months after a nonfatal overdose, 2040 persons (11%) enrolled in MMT for a median of 5 months (interquartile range, 2 to 9 months), 3022 persons (17%) received buprenorphine for a median of 4 months (interquartile range, 2 to 8 months), and 1099 persons (6%) received naltrexone for a median of 1 month (interquartile range, 1 to 2 months). Among the entire cohort, all-cause mortality was 4.7 deaths (95% CI, 4.4 to 5.0 deaths) per 100 person-years and opioid-related mortality was 2.1 deaths (CI, 1.9 to 2.4 deaths) per 100 person-years. Compared with no MOUD, MMT was associated with decreased all-cause mortality (adjusted hazard ratio [AHR], 0.47 [CI, 0.32 to 0.71]) and opioid-related mortality (AHR, 0.41 [CI, 0.24 to 0.70]). Buprenorphine was associated with decreased all-cause mortality (AHR, 0.63 [CI, 0.46 to 0.87]) and opioid-related mortality (AHR, 0.62 [CI, 0.41 to 0.92]). No associations between naltrexone and all-cause mortality (AHR, 1.44 [CI, 0.84 to 2.46]) or opioid-related mortality (AHR, 1.42 [CI, 0.73 to 2.79]) were identified. Limitation:Few events among naltrexone recipients preclude confident conclusions. Conclusion:A minority of opioid overdose survivors received MOUD. Buprenorphine and MMT were associated with reduced all-cause and opioid-related mortality. Primary Funding Source:National Center for Advancing Translational Sciences of the National Institutes of Health.
| S-EPMC6387681 | biostudies-literature
Project description:IntroductionAmong individuals who are released from prison, opioid overdose is a leading cause of death with a risk more than ten-fold the general population. Although the epidemiology of opioid-related fatalities has been described, few studies have characterized both fatal and nonfatal opioid-related poisonings. The objective of this study was to estimate risk of fatal and nonfatal opioid overdose among adults released from prison.MethodsThe study estimated fatal and nonfatal opioid overdose rates using linked corrections, Medicaid, hospital discharge, and vital statistics from the state of Oregon from 2014 to 2018. Multivariable proportional hazards models identified demographic and prison-related factors associated with overdose.ResultsBetween 2014 and 2017, 18,258 individuals were released from prison. A majority of individuals were male (87 %) and ages 26 to 64 (83 %). Two-thirds had a documented substance use disorder treatment need and 20 % demonstrated mental health treatment need. Following prison release, 579 opioid overdose events occurred; 65 (11 %) were fatal. The rate of opioid overdose was 1085.7 per 100,000 person-years (PY). Rates were highest in the first two weeks (2286.7 per 100,000 PY), among women (1582.9 per 100,000 PY), and those with mental health (1624.3 per 100,000 PY) or substance use disorder treatment needs (1382.6 per 100,100 PY). Only mental health (adjusted hazard ratio [aHR] 1.54, 95 % CI 1.24 to 1.90) and substance use need (aHR 2.59; 95 % CI 2.01 to 3.34) remained significant in multivariable models.ConclusionsThe rate of opioid overdose is markedly elevated after prison release, particularly in the first two weeks. In women, the higher rate of opioid overdose is mediated by a greater mental health burden.
| S-EPMC10795482 | biostudies-literature
Project description:ObjectiveWe aimed to estimate risk of recurrent overdose associated with psychosocial assessment by psychiatrists during hospitalization for nonfatal overdose and prescribing patterns of psychotropic medications after discharge.MethodsA retrospective cohort study was conducted using a nationwide claims database in Japan. We classified patients aged 19-64 years hospitalized for nonfatal overdose between October 2012 and September 2013 into two cohorts: 1) those who had consulted a psychiatrist prior to overdose (n=6,790) and 2) those who had not (n=4,950). All patients were followed up from 90 days before overdose until 365 days after discharge.ResultsOverall, 15.3% of patients with recent psychiatric treatment had a recurrent overdose within 365 days, compared with 6.0% of those without psychiatric treatment. Psychosocial assessment during hospital admission had no significant effect on subsequent overdose, irrespective of treatment by psychiatrists before overdose. There was a dose-response relationship for the association of benzodiazepine prescription after overdose with subsequent overdose in either cohort, even after accounting for average daily dosage of benzodiazepines before overdose and other confounders. In patients with recent psychiatric treatment, the cumulative proportion of recurrent overdose at 365 days was 27.7% for patients receiving excessive dosages of benzodiazepines, 22.0% for those receiving high dosages, 15.3% for those receiving normal dosages, and 7.6% for those receiving no benzodiazepines. In patients without psychiatric treatment, the cumulative proportion of recurrent overdose at 365 days was 24.3% for patients receiving excessive dosages of benzodiazepines, 18.0% for those receiving high dosages, 9.0% for those receiving normal dosages, and 4.1% for those receiving no benzodiazepines.ConclusionLower dose of benzodiazepines after overdose is associated with lower risk of subsequent overdose.
| S-EPMC5342613 | biostudies-literature
Project description:BACKGROUND:Naloxone is the usual drug used in opioid-induced respiratory depression but it has a short half-life, precipitates withdrawal in dependent patients, and thus for persistent reversal of long-acting opioids has to be given by titrated doses and infusions. The partial agonist buprenorphine has a much longer duration of action and causes less severe withdrawal, but still should largely reverse respiratory depression induced by full agonist opioids. We aimed to compare the efficacy/safety of buprenorphine and naloxone in reversing respiratory depression in methadone-poisoned opioid-dependent patients. METHODS:Patients with methadone-induced respiratory depression were randomized to receive naloxone (titrated doses), or lower or higher doses of buprenorphine (10??g/kg or 15??g/kg). The primary outcome was immediate reversal of respiratory depression. We also recorded acute opioid withdrawal, need for intubation/recurrent apnea, repeated doses of opioid antagonists, length of hospital stay, other morbidity, and mortality. The study was registered with the Iranian Registry of Clinical Trials (Trial ID: 18265; Approval code: IRCT2015011020624N1). RESULTS:Eighty-five patients were randomized; 55/56 patients who received buprenorphine had rapid reversal of respiratory depression, which persisted for at least 12?h. Naloxone was effective in 28/29 patients, but often required very high titrated doses (thus delaying time to respond) and prolonged infusions. Intubation (8/29 vs 5/56) and opioid withdrawal (15/29 vs 7/56) were less common with buprenorphine. There were no serious complications or deaths in those receiving buprenorphine. The 15-?g/kg buprenorphine dose appeared to provide a longer duration of action, but precipitated withdrawal more frequently than the 10-?g/kg dose. CONCLUSION:Buprenorphine appears to be a safe and effective substitute for naloxone in overdosed opioid-dependent patients. Further studies are warranted to explore the optimal dosing strategy for buprenorphine to consistently maintain reversal of respiratory depression but not precipitate withdrawal. TRIAL REGISTRATION NUMBER:IRCT2015011020624N1. Registered 30 September 2015.
| S-EPMC7006192 | biostudies-literature