The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies

Establishing a research agenda for the study and assessment of opioid withdrawal

The opioid crisis is an international public health concern. Treatments for opioid use disorder centre largely on the management of opioid withdrawal, an aversive collection of signs and symptoms that contribute to opioid use disorder. Whereas in the past 50 years more than 90 medications have been developed for depression, only five medications have been developed for opioid use disorder during this period. We posit that underinvestment has occurred in part due to an underdeveloped understanding of opioid withdrawal syndrome. This Personal View summarises substantial gaps in our understanding of opioid withdrawal that are likely to continue to limit major advancements in its treatment. There is no firm consensus in the field as to how withdrawal should be precisely defined; 10-550 symptoms of withdrawal can be measured on 18 scales. The imprecise understanding of withdrawal is likely to result in overestimating or underestimating the severity of an individual's withdrawal syndrome or potential therapeutic effects of different candidate medications. The severity of the opioid crisis is not remitting, and an international research agenda for the study and assessment of opioid withdrawal is necessary to support transformational changes in withdrawal management and treatment of opioid use disorder. Nine actionable targets are delineated here: develop a consensus definition of opioid withdrawal; understand withdrawal symptomatology after exposure to different opioids (particularly fentanyl); understand precipitated opioid withdrawal; understand how co-exposure of other drugs (eg, xylazine and stimulants) influences withdrawal expression; examine individual variation in withdrawal phenotypes; precisely characterise the protracted withdrawal syndrome; identify biomarkers of opioid withdrawal severity; identify predictors of opioid withdrawal severity; and understand which symptoms are most closely associated with treatment attrition or relapse. The US Food and Drug Administration recently established a formal indication for opioid withdrawal that has invigorated interest in drug development for opioid withdrawal management. Action is now needed to support these interests and help industry identify new classes of medications so that real change can be achieved for people with opioid use disorder.

Clinically meaningful individual differences in opioid withdrawal expression

Opioid use disorder (OUD) is a significant public health concern. An individual with an OUD may experience withdrawal after stopping opioid use. There has been limited exploration of the individual differences in withdrawal expression. This study expands understanding of this issue by examining the presence and frequency at which persons who have ever had opioid withdrawal have experienced different opioid withdrawal symptoms. Using cross-sectional data captured online from Amazon Mechanical Turk, 124 adults with a lifetime experience of opioid withdrawal were included. Respondents were able to indicate ever experiencing 31 individual opioid withdrawal symptoms. If a symptom was ever experienced, respondents would indicate if it was common and whether it bothered them. A cluster analysis was used to explore variability between the withdrawal symptoms. The sample was primarily men (n = 76, 61.3%) with an average age of 34.7 (SD = 11.6). The typical withdrawal syndrome lasted 6.5 days (SD = 4.9) and was most severe at 5.7 (SD = 4.9) days. Lifetime endorsement of individual symptoms ranged from a high of 73.4% (anxious) to a low of 43.5% (nausea). The cluster analysis was significant, F(1, 122) = 215.6, p < .001, with good Bayesian information criteria (0.7). The two clusters are conceptualized here as HIGH (N = 73; 59%) and LOW (N = 51; 41%) endorsing, with a mean of 21.9 and 8.5 items endorsed. These data add to prior studies by suggesting high variability in the individual expression of opioid withdrawal symptoms. It may be time for the field to develop a consensus regarding opioid withdrawal symptom expression and measurement to enhance clinical care. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Decreasing Length of Stay in Opioid Withdrawal

INTRODUCTION

In 2017, more than 1,200 opioid-related deaths were reported in Virginia, with slightly fewer in 2018, at 1,193 deaths. The current opioid crisis has placed a strain on an already limited number of mental health (MH) inpatient beds. The industry standard for assessment and treatment of opioid withdrawal symptoms, in the inpatient setting, is the Clinical Opiate Withdrawal Scale (COWS), and yet some units continue to utilize the Clinical Institute Withdrawal Assessment for Alcohol-Revised (CIWA-Ar) for this population.

AIM

The purpose of this nurse-led performance improvement project was to implement COWS in the inpatient MH setting and improve length of stay (LOS) by 1 day.

