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Prevalence and characteristics of hospitalized adults on chronic opioid therapy.

ABSTRACT: As chronic opioid therapy (COT) becomes more common, complexity of pain management in the inpatient setting increases; little is known about medical inpatients on COT.To determine the prevalence of COT among hospitalized patients and to compare outcomes among these patients relative to those not receiving COT.Observational study of inpatient and outpatient administrative data.All veterans with acute medical admissions to 129 Veterans Administration hospitals during fiscal years 2009 to 2011, residing in the community, and with outpatient pharmacy use.We defined COT as 90 or more days of opioids prescribed in the 6 months prior to hospitalization. Patient characteristics included demographic variables and major comorbidities. Outcomes included 30-day readmission and death during hospitalization or within 30 days, with associations ascertained using multivariable logistic regression.Of 122,794 hospitalized veterans, 31,802 (25.9%) received COT. These patients differed from comparators in age, sex, race, residence, and presence of chronic noncancer pain, chronic obstructive pulmonary disease, complicated diabetes, cancer, and mental health diagnoses including post-traumatic stress disorder. After adjustment for demographic factors, comorbidities, and admission diagnosis, COT was associated with hospital readmission (odds ratio [OR]: 1.15, 95% confidence interval [CI]: 1.10-1.20) and death (OR: 1.19, 95% CI: 1.10-1.29).COT is common among medical inpatients. Patients on COT differ from patients without COT beyond dissimilarities in pain and cancer diagnoses. Occasional and chronic opioid use are associated with increased risk of hospital readmission, and COT is associated with increased risk of death. Additional research relating COT to hospitalization outcomes is warranted.

SUBMITTER: Mosher HJ

PROVIDER: S-EPMC4197819 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Prevalence and characteristics of hospitalized adults on chronic opioid therapy.

Mosher Hilary J HJ Jiang Lan L Vaughan Sarrazin Mary S MS Cram Peter P Kaboli Peter J PJ Vander Weg Mark W MW

Journal of hospital medicine 20131206 2

<h4>Background</h4>As chronic opioid therapy (COT) becomes more common, complexity of pain management in the inpatient setting increases; little is known about medical inpatients on COT.<h4>Objective</h4>To determine the prevalence of COT among hospitalized patients and to compare outcomes among these patients relative to those not receiving COT.<h4>Design</h4>Observational study of inpatient and outpatient administrative data.<h4>Participants</h4>All veterans with acute medical admissions to 12 ...[more]

PMID: 24311455

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Project description:BACKGROUND:Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ??90-day supply of opioids with <?30-day gap in supply over a 180-day period or receipt of ??10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge. OBJECTIVE:The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization. DESIGN:Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression. PATIENTS:Hospitalized patients at an urban, safety net hospital. MAIN MEASUREMENTS:Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others. KEY RESULTS:Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients. CONCLUSIONS:Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

Project description:BackgroundOpioids have been prescribed and used for chronic noncancer pain at prolific rates in the United States during the past 2 decades. Patients who transition to incident chronic opioid therapy are at increased risk for significant negative health consequences, including cardiovascular risk, endocrine disorders, opioid use disorder, and death.ObjectiveTo identify the leading predictors associated with transitioning to incident chronic opioid therapy among working-age adults without cancer.MethodThis retrospective observational cohort study is based on medical and pharmacy claims of a nationally representative sample of adults enrolled in commercial health insurance plans. Standard parametric (logistic regressions) and nonparametric methods based on a decision tree were used for prediction. To facilitate comparison with the available published literature, we also present adjusted odds ratios (AORs) and 95% confidence intervals (CIs). The 10% random sample of 491,442 patients included in the study who were working-age adults (age, 28-63 years) were insured in a commercial health plan, did not have cancer, and initiated opioid therapy between January 2007 and May 2015. Transition to incident chronic opioid therapy was defined as having claims for at least a 90-day supply of opioids within 120 days after the index date (ie, initiation of opioid therapy). Predictive models used for the analysis comprised a comprehensive list of factors available in the claims data, including opioid regimen characteristics, pain conditions, physical and mental health conditions, concomitant medications use (ie, benzodiazepine, stimulants, nonopioid analgesics, and polypharmacy), patient characteristics, and health insurance type.ResultsIn our sample, the transition to incident chronic opioid therapy was 1.3% and pain-specific diagnoses were documented for only one-third (31.7%) of patients. The 4 leading predictors of chronic opioid therapy were opioid duration of action (AOR, 12.28; 95% CI, 8.06-18.72), the parent opioid compound (eg, tramadol vs codeine; AOR, 7.26; 95% CI, 5.20-10.13), the presence of conditions that are very likely to cause chronic pain (AOR, 5.47; 95% CI, 3.89-7.68), and drug use disorders (AOR, 4.02; 95% CI, 2.53-6.40).ConclusionThe initial opioid regimen's characteristics are powerful predictors of chronic opioid therapy. Predictive algorithms created from readily available claims data can be used to develop real-time predictions of the future risk for a patient's transition to chronic opioid use.

