Abstract:Objectives: To identify the incidence, characteristics, and predictors for 30 and 90-day readmission among acutely hospitalized patients with opioid use disorder (OUD). Methods: This retrospective, cohort study evaluated consecutive adults with OUD admitted to an academic medical center over a 5-year period (10/1/11 to 9/30/16). Multivariable logistic regression was used to determine independent predictors for 30 and 90-day readmissions based on pertinent admission, hospital, and discharge variables collected … Show more
“…One quarter of patients in this subtype had been diagnosed with a mental health condition in our health system, and this subtype had the highest proportion of patients leaving against medical advice. Buprenorphine initiation during the hospital encounter has been shown to reduce hospitalizations in a similar cohort of patients with heroin use [44]. The high rates of uninsured status, low SES metrics from the census-tract variables (% in poverty, unemployed, education, and median household earnings), and high rates of mental health conditions imply behavioral and social determinants of health are important considerations in this subtype [45].…”
Background Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes. Methods This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes. Findings The analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1
“…One quarter of patients in this subtype had been diagnosed with a mental health condition in our health system, and this subtype had the highest proportion of patients leaving against medical advice. Buprenorphine initiation during the hospital encounter has been shown to reduce hospitalizations in a similar cohort of patients with heroin use [44]. The high rates of uninsured status, low SES metrics from the census-tract variables (% in poverty, unemployed, education, and median household earnings), and high rates of mental health conditions imply behavioral and social determinants of health are important considerations in this subtype [45].…”
Background Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes. Methods This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes. Findings The analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1
“…3,5 Opioid agonist therapy (OAT)-buprenorphine or methadone 6 -are provided infrequently and variably during hospitalization 7 or upon discharge. 8 Underutilization occurs although OAT delivery during hospitalization is feasible 9,10 and OAT receipt is associated with decreased illicit opioid use upon discharge, 9 reduced 30 and 90-day readmissions, 11 and increased post-hospital substance use disorder (SUD) treatment engagement. 11,12 Veterans are particularly vulnerable as they are twice as likely to die from accidental opioid overdose than non-veterans.…”
Section: Introductionmentioning
confidence: 99%
“…8 Underutilization occurs although OAT delivery during hospitalization is feasible 9,10 and OAT receipt is associated with decreased illicit opioid use upon discharge, 9 reduced 30 and 90-day readmissions, 11 and increased post-hospital substance use disorder (SUD) treatment engagement. 11,12 Veterans are particularly vulnerable as they are twice as likely to die from accidental opioid overdose than non-veterans. 13 OUD diagnoses within the Veterans Health Administration (VHA) have increased by nearly two-fold between 2004 (n = 30,093) 14 and 2017 (n = 54,078).…”
Background: Hospitalization of patients with opioid use disorder (OUD) is increasing, yet little is known about opioid agonist therapy (OAT: methadone and buprenorphine) administration during admission. Objective: Describe and examine patient-and hospital-level characteristics associated with OAT receipt during hospitalization in the Veterans Health Administration (VHA). Participants: 12,407 unique patients, ≥ 18 years old, with an OUD-related ICD-10 diagnosis within 12 months prior to or during index hospitalization in fiscal year 2017 from 109 VHA hospitals in the continental United States. Main Measure: OAT received during hospitalization. Key Results: Few admissions received OAT (n = 1,914; 15%) and when provided it was most often for withdrawal management (n = 834; 7%). Among patients not on OAT prior to admission who survived hospitalization (n = 10,969), 2.0% (n = 203) were newly initiated on OAT with linkage to care after hospital discharge. Hospitals varied in the frequency of OAT delivery (range 0% to 43% of qualified admissions). Patients with pre-admission OAT (Adjusted Odds Ratio [AOR] = 15.30; 95% CI [13.2, 17.7]), acute OUD diagnosis (AOR = 2.3; 95% CI [1.99, 2.66]), and male gender (AOR 1.52; 95% CI [1.16, 2.01]) had increased odds of OAT receipt. Patients who received non-OAT opioids (AOR 0.53; 95 CI [0.46, 0.61]) or surgical procedures (AOR 0.75; 95 CI [0.57, 0.99]) had decreased odds of OAT receipt. Large (AOR = 2.0; 95% CI [1.39, 3.00]) and medium-sized (AOR = 1.9; 95% CI [1.33, 2.70]) hospitals were more likely to provide OAT. Conclusions: In a sample of VHA inpatient medical admissions, OAT delivery was infrequent, varied across the health system, and was associated with specific patient and hospital characteristics. Policy and educational interventions should promote hospital-based OAT delivery.
“…We undertook an IRB-approved, retrospective, secondary analysis of a cohort of patients admitted for OUD [11]. We used the Partners Research Patient Data Registry (RPDR) to electronically identify consecutive adults admitted to one academic medical center in Boston, MA between October 1, 2011 and September 30, 2016 with either an ICD-9-CM or ICD-10 diagnostic code or problem list item suggestive of OUD (e.g.…”
Hospitals often perform urine drug screens (UDS) upon inpatient admission to confirm selfreported psychoactive substance use for patients with opioid use disorder (OUD). We sought to evaluate the agreement between UDS and patient self-report for psychoactive substances detected with UDS for adults with OUD admitted to hospital. For 11 substance categories, we evaluated agreement between the UDS and the documented history over a 5-year period for consecutive adults admitted to one academic center with a history of OUD. Among the 153 patients, overall agreement across the 1683 different history/UDS pairs (i.e. either historyþ/ UDS þ or history-/UDS-) was high (81.3%) but varied (from lowest to highest) by substance [opiates (56.9%), benzodiazepines (66.0%), 6-acetylmorphine (67.3%), cocaine (81.0%), cannabinoids (81.0%), methadone (83.7%), buprenorphine (85.0%), amphetamine (94.8%), barbiturates (95.4%), and phencyclidine (98.7%)]. Historyþ/UDS-pair mismatches were most frequent for 6-acetylmorphine (32.7%), methadone (14.3%) and oxycodone (12.4%); history-/UDS þ pair mismatches were most frequent for opiates (43.1%), benzodiazepines (24.8%) and cannabinoids (18.3%). The change in agreement over time of self-reported heroin use may reflect an increasing number of patients unknowingly using illicit fentanyl products. Among hospitalized patients with OUD, agreement between reported psychoactive substance use history and UDS results is strong with the exception of opiates, heroin, and benzodiazepines.
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