METHOD

In 2018, in a large federal teaching facility in the mid-Atlantic region, the COWS was implemented to replace the CIWA-Ar for opioid withdrawal, with the focus on decreasing LOS. Prior to implementation of COWS, LOS on the inpatient MH unit for opioid withdrawal was 8.6 days, which was higher than the ~6- to 7-day LOS for surrounding private sector hospitals. Individual electronic medical records were reviewed for LOS and completion of COWS and pertinent details were discussed daily with nursing staff and monthly with the interdisciplinary team. Baseline data were collected from April 2017 to March 2018, with data collection continuing through the project implementation, April to September 2018.

RESULTS

Completion of COWS on 100% of patients admitted with opioid withdrawal and a decrease in LOS from 8.6 to 4.7 days was found, a 45% reduction.

CONCLUSION

The nurse-driven performance improvement project affected business acumen, through decreased LOS, as well as quality of care, through better symptom management.

Nucleus accumbens D1/D2 circuits control opioid withdrawal symptoms in mice

The nucleus accumbens (NAc) is the most promising target for drug use disorder treatment. Deep brain stimulation (DBS) of NAc is effective for drug use disorder treatment. However, the mechanisms by which DBS produces its therapeutic effects remain enigmatic. Here, we define a behavioral cutoff criterion to distinguish depressive-like behaviors and non-depressive-like behaviors in mice after morphine withdrawal. We identified a basolateral amygdala (BLA) to NAc D1 medium spiny neuron (MSN) pathway that controls depressive-like behaviors after morphine withdrawal. Furthermore, the paraventricular nucleus of thalamus (PVT) to NAc D2 MSN pathway controls naloxone-induced acute withdrawal symptoms. Optogenetically induced long-term potentiation with κ-opioid receptor (KOR) antagonism enhanced BLA to NAc D1 MSN signaling and also altered the excitation/inhibition balance of NAc D2 MSN signaling. We also verified that a new 50 Hz DBS protocol reversed morphine withdrawal-evoked abnormal plasticity in NAc. Importantly, this refined DBS treatment effectively alleviated naloxone-induced withdrawal symptoms and depressive-like behaviors and prevented stress-induced reinstatement. Taken together, the results demonstrated that input- and cell type-specific synaptic plasticity underlies morphine withdrawal, which may lead to novel targets for the treatment of opioid use disorder.

Non-Prescribed Substance Use during the First Month of Treatment by People Receiving Depot Buprenorphine for Opioid Use Disorder

Background: Non-prescribed substance use (NPSU) during the treatment of opioid use disorder (OUD) is a recognized phenomenon. The use of non-prescribed substances is associated with discontinuing treatment and drop-out can occur within the early weeks of treatment, before benefit from treatment occurs. Recent developments in treatment include long-acting, slow-release depot buprenorphine injections. This article focuses on NPSU during the first month of treatment with depot buprenorphine, addressing the frequency with which it occurs, the substances used, and reasons for use. Methods: 70 semi-structured interviews (held at three time-points) were conducted with 26 patients initiating depot buprenorphine as part of a longitudinal qualitative study. Analysis prioritized content and framework analyses. Findings: 17/26 participants self-reported NPSU at various times during the first month of treatment. NPSU typically involved heroin, crack-cocaine and some use of benzodiazepines and/or cannabis. Participants' reasons for heroin use were connected to their subjective accounts of opioid withdrawal symptoms, the management of pain, and experimentation (to test the blockade effect of buprenorphine). Frequency of heroin use was typically episodic rather than sustained. Participants associated crack-cocaine use with stimulant-craving and social connections, and considered their use of this substance to be difficult to manage. Conclusions: Patients' initial engagement with treatment for OUD is rarely examined in qualitative research. This study highlights how NPSU amongst patients receiving new forms of such treatment continues to be a challenge. As such, shared decision-making (between providers and patients) regarding treatment goals and NPSU should be central to the delivery of depot buprenorphine treatment programmes.