Project description:ImportancePatterns of preoperative opioid use are not well characterized across different surgical services, and studies in this patient population have lacked important self-reported data of pain and affect.ObjectivesTo assess the prevalence of preoperative opioid use and the characteristics of these patients in a broadly representative surgical cohort.Design, setting, and participantsCross-sectional, observational study of patients undergoing surgery at a tertiary care academic medical center. Data were collected as a part of large prospective institutional research registries from March 1, 2010, through April 30, 2016.ExposuresPreoperative patient and procedural characteristics, including prospectively assessed self-reported pain and functional measures.Main outcomes and measuresPatient-reported opioid use before surgery.ResultsOf the total 34 186 patients recruited (54.2% women; mean [SD] age, 53.1 [16.1] years), preoperative opioid use was reported in 7894 (23.1%). The most common opioids used were hydrocodone bitartrate (4685 [59.4%]), tramadol hydrochloride (1677 [21.2%]), and oxycodone hydrochloride (1442 [18.3%]). Age of 31 to 40 years (adjusted odds ratio [aOR], 1.26; 95% CI, 1.10-1.45), tobacco use (former use aOR, 1.32 [95% CI, 1.22-1.42]; current use aOR, 1.62 [95% CI, 1.48-1.78]), illicit drug use (aOR, 1.74; 95% CI, 1.16-2.60), higher pain severity (aOR, 1.33; 95% CI, 1.31-1.35), depression (aOR, 1.22; 95% CI, 1.12-1.33), higher Fibromyalgia Survey scores (aOR, 1.06, 95% CI, 1.05-1.07), lower life satisfaction (aOR, 0.95, 95% CI, 0.93-0.96), and more medical comorbidities (American Society of Anesthesiology score aOR, 1.47 [95% CI, 1.37-1.58]; Charlson Comorbidity Index aOR, 1.29 [95% CI, 1.18-1.41]) were all independently associated with preoperative opioid use. Preoperative opioid use was most commonly reported by patients undergoing orthopedic (226 [65.1%]) and neurosurgical spinal (596 [55.1%]) procedures and least common among patients undergoing thoracic procedures (244 [15.7%]). After adjusting for patient characteristics, the patients undergoing lower extremity procedures were most likely to report preoperative opioid use (aOR, 3.61; 95% CI, 2.81-4.64), as well as those undergoing pelvic (excluding hip) (aOR, 3.09; 95% CI, 1.88-5.08), upper extremity (aOR, 3.07; 95% CI, 2.12-4.45), and spinal or spinal cord (aOR, 2.68; 95% CI, 2.15-3.32) procedures, with the group undergoing intrathoracic surgery as the reference group.Conclusions and relevanceIn this large study of preoperative opioid use that includes patient-reported outcome measures, more than 1 in 4 patients presenting for surgery reported opioid use. These data provide important insights into this complicated patient population that would appear to help guide future preoperative optimization and perioperative opioid-weaning interventions.

Project description:ImportanceAttempts to discontinue opioid therapy to reduce the risk of overdose and adhere to prescribing guidelines may lead patients to be exposed to variability in opioid dosing. Such dose variability may increase the risk of opioid overdose even if therapy discontinuation is associated with a reduction in risk.ObjectiveTo examine the association between opioid dose variability and opioid overdose.Design, setting, and participantsA nested case-control study was conducted in a large Colorado integrated health plan and delivery system from January 1, 2006, through June 30, 2018. Cohort members were individuals prescribed long-term opioid therapy.ExposuresDose variability was defined as the SD of the milligrams of morphine equivalents across each patient's follow-up and categorized based on the quintile distribution of the SD in the cohort (0-5.3, 5.4-9.1, 9.2-14.6, 14.7-27.2, and >27.2 mg of morphine equivalents).Main outcomes and measuresOpioid overdose cases were identified using International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Each case patient with overdose was matched to up to 20 control patients using risk set sampling. Conditional logistic regression models were used to generate matched odds ratios and 95% CIs, adjusted for age, sex, race/ethnicity, drug or alcohol use disorder, tobacco use, benzodiazepine dispensings, medical comorbidities, mental health disorder, opioid dose, and opioid formulation.ResultsIn a cohort of 14 898 patients (mean [SD] age, 56.3 [16.0] years; 8988 [60.3%] female) prescribed long-term opioid therapy, 228 case patients with incident opioid overdose were matched to 3547 control patients. The mean (SD) duration of opioid therapy was 36.7 (33.7) months in case patients and 33.0 (30.9) months in control patients. High-dose variability (SD >27.2 mg of morphine equivalents) was associated with a significantly increased risk of overdose compared with low-dose variability (matched odds ratio, 3.32; 95% CI, 1.63-6.77) independent of opioid dose.Conclusions and relevanceVariability in opioid dose may be a risk factor for opioid overdose, suggesting that practitioners should seek to minimize dose variability when managing long-term opioid therapy.

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Prevalence and characteristics of hospitalized adults on chronic opioid therapy.

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Prevalence and characteristics of hospitalized adults on chronic opioid therapy.

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