Cross-validation of short forms of the Screener and Opioid Assessment for Patients with Pain-Revised (SOAPP-R)

Centers for Disease Control and Prevention Clinical Practice Guideline for Prescribing Opioids: United States, 2022: What dentists need to know

BACKGROUND

The Centers for Disease Control and Prevention (CDC) published the Clinical Practice Guideline for Prescribing Opioids for Pain-United States, 2022 (CDCCPG) to replace the 2016 guideline. This guideline was designed to serve as a clinical tool to improve communication between clinicians and patients and empower them to make informed, person-centered decisions regarding pain management and the prescribing of opioids. It is intended for primary care and other clinicians, including dentists, who provide pain management for adults with acute, subacute, and chronic pain.

TYPES OF STUDIES REVIEWED

This article summarizes the CDCCPG, with an emphasis on information of relevance to dentistry.

RESULTS

For dentists, the most important recommendations for pain management are that nonsteroidal anti-inflammatory medications are first-line medications for acute dental pain, interdisciplinary care for chronic orofacial pain is indicated, and opioids should only be prescribed for acute dental pain for a maximum of 3 days after risk assessment.

PRACTICAL IMPLICATIONS

The CDCCPG contains a great deal of relevant information that can help dentists and dental specialists make safe, effective, and evidence-based decisions in providing pain control for their patients.

A deep learning method to detect opioid prescription and opioid use disorder from electronic health records

OBJECTIVE

As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD.

MATERIALS AND METHODS

We developed an informatics algorithm that trains two deep learning models over patient Electronic Health Records (EHRs) using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both challenging outcomes.

RESULTS

Our deep learning models incorporate elements from EHRs to predict opioid prescription with an F1-score of 0.88 ± 0.003 and an AUC-ROC of 0.93 ± 0.002. We also constructed a model to predict OUD diagnosis achieving an F1-score of 0.82 ± 0.05 and AUC-ROC of 0.94 ± 0.008.

DISCUSSION

Our model for OUD prediction outperformed prior algorithms for specificity, F1 score and AUC-ROC while achieving equivalent sensitivity. This demonstrates the importance of a) deep learning approaches in predicting OUD and b) incorporating both structured and unstructured data for this prediction task. No prediction models for opioid prescription as an outcome were found in the literature and therefore our model is the first to predict opioid prescribing behavior.

CONCLUSION

Algorithms such as those described in this paper will become increasingly important to understand the drivers underlying this national epidemic.

Application of risk assessment tools to predict opioid usage after shoulder surgery

Background

Currently 128 people die daily from opioid-related overdoses in the United States. This burden has instigated a search for viable means to guide postoperative prescription decision-making. The Opioid Risk Tool (ORT) and the Screener and Opioid Assessment for Patient with Pain (SOAPP) are validated risk assessment tools to predict opioid usage in high-risk populations. The purpose of this study was to evaluate the accuracy of these opioid risk assessments and pain intensity scores, including the Patient-Reported Outcomes Measurement Information System (PROMIS), to predict postoperative opioid use and dependence in shoulder surgery.

Methods

A retrospective review of 81 patients who underwent shoulder surgery and completed 3 preoperative risk and pain assessments within a single hospital system from 2018 to 2020 was performed. Demographic variables and ORT-O, SOAPP-R (the revised version of the SOAPP assessment), and PROMIS 3a scores were recorded from preoperative assessments. Opioid prescriptions were recorded from Electronic-Florida Online Reporting of Controlled Substances Evaluation. Dependence was defined as opioid prescriptions at or greater than 3 months after surgery. Risk assessment scores were compared and tested against postoperative opioid prescriptions using statistical analyses and logistic regression modeling.

Results

In the cohort, there were 36 female and 45 male patients with an average age of 64.5 years and body mass index of 28.0. Preoperatively, the average pain score was 6.2, and 7.8% of patients reported prolonged preoperative narcotics use. The average ORT-O score was 3.0, with 35.8% of patients defined as either medium or high risk, and the average PROMIS pain intensity preoperatively was 10.8. Neither the ORT-O nor the PROMIS pain score were good predictors of postoperative opioid dependence (area under curve = 0.39 and 0.43, respectively). The SOAPP-R performed slightly better (area under curve = 0.70) and was the only assessment with significantly different mean scores between patients with postoperative opioid dependence and those without (33.4 and 24.5, respectively, P = .049) and a moderate correlation to postoperative total morphine equivalents (R = 0.46, P = .007).

Conclusion

With recent focus on preoperative risk assessments to predict postoperative opioid use and dependence, it is important to understand how well these tools work when applied to orthopedic patients. While the ORT may be helpful in other fields, it does not seem to be a strong predictor of postoperative opioid use or dependence in patients undergoing various types of shoulder surgery. Future studies are needed to explore the utility of the SOAPP-R in a larger sample and identify tools applicable to the orthopedic population to assist surgeons in screening at-risk patients.

Management of Physical Symptoms in Patients with Advanced Cancer During the Last Weeks and Days of Life

Patients with advanced cancer are faced with many devastating symptoms in the last weeks and days of life, such as pain, delirium, dyspnea, bronchial hypersecretions (death rattle), and intractable seizures. Symptom management in the last weeks of life can be particularly challenging because of the high prevalence of delirium complicating symptom assessment, high symptom expression secondary to psychosocial and spiritual factors, limited life-expectancy requiring special considerations for prognosis-based decision-making, and distressed caregivers. There is a paucity of research involving patients in the last weeks of life, contributing to substantial variations in clinical practice. In this narrative review, we shall review the existing literature and provide a practical approach to in-patient management of several of the most distressing physical symptoms in the last weeks to days of life.

Validation of the Opioid Overdose Risk Behavior Scale, version 2 (ORBS-2)

OBJECTIVE

To examine the factor structure of a revised and expanded opioid overdose risk behavior scale and assess its associations with known overdose indicators and other clinical constructs.

BACKGROUND

Opioid-related overdose remains high in the U.S. We lack strong instrumentation for assessing behavioral risk factors. We revised and expanded the opioid overdose risk behavior scale (ORBS-1) for use among a broader range of people who use opioids.

SETTING & SAMPLING FRAME

Using respondent-driven sampling we recruited adults (18+) reporting current unprescribed opioid use and New York City residence.

METHOD

Participants (N = 575) completed the ORBS-1, ORBS-2, and a variety of clinical measures and then completed the ORBS-2 and overdose risk outcomes across monthly follow-up assessments over a 13-month period.

RESULTS

Principal components analysis was used to identify six ORBS-2 subscales, Prescription Opioid Misuse, Risky Non-Injection Use, Injection Drug Use, Concurrent Opioid and Benzodiazepine Use, Concurrent Opioid and Alcohol Use, and Multiple-Drug Polysubstance Use. All subscales showed moderate non-parametric correlations with the ORBS-1 and with corresponding clinical constructs. Five of the subscales were significantly (p < .01) positively associated with self-reported non-fatal overdose. Of note, the Risky Non-Injection Use subscale was the most strongly associated with past-month overdose indicators.

CONCLUSIONS

Psychometrics for the opioid overdose risk behavior subscales identified suggest the ongoing utility of risk behavioral instrumentation for epidemiological research and clinical practice focused on risk communication and minimization. Use of the entire ORBS-2 measure can provide insight into the proximal/behavioral factors of greatest concern to reduce overdose mortality.

Mortality Secondary to Unintentional Poisoning after Inpatient Rehabilitation among Individuals with Moderate to Severe Traumatic Brain Injury

Studies have shown reduced life expectancy following moderate-severe traumatic brain injury (TBI) with death from unintentional poisoning (UP) being 11 times higher following TBI than in the general population. The characteristics of those who die of unintentional poisoning are compared with the characteristics of those who die of other causes (OC) in a retrospective cohort who received inpatient rehabilitation following TBI and enrolled in the TBI Model Systems National Database between 1989 and 2017 (n = 15,835 cases with 2,238 deaths recorded). Seventy-eight cases (3.5%) of deaths were the result of UP, 76% were the result of OC, and 20.5% were from an unknown cause. Among the UP deaths, 90% involved drugs (of these, 67% involved narcotic drugs and 14% involved psychostimulants), and 8% involved alcohol. Age-adjusted risk for UP death was associated with: white/non-Hispanic race/ethnicity, living alone, non-institutionalization, pre- and post-injury illicit drug use and alcohol/drug problem use, any alcohol use at last follow-up, better Functional Independence Measure^TM (FIM) scores, history of arrest, moderate disability (vs. severe disability or good recovery), less supervision needed, and greater anxiety. Adults who receive inpatient rehabilitation for TBI who die from UP are distinguishable from those who die of OC. Factors such as pre-injury substance use in the context of functional independence may be regarded as targets for prevention and/or intervention to reduce substance use and substance-related mortality among survivors of moderate-severe TBI. The current findings may have implications for medical care, surveillance, prevention, and health promotion.