Leveraging Accelerometry as a Prognostic Indicator for Increase in Opioid Withdrawal Symptoms

Treating opioid use disorder (OUD) is a significant healthcare challenge in the United States. Remaining abstinent from opioids is challenging for individuals with OUD due to withdrawal symptoms that include restlessness. However, to our knowledge, studies of acute withdrawal have not quantified restlessness using involuntary movements. We hypothesized that wearable accelerometry placed mid-sternum could be used to detect withdrawal-related restlessness in patients with OUD. To study this, 23 patients with OUD undergoing active withdrawal participated in a protocol involving wearable accelerometry, opioid cues to elicit craving, and non-invasive Vagal Nerve Stimulation (nVNS) to dampen withdrawal symptoms. Using accelerometry signals, we analyzed how movements correlated with changes in acute withdrawal severity, measured by the Clinical Opioid Withdrawal Scale (COWS). Our results revealed that patients demonstrating sinusoidal-i.e., predominantly single-frequency oscillation patterns in their motion almost exclusively demonstrated an increase in the COWS, and a strong relationship between the maximum power spectral density and increased withdrawal over time, measured by the COWS (R = 0.92, p = 0.029). Accelerometry may be used in an ambulatory setting to indicate the increased intensity of a patient's withdrawal symptoms, providing an objective, readily-measurable marker that may be captured ubiquitously.

The Complexity of Educating Acute Care Nurses on Opioid Use Disorder: A Quality Improvement Project

BACKGROUND

Opioid use disorder (OUD) is a public health crisis, yet most acute care nurses are not educated to deliver evidence-based OUD care. Hospitalization provides a unique opportunity to initiate and coordinate OUD care in people presenting for other medical-surgical reasons. The aim of this quality improvement project was to determine the impact of an educational program on self-reported competencies of medical-surgical nurses caring for people with OUD at a large academic medical center in the Midwestern United States.

METHOD

Data were collected from two time points using a quality survey examining self-reported nurse competencies related to (a) assessment, (b) intervention, (c) treatment recommendation, (d) resource use, (e) beliefs, and (f) attitudes toward caring for people with OUD.

RESULTS

Nurses surveyed before education (T1G1, N = 123) and, after education, those who received the intervention (T2G2, N = 17) and those who did not (T2G3, N = 65) were included. Resource use subscores increased over time (T1G1: x = 3.83, T2G3: x = 4.07, p = .006). Results from the two measurement points found no difference in mean total scores (T1G1: x = 3.53, T2G3: x = 3.63, p = .09). Comparison of mean total scores of nurses who directly received the educational program with those who did not during the second time point showed no improvement (T2G2: x = 3.52, T2G3: x = 3.63, p = .30).

CONCLUSIONS

Education alone was insufficient in improving self-reported competencies of medical-surgical nurses caring for people with OUD. Findings can be used to inform efforts to increase nurse knowledge and understanding of OUD and to decrease negative attitudes, stigma, and discriminatory behaviors perpetuating care.

Day-to-day opioid withdrawal symptoms, psychological distress, and opioid craving in patients with chronic pain prescribed opioid therapy

BACKGROUND

Research has shown that opioid craving is one of the strongest determinants of opioid misuse in patients with chronic pain. To date, however, little is known on the factors that contribute to opioid craving in these patients. It is possible that patients' physical dependence to opioids, manifested by opioid withdrawal symptoms in between daily opioid doses, contribute to opioid craving. Physical dependence symptoms might also lead to psychological distress, which in turn might contribute to opioid craving. The first objective of this study was to examine the day-to-day association between opioid withdrawal symptoms and opioid craving among patients with chronic pain. We also examined whether negative affect and catastrophic thinking mediated this association.

METHODS

In this longitudinal study, chronic pain patients (n = 79) prescribed short-acting opioids completed daily diaries for 14 consecutive days. Diaries assessed a host of pain, psychological, and opioid-related variables.

RESULTS

Day-to-day elevations in opioid withdrawal symptoms were associated with heightened opioid craving (p < .001). Results of a multilevel mediation analysis revealed that this association was mediated by patients' daily levels of negative affect and catastrophizing (p < .001).

CONCLUSIONS

Our study provides valuable new insights into our understanding of factors that may contribute to prescription opioid craving among patients with chronic pain.

Interobserver agreement between emergency clinicians and nurses for Clinical Opiate Withdrawal Scale

OBJECTIVES

The Clinical Opiate Withdrawal Scale (COWS) is a validated, commonly used tool to objectively quantify withdrawal symptoms, often in anticipation of treatment with buprenorphine. Our primary aim was to determine the agreement between emergency department (ED) nurses compared with emergency physicians in determining this score in ED patients who presented for opioid withdrawal treatment. Secondarily, we wanted to investigate the safety of buprenorphine induction in the ED setting.

METHODS

Scoring for opioid withdrawal using the COWS was performed by ED clinicians and ED nurses independently on 120 patients. In addition to overall concordance, agreement (weighted kappa) was calculated between the 2 scores by various cutoffs: overall severity, COWS ≥ 5, and the 11 different individual measures. Patient documents also were reviewed for complications that could be possibly linked to buprenorphine induction.

RESULTS

Our study sample of 120 subjects was 77% Hispanic and 78.3% male. The clinicians assigned a median interquartile range overall COWS score of 6 (2-12), which categorizes as mild withdrawal. Seventy-eight (65%) subjects met the criteria of withdrawal (≥ 5 COWS) and 69 (58%) received an induction dose of buprenorphine (range 2 mg-24 mg) during the ED visit. No adverse effects or worsening withdrawal were reported. The overall observed concordance, based on severity withdrawal categorization, for all clinician pairs, was 67.5% (81/120) (95% confidence interval [CI], 58.7-75.2%). The weighted kappa for that concordance was 0.55 (95% CI, 0.43-0.67), giving a moderate strength of agreement. When data are dichotomized by COWS score ≥5, concordance was 82.5% (99/120) (95% CI, 74.7%-88.3%) and the weighted kappa was 0.65 (95% CI, 0.51-0.78), indicating substantial agreement. The breakdown by the 11 factors that constitute COWS showed only substantial agreement for pulse measurement.

CONCLUSION

The agreement between ED clinicians and nurses for the overall COWS scoring in patients presenting for opioid withdrawal treatment was substantial. COWS scoring by ED nurses may help expedite treatment with buprenorphine on presentation.

A systematic review of integrative medicine for opioid withdrawal

INTRODUCTION

The United States has been battling an opioid epidemic for decades. As substance use disorders have grown, so too has investigation into treatment options, including integrative medicine approaches, for managing opioid withdrawal symptoms (OWS).

OBJECTIVES

This systematic review sought to assess the use of integrative medicine approaches for the alleviation of OWS in patients dependent on opioids and to summarize the available data.

METHODS

The authors searched using synonyms for opioids, substance use disorder, and integrative medicine and standardized searches in Embase, PubMed, and Cochrane Library. We also hand searched references for systematic reviews. This review did not include articles that could not be obtained as full-text publications via interlibrary loan. The review also excluded studies with interventions involving acupuncture because multiple systematic reviews on this approach already exist. In addition, we also excluded studies of therapy for opioid maintenance. We evaluated studies for inclusion based on the Jadad criteria. We compared opioid withdrawal outcomes of the studies to determine the efficacy of integrative medicine approaches.

RESULTS

The authors identified a total of 382 unique publications initially for possible inclusion through systematic searches. After applying inclusion and exclusion criteria, five studies met Jadad criteria. The authors identified an additional two studies for inclusion via hand searching. A total of seven studies included interventions consisting of passionflower, weinicom, fu-yuan pellet, jinniu capsules, tai-kang-ning, dynorphin, and l-tetrahydropalmatine. Analyzing the articles was difficult given the varied scoring methods they used to quantify opioid withdrawal symptoms and the small sample sizes in the trials. Most showed evidence that supported integrative medicine approaches for OWS, although the strength of evidence was limited because of sample sizes.

CONCLUSIONS

This review found evidence of multiple integrative medicine approaches for opioid withdrawal symptoms. Well-designed randomized controlled trials should assess the efficacy of integrative medicine for improvement in OWS.

Data Needs in Opioid Systems Modeling: Challenges and Future Directions

INTRODUCTION

The opioid crisis is a pervasive public health threat in the U.S. Simulation modeling approaches that integrate a systems perspective are used to understand the complexity of this crisis and analyze what policy interventions can best address it. However, limitations in currently available data sources can hamper the quantification of these models.

