Dataset Information

Risk Factors for Severe Opioid-Related Adverse Events in a National Cohort of Medical Hospitalizations.

ABSTRACT:

Background

Opioids are a leading cause of adverse drug events in the hospital. Guidelines recommend that physicians assess the risks of opioids and discuss them with patients when considering opioid use. There are no studies examining patient- and prescribing-related risk factors for opioid-related adverse drug events (ORADEs) in hospitalized medical patients.

Objective

To identify independent risk factors for severe ORADEs in hospitalized medical patients.

Design

Retrospective cohort study.

Patients

Medical patients hospitalized at US, non-federal, and acute care facilities, with at least one pharmacy charge for an opioid during hospitalization. We excluded patients with metastatic malignancy, hospice, or palliative care billing codes.

Main measures

We used Cox proportional hazards modeling to identify risk factors for severe ORADEs, defined by a pharmacy charge for naloxone. Candidate risk factors were chosen a priori, based on clinical grounds and prior literature.

Key results

Among 731,208 hospitalizations (median age 60, 56.5% female), a severe ORADE occurred in 2727 (0.4%). Independent risk factors included patient characteristics (advanced age, female gender), comorbidities (congestive heart failure, opioid abuse/dependence, non-opioid drug abuse/dependence, psychosis, depression, obstructive sleep apnea), organ failures on admission (respiratory failure, shock/hypotension, renal failure, hepatic failure, acidosis, and neurologic failure), medication co-administrations (antipsychotics and short-acting benzodiazepines), and characteristics of the opioid prescriptions themselves (total dose for the day, parenteral route of administration, and receipt of multiple types of opioids in a day). Although a risk prediction model derived from these factors performed well on stratified k-fold cross-validation (average c-statistics 0.68-0.71), the low incidence of the outcome limited the positive predictive value of the risk score.

Conclusions

In this national cohort of medical patients, we identified several risk factors for ORADEs that can be used to inform physician decision-making, conversations with patients about risk, and development and targeting of harm reduction strategies for at-risk populations.

SUBMITTER: Herzig SJ

PROVIDER: S-EPMC7018928 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk Factors for Severe Opioid-Related Adverse Events in a National Cohort of Medical Hospitalizations.

Herzig Shoshana J SJ Stefan Mihaela S MS Pekow Penelope S PS Shieh Meng-Shiou MS Soares William W Raghunathan Karthik K Lindenauer Peter K PK

Journal of general internal medicine 20191114 2

<h4>Background</h4>Opioids are a leading cause of adverse drug events in the hospital. Guidelines recommend that physicians assess the risks of opioids and discuss them with patients when considering opioid use. There are no studies examining patient- and prescribing-related risk factors for opioid-related adverse drug events (ORADEs) in hospitalized medical patients.<h4>Objective</h4>To identify independent risk factors for severe ORADEs in hospitalized medical patients.<h4>Design</h4>Retrospec ...[more]

PMID: 31728892

Similar Datasets

Project description:BackgroundAlthough opioids are initiated on hospital discharge in millions of older adults each year, there are no studies examining patient- and prescribing-related risk factors for opioid-related adverse drug events (ADEs) after hospital discharge among medical patients.MethodsA retrospective cohort study of a national sample of Medicare beneficiaries aged 65 years and older, hospitalized for a medical reason, with at least one claim for an opioid within 2 days of hospital discharge. We excluded patients receiving hospice care and patients admitted from or discharged to a facility. We used administrative billing codes and medication claims to define potential opioid-related ADEs within 30 days of hospital discharge, and competing risks regression to identify risk factors for these events.ResultsAmong 22,879 medical hospitalizations (median age 74, 36.9% female) with an opioid claim within 2 days of hospital discharge, a potential opioid-related ADE occurred in 1604 (7.0%). Independent risk factors included age of 80 years and older (HR 1.18, 95% CI 1.05-1.33); clinical conditions, including kidney disease (HR 1.22, 95% CI 1.08-1.37), dementia/delirium (HR 1.38, 95% CI 1.22-1.56), anxiety disorder (HR 1.20, 95% CI 1.06-1.36), opioid use disorder (HR 1.20, 95% CI 1.03-1.39), intestinal disorders (HR 1.31, 95% CI 1.15-1.49), pancreaticobiliary disorders (HR 1.32, 95% CI 1.09-1.61), and musculoskeletal and nervous system injuries (HR 1.35, 95% CI 1.17-1.54); red flags for opioid misuse (HR 1.37, 95% CI 1.04-1.80); opioid use in the 30 days before hospitalization (HR 1.20, 95% CI 1.08-1.34); and prescription of long-acting opioids (HR 1.34, 95% CI 1.06-1.70).ConclusionsPotential opioid-related ADEs occurred within 30 days of hospital discharge in 7.0% of older adults discharged from a medical hospitalization with an opioid prescription. Identified risk factors can be used to inform physician decision-making, conversations with older adults about risk, and development and targeting of harm reduction strategies.

Project description:BackgroundAny opioid-related hospitalization is an indicator of opioid-related harm and should ideally trigger carefully monitored decreases in opioid prescribing after inpatient stays in many, if not most, cases. However, past studies on opioid prescribing after hospitalizations have largely been limited to overdose related visits. It is unclear whether prescribing is different for other opioid-related indications such as opioid dependence and abuse and how that may compare with hospitalizations for overdose.ObjectiveTo examine opioid-prescribing patterns before and after opioid-related hospitalizations for all opioid-related indications, not limited to overdose.Research designRetrospective cohort analysis of Veterans Health Administration (VHA) administrative claims from 2011 to 2014.SubjectsVHA patients who were hospitalized between fiscal years 2011 and 2014 and had at least 1 prescription opioid medication filled through the VHA pharmacy before their hospitalization.MeasuresOpioid dispensing trajectories after hospitalization by opioid-related indication (ie, opioid dependence and/or abuse vs. overdose) compared with prescribing patterns for non-opioid-related hospitalizations.ResultsOverall, opioid dosage dropped significantly (66% for dependence/abuse, 42% for overdose, and 3% for nonopioid diagnoses; P<0.001) across all 3 categories when comparing dose 57-63 days after admission to 57-63 days before hospitalization. However, 47% of the patients remained on the same dose or increased their opioid dose at 60 days after an opioid-related hospitalization. After adjusting for covariates, patients with a primary diagnosis of dependence/abuse had higher odds of having their dose discontinued compared with those with overdose: odds ratio (OR) 2.17 (1.19-3.96). Patients with admissions for opioid dependence and/or abuse had a statistically significant higher prevalence of depression, posttraumatic stress disorder, anxiety, and substance use disorders compared with those with an opioid overdose hospitalization.ConclusionsOpioid prescribing and patient risk factors before and after opioid-related hospitalizations vary by indication for hospitalization. To reduce costs and morbidity associated with opioid-related hospitalizations, opioid deintensification efforts need to be tailored to indication for hospitalization.

Dataset Information

Risk Factors for Severe Opioid-Related Adverse Events in a National Cohort of Medical Hospitalizations.

Background

Objective

Design

Patients

Main measures

Key results

Conclusions

Publications

Risk Factors for Severe Opioid-Related Adverse Events in a National Cohort of Medical Hospitalizations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets