Opioid and benzodiazepine prescribing after COVID-19 hospitalization

Declining Postoperative 90-Day Opioid Prescriptions From 2010 to 2021 Following Hip Arthroscopy for Femoroacetabular Impingement Syndrome

Purpose

To analyze postoperative opioid prescriptions after hip arthroscopy for femoroacetabular impingement syndrome (FAIS) in a large, opioid-naive population and to evaluate factors associated with receiving more opioids.

Methods

Opioid-naive adult patients who underwent hip arthroscopy for FAIS were queried in the 2010 to 2022 PearlDiver Mariner 161 national administrative database. Exclusion criteria included patients with a history of chronic pain and patients who received opioid prescriptions more than 30 days before surgery. Patient variables were extracted: age, sex, and Elixhauser Comorbidity Index. Ninety-day postoperative opioid prescriptions (by total morphine milligram equivalents [MMEs]) were assessed with multivariate linear regression. Ninety-day postoperative opioid prescriptions from 2011 to 2021 were assessed.

Results

Of 27,079 patients with postoperative opioid prescriptions identified, a mean ± standard deviation of 347.6 ± 729.2 MMEs (40 tablets of 5 mg oxycodone) were prescribed per patient, with a mean of 1.6 prescriptions filled per patient within 90 days following surgery. Seventy-five percent of patients filled fewer than 600 MMEs, but a small subset filled more than 2,000 MMEs. Multivariate analysis revealed that, compared to patients in the age 30- to 39-year group, those aged 20 to 29 years received fewer MMEs (Δ = -72.5, P < .017). Compared to those with an Elixhauser Comorbidity Index of 2 or under, those >2 were prescribed more MMEs (Δ = 52.5, P < .017). Sex did not correlate with the postoperative MMEs prescribed. From 2011 to 2021, a 58.2% decrease in the 90-day mean MMEs prescribed was noted per patient (P < .017).

Conclusions

Fewer postoperative MMEs were filled following FAIS hip arthroscopy for patients in their 20s relative to those in their 30s, as well as for those with lower comorbidity burden. Patient sex was not associated with differences in postoperative MMEs prescribed. The amount of mean MMEs prescribed per patient decreased from 2011 to 2021.

Clinical Relevance

This study provides information about the typical amount of narcotics required after surgery. This is increasingly useful information, as surgeons/clinicians continue to try to minimize the role of narcotics in postoperative recovery.

New initiation of opioids, benzodiazepines and antipsychotics following hospitalization for COVID-19

BACKGROUND

Patients newly initiated on opioids (OP), benzodiazepines (BZD), and antipsychotics (AP) during hospitalization are often prescribed these on discharge. Implications of this practice on outcomes remains unexplored.

OBJECTIVE

To explore the prevalence and risk factors of new initiation of select OP, BZD and AP among patients requiring in-patient stays. Test the hypothesis that new prescriptions are associated with higher odds of readmission or death within 28 days of discharge.

DESIGN

Single center retrospective cohort study.

SETTING AND PARTICIPANTS

Patients admitted to a tertiary-level medical center with either a primary diagnosis of RT-PCR positive for COVID-19 or high index of clinical suspicion thereof.

INTERVENTION

None.

MAIN OUTCOME AND MEASURES

Exposure was the new initiation of select common OP, BZD, and AP which were continued on hospital discharge. Outcome was a composite of 28-day readmission or death following index admission. Multivariable logistic regression was used to assess patient mortality or readmission within 28 days of discharge associated with new prescriptions at discharge.

RESULTS

1319 patients were included in the analysis. 11.3% (149/1319) were discharged with a new prescription of select OP, BZD, or AP either alone or in combination. OP (110/149) were most prescribed followed by BZD (41/149) and AP (22/149). After adjusting for unbalanced confounders, new prescriptions (adjusted odds ratio: 2.44, 95% confidence interval: 1.42-4.12; p = .001) were associated with readmission or death within 28 days of discharge. One in nine patients admitted with a diagnosis of COVID-19 or high clinical suspicion thereof were discharged with a new prescription of either OP, BZD or AP. New prescriptions were associated with higher odds of 28-day readmission or death. Strengthening medication reconciliation processes focused on these classes may reduce avoidable harm.