METHODS

To understand and discuss data needs and challenges for opioid systems modeling, a meeting of federal partners, modeling teams, and data experts was held at the U.S. Food and Drug Administration in April 2019. This paper synthesizes the meeting discussions and interprets them in the context of ongoing simulation modeling work.

RESULTS

The current landscape of national-level quantitative data sources of potential use in opioid systems modeling is identified, and significant issues within data sources are discussed. Major recommendations on how to improve data sources are to: maintain close collaboration among modeling teams, enhance data collection to better fit modeling needs, focus on bridging the most crucial information gaps, engage in direct and regular interaction between modelers and data experts, and gain a clearer definition of policymakers' research questions and policy goals.

CONCLUSIONS

This article provides an important step in identifying and discussing data challenges in opioid research generally and opioid systems modeling specifically. It also identifies opportunities for systems modelers and government agencies to improve opioid systems models.

Identifying Opioid Withdrawal Using Wearable Biosensors

Wearable biosensors can be used to monitor opioid use, a problem of dire societal consequence given the current opioid epidemic in the US. Such surveillance can prompt interventions that promote behavioral change. Prior work has focused on the use of wearable biosensor data to detect opioid use. In this work, we present a method that uses machine learning to identify opioid withdrawal using data collected with a wearable biosensor. Our method involves developing a set of machine-learning classifiers, and then evaluating those classifiers using unseen test data. An analysis of the best performing model (based on the Random Forest algorithm) produced a receiver operating characteristic (ROC) area under the curve (AUC) of 0.9997 using completely unseen test data. Further, the model is able to detect withdrawal with just one minute of biosensor data. These results show the viability of using machine learning for opioid withdrawal detection. To our knowledge, the proposed method for identifying opioid withdrawal in OUD patients is the first of its kind.

Differences in patient-reported and observer-rated opioid withdrawal symptom etiology, time course, and relationship to clinical outcome

Although opioid withdrawal ratings are frequently used as primary outcomes for therapeutic trials, there has been limited empirical examination of opioid withdrawal symptom onset or etiology as an outcome, and also little examination of differences in outcomes between patient-reported and observer-ratings of withdrawal. Patient-reported (Subjective Opiate Withdrawal Scale, SOWS) and observer ratings (Clinical Opiate Withdrawal Scale, COWS) of opioid withdrawal collected as part of a randomized controlled residential clonidine, tramadol-extended release, and buprenorphine/naloxone 7-day taper for opioid withdrawal management were analyzed. Withdrawal ratings were collected seven times daily and primary outcomes were percent of participants (N = 103) endorsing symptoms, time of symptom onset, and relationship of scales to taper completion. Participants had variable endorsement of specific symptoms, ranging from 37 % ("feel like vomiting") to 97 % ("change in resting pulse"). Symptoms were more likely to be reported on the SOWS than COWS. Most symptoms began around 8 h after last dose, though comparison of like symptoms across the scales revealed patients reported symptoms on the SOWS > 10 h before they were observed on the COWS. SOWS peak severity score was more closely associated with taper completion than the COWS. Data suggest the patient-reported SOWS demonstrated a higher level of symptom endorsement, earlier detection of symptom onset, and better association with taper completion relative to the observer rated COWS. These data provide insight into the etiology of opioid withdrawal symptom expression and time course that can be used to inform treatment intervention timing and provide a baseline for other withdrawal evaluations.

Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review

Background

Advances in technology engender the investigation of technological solutions to opioid use disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited.

Objective

The overarching aim of our research was to design OUD management technologies that utilize wearable sensors to provide continuous monitoring capabilities. The objectives of this study were to (1) document the currently available opioid-related mHealth apps, (2) review past and existing technology solutions that address OUD, and (3) discuss opportunities for technological withdrawal management solutions.

Methods

We used a two-phase parallel search approach: (1) an app search to determine the availability of opioid-related mHealth apps and (2) a scoping review of relevant literature to identify relevant technologies and mHealth apps used to address OUD.

Results

The app search revealed a steady rise in app development, with most apps being clinician-facing. Most of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the scoping review found no study that investigated the efficacy of mHealth apps to address OUD.

Conclusions

Our findings highlight a general gap in technological solutions of OUD management and the potential for mHealth apps and wearable sensors to address OUD.