Dataset Information

Opioid Use and Storage Patterns by Patients after Hospital Discharge following Surgery.

ABSTRACT:

Introduction

Opioid-based analgesic therapy represents a cornerstone of pain management after surgery. The recent rise in opioid sales and opioid overdoses suggests it is important to maximize the safety of opioid prescribing after surgery. Given that patients may live with other family members in the home, safe storage and appropriate disposal of excess opioids after hospital discharge are necessary to prevent unintended secondary exposures. Identifying characteristics of patients who are likely to be prescribed excess opioids after surgery may enable more targeted prescription practices and safety interventions. Our study aimed to elucidate patient-reported opioid use patterns and modes of home storage of opioids among patients discharged home after Cesarean section (C-section) and thoracic surgery. Specifically, we sought to identify characteristics of patients who reported using about half or more versus less of the opioids prescribed to them for use after hospital discharge.

Methods

For this cohort study, we developed a survey on quality of analgesia following hospital discharge, amounts of opioids taken relative to the amount prescribed, reasons for not taking all prescribed medications, and storage and disposal methods for leftover opioids. Adult patients, who had C-section or thoracic surgery at a tertiary academic medical center, were given a web-based self-administered survey after discharge. Descriptive statistics (means and standard deviations, proportions) were used to describe the study sample and survey results. Comparisons between patients who reported taking about half or more versus less of the opioids prescribed to them for use after hospital discharge were made using unpaired t-tests, Mann-Whitney tests, and Chi-square tests as appropriate.

Results

The majority (53%) of respondents after C-section (N = 30) reported taking either no or very few (less than 5) prescribed opioid pills; 83% reported taking half or less; and 17% of women, reported taking all or nearly all (5 or fewer pills left over) of their opioid prescription. In a cohort of patients after thoracic surgery (n = 31) 45% reported taking either no or very few (5 or less) prescribed opioid pills; 71% reported taking half or less; and 29% of patients reported taking all or nearly all (5 or fewer pills left over) of their opioid prescription. In both cohorts, use of opioids while hospitalized was higher in the group reporting using about half or more of prescribed opioids after discharge. Leftover opioids were stored in an unlocked location in 77% and 73% of cases following C-section and thoracic surgery, respectively.

Conclusion

Our findings from surveys in two distinct patient populations at a single academic medical center suggest that current opioid prescribing practices for pain management at hospital discharge following Cesarean section and thoracic surgery may not account for individual patients' analgesic requirements. Excess opioid pills are commonly stored in unsecured locations and represent a potential source for non-medical opioid use and associated morbidity and mortality in patients and their families. Research to develop goal-directed and patient-centered post-discharge opioid prescription practices and encourage opioid safety practices after surgery is needed.

SUBMITTER: Bartels K

PROVIDER: S-EPMC4732746 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Opioid Use and Storage Patterns by Patients after Hospital Discharge following Surgery.

Bartels Karsten K Mayes Lena M LM Dingmann Colleen C Bullard Kenneth J KJ Hopfer Christian J CJ Binswanger Ingrid A IA

PloS one 20160129 1

<h4>Introduction</h4>Opioid-based analgesic therapy represents a cornerstone of pain management after surgery. The recent rise in opioid sales and opioid overdoses suggests it is important to maximize the safety of opioid prescribing after surgery. Given that patients may live with other family members in the home, safe storage and appropriate disposal of excess opioids after hospital discharge are necessary to prevent unintended secondary exposures. Identifying characteristics of patients who a ...[more]

PMID: 26824844

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Project description:BackgroundChronic opioid therapy for chronic pain treatment has increased. Hospital physicians, including hospitalists and medical/surgical resident physicians, care for many hospitalized patients, yet little is known about opioid prescribing at hospital discharge and future chronic opioid use.ObjectiveWe aimed to characterize opioid prescribing at hospital discharge among 'opioid naïve' patients. Opioid naïve patients had not filled an opioid prescription at an affiliated pharmacy 1 year preceding their hospital discharge. We also set out to quantify the risk of chronic opioid use and opioid refills 1 year post discharge among opioid naïve patients with and without opioid receipt at discharge.DesignThis was a retrospective cohort study.ParticipantsFrom 1 January 2011 to 31 December 2011, 6,689 opioid naïve patients were discharged from a safety-net hospital.Main measureChronic opioid use 1 year post discharge.Key resultsTwenty-five percent of opioid naïve patients (n?=?1,688) had opioid receipt within 72 hours of discharge. Patients with opioid receipt were more likely to have diagnoses including neoplasm (6.3% versus 3.5%, p?<?0.001), acute pain (2.7% versus 1.0 %, p?<?0.001), chronic pain at admission (12.1% versus 3.3%, p?<?0.001) or surgery during their hospitalization (65.1% versus 18.4%, p?<?0.001) compared to patients without opioid receipt. Patients with opioid receipt were less likely to have alcohol use disorders (15.7% versus 20.7%, p?<?0.001) and mental health disorders (23.9% versus 31.4%, p?<?0.001) compared to patients without opioid receipt. Chronic opioid use 1 year post discharge was more common among patients with opioid receipt (4.1% versus 1.3%, p?<?0.0001) compared to patients without opioid receipt. Opioid receipt was associated with increased odds of chronic opioid use (AOR?=?4.90, 95% CI 3.22-7.45) and greater subsequent opioid refills (AOR?=?2.67, 95% CI 2.29-3.13) 1 year post discharge compared to no opioid receipt.ConclusionOpioid receipt at hospital discharge among opioid naïve patients increased future chronic opioid use. Physicians should inform patients of this risk prior to prescribing opioids at discharge.