A Comparison of Short Forms of the Screener and Opioid Assessment for Patients With Pain – Revised (SOAPP-R)

Abstract. The Screener and Opioid Assessment for Patients with Pain – Revised (SOAPP-R) is a 24-item self-report questionnaire that assesses risk of aberrant medication-related behavior among chronic pain patients. Recently, an 8-item version of the SOAPP-R that weights items differentially was proposed. However, no previous study had compared the 8-item form with other short versions of the SOAPP-R, including a static 12-item short form and computer-based versions customizing the test length to the individual respondent. Moreover, no prior research had investigated combining the 8-item short form with customized computer-based stopping rules to further enhance efficiency. The objectives of this study were to compare the 8-item version with previously recommended short forms of the SOAPP-R, and to develop and evaluate a new version of the SOAPP-R combining the 8-item version with computer-based stopping rules. Versions were compared via sensitivity, specificity, and mean test length using real-data simulation of three datasets. Although results varied across datasets, the 8-item SOAPP-R compared favorably to previously recommended forms. Combining the 8-item form with computer-based stopping rules reduced the mean test length without affecting sensitivity or specificity; thus, the combined approach is recommended. The methodology used to shorten questionnaires via computer-based testing can also be applied to other instruments.

The Association Between Cannabis Use and Aberrant Behaviors During Chronic Opioid Therapy for Chronic Pain

Objective

Health care providers are likely to see an increase in the concomitant use of cannabis and opioids, particularly with the increased liberalization and ongoing research into the possible role of medical marijuana for chronic pain. Recent literature reports a prevalence of concurrent use ranging from 8.9% to 31.8%. The primary aim of this study was to determine the relationship between cannabis use and aberrant drug behaviors in noncancer pain patients receiving chronic opioid therapy.

Design

Retrospective chart review.

Setting

Community-based, interdisciplinary pain management center.

Subjects

Data from 209 patients who were evaluated for a medication management program between October 1, 2011, and January 1, 2014, and met inclusion criteria. Forty-four were positive for cannabis in their initial random urine drug toxicology.

Methods

Data from electronic health records, including demographics, urine drug toxicology, disability, opioid dose, opioid risk assessment data, and pain severity were analyzed to examine differences among cannabis users and noncannabis users.

Results

Subjects with cannabis in their initial urine drug toxicology were more likely to have a future occurrence of an opioid-related aberrancy (P < 0.001), be male (P = 0.047), have a history of substance abuse (P = 0.013), and be enrolled into a higher level of clinical monitoring of opioid medication use (P = 0.008). No other associations with demographic and clinical variables reached statistical significance.

Conclusions

Concurrent use of cannabis and opioids by patients with chronic pain appears to indicate higher risk for opioid misuse. Closer monitoring for opioid-related aberrancy is indicated for this group of patients.

Techniques to Shorten a Screening Tool for Emergency Department Patients

Introduction

Screening of patients for opioid risk has been recommended prior to opioid prescribing. Opioids are prescribed frequently in the emergency department (ED) setting, but screening tools are often of significant length and therefore limited in their utility. We describe and evaluate three approaches to shortening a screening tool: creation of a short form; curtailment; and stochastic curtailment.

Methods

To demonstrate the various shortening techniques, this retrospective study used data from two studies of ED patients for whom the provider was considering providing an opioid prescription and who completed the Screener and Opioid Assessment for Patients with Pain-Revised, a 24-item assessment. High-risk criteria from patients’ prescription drug monitoring program data were used as an endpoint. Using real-data simulation, we determined the sensitivity, specificity, and test length of each shortening technique.

Results

We included data from 188 ED patients. The original screener had a test length of 24 questions, a sensitivity of 44% and a specificity of 76%. The 12-question short form had a sensitivity of 41% and specificity of 75%. Curtailment and stochastic curtailment reduced the question length (mean test length ranging from 8.1–19.7 questions) with no reduction in sensitivity or specificity.

Conclusion

In an ED population completing computer-based screening, the techniques of curtailment and stochastic curtailment markedly reduced the screening tool’s length but had no effect on test characteristics. These techniques can be applied to improve efficiency of screening patients in the busy ED environment without sacrificing sensitivity or specificity.

Dying on Hospice in the Midst of an Opioid Crisis: What Should We Do Now?

The current opioid crisis in the United States is a major problem facing health-care providers, even at the end of life. Opioids continue to be the mainstay treatment for pain at the end of life, with the prevalence of pain reported in up to 80% of patients and tends to increase as one gets closer toward the end of life. In the past year, 20.2 million Americans had a substance use disorder (SUD) and SUDs are disabling disorders that largely go untreated. In addition, the coexistence of both a mental health and SUD is very common with the use of opioids often as a means of chemical coping. Most hospice programs do not have standardized SUD policies/guidelines in place despite the increasing concerns about substance abuse within the United States. The goal of this article is to review the literature on this topic and offer strategies on how to manage pain in patients who have active SUD or who are at risk for developing SUD in those dying on hospice.