Perioperative Opioid-Related Harms: Opportunities to Minimize Risk

Although substantial attention has been given to opioid prescribing in the United States, opioid-related mortality continues to climb due to the rising incidence and prevalence of opioid use disorder. Perioperative care has an important role in the consideration of opioid prescribing and the care of individuals at risk for poor postoperative pain- and opioid-related outcomes. Opioids are effective for acute pain management and commonly prescribed for postoperative pain. However, failure to align prescribing with patient need can result in overprescribing and exacerbate the flow of unused opioids into communities. Conversely, underprescribing can result in the undertreatment of pain, complicating recovery and impairing well-being after surgery. Optimizing pain management can be particularly challenging for individuals who are previously exposed to opioids or have critical risk factors, including opioid use disorder. In this review, we will explore the role of perioperative care in the broader context of the opioid epidemic in the United States, and provide considerations for a multidisciplinary, comprehensive approach to perioperative pain management and optimal opioid stewardship.

Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases

Background

Previous studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.

Objective

This study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.

Methods

Data were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.

Results

The study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.

Conclusion

This study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Defining opioid naïve and implications for monitoring opioid use: A population-based study in Alberta, Canada

PURPOSE

Reducing initial exposure of "opioid naïve" patients to opioids is a public health priority. Identifying opioid naïve patients is difficult, as numerous definitions are used. The objective is to summarize current definitions and evaluate their impact on opioid naïve measures in Alberta.

METHODS

An exploratory data analysis of the literature was conducted over the last 10 years to identify definitions commonly used in the literature to define opioid naïve. Then, using these definitions as a guide, we descriptively report the proportion of patients in Alberta between 2017 and 2021 who would be considered as opioid naïve using these definitions and all opioid dispensing data.

RESULTS

Three categories of definitions were broadly identified: (1) no opioid use within the previous 30 days/6 months/1 year, based on dispensation date; (2) no opioid use based on dispensation date plus days of supply; and, (3) exclusion of codeine from Definitions 1 and 2. Applying these definitions to the Alberta population showed a very wide range in the proportion who would be considered as opioid naïve. Overall, 36.4% of Albertans (n = 1 551 075) had an opioid dispensation in 2017-2021. The average age was 46.6 ± 18.8 and 52.8% were female. The proportion of opioid naïve were most affected by the "opioid free" period, with 97.4%, 83.2%, and 65.6% being classified as opioid naïve using time windows from Definition 1 (30 days, 6 months, 1 year of no prior opioid use). Definitions 2 and 3 did not materially change the results. Further extending the "opioid free" period to 2 years showed only 35% were opioid naïve.

CONCLUSIONS

The most convenient definition for "opioid naïve" was the use of an "opioid free" period. The choice of window would depend on how the information may be used to assistant in clinical decisions with longer windows more likely to reflect true opioid naïve patients. Irrespective of definition used, a large proportion of opioid users would be considered opioid naïve in Alberta.

Acute Pain and Development of Opioid Use Disorder: Patient Risk Factors

PURPOSE OF REVIEW

Pharmacological therapy for acute pain carries the risk of opioid misuse, with opioid use disorder (OUD) reaching epidemic proportions worldwide in recent years. This narrative review covers the latest research on patient risk factors for opioid misuse in the treatment of acute pain. In particular, we emphasize newer findings and evidence-based strategies to reduce the prevalence of OUD.

RECENT FINDINGS

This narrative review captures a subset of recent advances in the field targeting the literature on patients' risk factors for OUD in the treatment for acute pain. Besides well-recognized risk factors such as younger age, male sex, lower socioeconomic status, White race, psychiatric comorbidities, and prior substance use, additional challenges such as COVID-19 further aggravated the opioid crisis due to associated stress, unemployment, loneliness, or depression. To reduce OUD, providers should evaluate both the individual patient's risk factors and preferences for adequate timing and dosing of opioid prescriptions. Short-term prescription should be considered and patients at-risk closely monitored. The integration of non-opioid analgesics and regional anesthesia to create multimodal, personalized analgesic plans is important. In the management of acute pain, routine prescription of long-acting opioids should be avoided, with implementation of a close monitoring and cessation plan.