Project description:BackgroundWhile opioid analgesics are often necessary for the management of acute postoperative pain, appropriate prescribing practices are crucial to avoid harm. The aim was to investigate the changes in the proportion of people receiving initial opioid prescriptions after hospital discharge following colectomy, and describe trends and patterns in prescription characteristics.MethodsThis was a retrospective cohort study. Patients undergoing colectomy in England between 2010 and 2019 were included using electronic health record data from linked primary (Clinical Practice Research Datalink Aurum) and secondary (Hospital Episode Statistics) care. The proportion of patients having an initial opioid prescription issued in primary care within 90 days of hospital discharge was calculated. Prescription characteristics of opioid type and formulation were described.ResultsOf 95 155 individuals undergoing colectomy, 15 503 (16.3%) received opioid prescriptions. There was a downward trend in the proportion of patients with no prior opioid exposure (opioid naive) who had a postdischarge opioid prescription (P <0.001), from 11.4% in 2010 to 6.7% in 2019 (-41.3%, P <0.001), whereas the proportions remained stable for those prescribed opioids prior to surgery, from 57.5% in 2010 to 58.3% in 2019 (P = 0.637). Codeine represented 44.5% of all prescriptions and prescribing increased by 14.5% between 2010 and 2019. Prescriptions for morphine and oxycodone rose significantly by 76.6% and 31.0% respectively, while tramadol prescribing dropped by 48.0%. The most commonly prescribed opioid formulations were immediate release (83.9%), followed by modified release (5.8%) and transdermal (3.2%). There was a modest decrease in the prescribing of immediate-release formulations from 86.0% in 2010 to 82.0% in 2019 (P <0.001).ConclusionOver the 10 years studied, there was a changing pattern of opioid prescribing following colectomy, with a decrease in the proportion of opioid-naive patients prescribed postdischarge opioids.

Project description:BackgroundThe incidence of opioid-related death in women has increased 5-fold over the past decade. For many women, their initial opioid exposure will occur in the setting of routine medical care. Approximately 1 in 3 deliveries in the United States is by cesarean, and opioids are commonly prescribed for postsurgical pain management.ObjectiveThe objective of this study was to determine the risk that opioid-naïve women prescribed opioids after cesarean delivery will subsequently become consistent prescription opioid users in the year following delivery and to identify predictors for this behavior.Study designWe identified women in a database of commercial insurance beneficiaries who underwent cesarean delivery and who were opioid naïve in the year prior to delivery. To identify persistent users of opioids, we used trajectory models, which group together patients with similar patterns of medication filling during follow-up, based on patterns of opioid dispensing in the year following cesarean delivery. We then constructed a multivariable logistic regression model to identify independent risk factors for membership in the persistent user group.ResultsA total of 285 of 80,127 (0.36%, 95% confidence interval, 0.32-0.40), opioid-naïve women became persistent opioid users (identified using trajectory models based on monthly patterns of opioid dispensing) following cesarean delivery. Demographics and baseline comorbidity predicted such use with moderate discrimination (c statistic = 0.73). Significant predictors included a history of cocaine abuse (risk, 7.41%; adjusted odds ratio, 6.11, 95% confidence interval, 1.03-36.31) and other illicit substance abuse (2.36%; adjusted odds ratio, 2.78, 95% confidence interval, 1.12-6.91), tobacco use (1.45%; adjusted odds ratio, 3.04, 95% confidence interval, 2.03-4.55), back pain (0.69%; adjusted odds ratio, 1.74, 95% confidence interval, 1.33-2.29), migraines (0.91%; adjusted odds ratio, 2.14, 95% confidence interval, 1.58-2.90), antidepressant use (1.34%; adjusted odds ratio, 3.19, 95% confidence interval, 2.41-4.23), and benzodiazepine use (1.99%; adjusted odds ratio, 3.72, 95% confidence interval, 2.64-5.26) in the year prior to the cesarean delivery.ConclusionA very small proportion of opioid-naïve women (approximately 1 in 300) become persistent prescription opioid users following cesarean delivery. Preexisting psychiatric comorbidity, certain pain conditions, and substance use/abuse conditions identifiable at the time of initial opioid prescribing were predictors of persistent use.

Project description:BackgroundOpioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and social risks and behaviors. To capture this information at scale, natural language processing (NLP) tools must be developed and evaluated. We developed and applied an annotation schema to deeply characterize OUD and related clinical, behavioral, and environmental factors, and automated the annotation schema using machine learning and deep learning-based approaches.MethodsUsing the MIMIC-III Critical Care Database, we queried hospital discharge summaries of patients with International Classification of Diseases (ICD-9) OUD diagnostic codes. We developed an annotation schema to characterize problematic opioid use, identify individuals with potential OUD, and provide psychosocial context. Two annotators reviewed discharge summaries from 100 patients. We randomly sampled patients with their associated annotated sentences and divided them into training (66 patients; 2,127 annotated sentences) and testing (29 patients; 1,149 annotated sentences) sets. We used the training set to generate features, employing three NLP algorithms/knowledge sources. We trained and tested prediction models for classification with a traditional machine learner (logistic regression) and deep learning approach (Autogluon based on ELECTRA's replaced token detection model). We applied a five-fold cross-validation approach to reduce bias in performance estimates.ResultsThe resulting annotation schema contained 32 classes. We achieved moderate inter-annotator agreement, with F1-scores across all classes increasing from 48 to 66%. Five classes had a sufficient number of annotations for automation; of these, we observed consistently high performance (F1-scores) across training and testing sets for drug screening (training: 91-96; testing: 91-94) and opioid type (training: 86-96; testing: 86-99). Performance dropped from training and to testing sets for other drug use (training: 52-65; testing: 40-48), pain management (training: 72-78; testing: 61-78) and psychiatric (training: 73-80; testing: 72). Autogluon achieved the highest performance.ConclusionThis pilot study demonstrated that rich information regarding problematic opioid use can be manually identified by annotators. However, more training samples and features would improve our ability to reliably identify less common classes from clinical text, including text from outpatient settings.

Project description:Importance:The United States is experiencing an opioid abuse epidemic. Opioid overprescription by physicians may contribute to this epidemic. Objectives:To determine if there was a correlation between a postoperative patient's 24-hour predischarge opioid use and the amount of opioids prescribed at hospital discharge and to determine the number of patients who used no opioids prior to discharge but were still prescribed opioids after hospital discharge. Design, Setting, and Participants:This cross-sectional study performed a retrospective record review of 18 343 postoperative patients at Boston Medical Center and Lahey Hospital and Medical Center-Burlington Campus who were discharged home after a postoperative inpatient admission of at least 24 hours. Data collection spanned from May 22, 2014, to June 30, 2016, in the Boston Medical Center data set and from March 23, 2015, to September 7, 2016, in the Lahey Hospital and Medical Center-Burlington Campus data set. Exposures:Surgery requiring a postoperative inpatient hospital stay longer than 24 hours. Main Outcomes and Measures:The main outcome measures were the patient's 24-hour predischarge opioid use and the total quantity of opioids prescribed at hospital discharge. Potential overprescription was defined as the number of patients who used no opioids in the 24 hours prior to hospital discharge but were still prescribed opioids after hospital discharge. Results:Among the 18 343 patients (10 069 women and 8274 men; mean age, 52.2 years) who underwent 21 452 surgical procedures, there was wide variation in the amount of opioids prescribed at hospital discharge given a postoperative patient's 24-hour predischarge opioid use. A total of 6548 patients (35.7%) used no opioids in the 24 hours prior to hospital discharge; however, 2988 of these patients (45.6%) were prescribed opioids at hospital discharge, suggesting potential overprescription. Services that had the highest rates of potential overprescription (obstetrics [adjusted odds ratio (AOR), 3.146; 95% CI, 2.094-4.765] and gynecology [AOR, 2.355; 95% CI, 1.663-3.390], orthopedics [AOR, 0.943; 95% CI, 0.719-1.242], and plastic surgery [AOR, 0.733; 95% CI, 0.334-1.682]) generally had the highest rates of patients still using opioids at hospital discharge. Pediatric surgery was the only service that did not have any cases of potential overprescription (AOR, 2.09 × 10-7; 95% CI, 0.000-0.016). Conclusions and Relevance:Opioids are not regularly prescribed in a patient-specific manner to postoperative patients. Potential opioid overprescription occurs regularly after surgery among almost all surgical specialties.

Dataset Information

Opioid Use and Storage Patterns by Patients after Hospital Discharge following Surgery.

Introduction

Methods

Results

Conclusion

Publications

Opioid Use and Storage Patterns by Patients after Hospital Discharge following Surgery.

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