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Opioid exit plans for tapering postoperative pain control in noncancer patients: a systematic review

Patient Safety in Surgery volume 18, Article number: 25 (2024) Cite this article

Abstract

Background

A growing number of countries have reported sharp increases in the use and harm of opioid analgesics. High rates of new opioid initiation are observed in postoperative patients. In response, various tertiary care institutions have developed opioid exit plans (OEPs) to curb potential opioid-related harm.

Methods

PubMed and Embase were systematically searched to identify, summarize, and compare the interventional elements of OEPs for postoperative patient populations published from January 1, 2000, to June 4, 2024. Two researchers independently screened the articles for eligibility following the PRISMA 2020 guidelines, extracted the data, and assessed the study quality and risk of bias. Data synthesis was performed for study characteristics, intervention details, efficacy, and development.

Results

A total of 2,585 articles were screened, eight of which met the eligibility criteria. All studies were conducted in North America and focused on orthopedic surgery patients following total hip or knee arthroplasty (n = 5) or neurosurgery (n = 3). Most studies (n = 7) included a pre-post (n = 4) or randomized clinical design (n = 3). Three studies were of good quality, and none had a low risk of bias. The interventions varied and ranged from educational sessions (n = 1) to individualized tapering protocols (n = 4) or a combination of the two (n = 2). Key elements were instructions on how to anticipate patients’ postoperative need for opioid analgesics and tapering strategies based on 24-h predischarge opioid consumption. Six studies included efficacy as an endpoint in their analysis, of which four assessed statistical significance, with all four identifying that the OEPs were successful in reducing postoperative opioid use.

Conclusion

Despite differences in design and implementation, the identified OEPs suggest that they are efficacious in reducing outpatient opioid consumption. They provide a robust estimate of postoperative analgesic requirements and a rationale for tapering duration and rate. However, more rigorous studies are needed to evaluate their real-world effectiveness.

Background

Over the past two decades, opioid overdoses have claimed hundreds of thousands of lives, with millions grappling with opioid use disorder [1, 2]. Analyses of drug monitoring systems have revealed high rates of new opioid prescriptions among postoperative patients and within family medicine [3,4,5,6,7,8,9]. While the US opioid crisis is largely fueled by illicit opioid use (i.e., fentanyl), it is a result of an ongoing epidemic rooted in high rates of prescription opioid use [2].

Europe now witnesses a similar surge in prescription opioids [10,11,12,13,14,15,16,17,18,19,20], resulting in an increased incidence of opioid-related harms associated with opioid overconsumption, defined as prolonged use or higher doses for noncancer pain [21,22,23,24]. Notably, prolonged use may develop rapidly among opioid-naïve users [25,26,27]. Despite lower rates of opioid-related deaths in Europe than in the US, early intervention is crucial to prevent a shift from prescription to illicit opioids, as health policies alone may not suffice [28, 29].

Opioid stewardship programs have emerged in North America as a response to the prescription opioid crisis, employing strategies to decrease and track opioid prescriptions [30, 31]. These have been effective in reducing the number of opioid prescriptions or tablets without compromising patient well-being [32,33,34]. At their core, these programs incorporate opioid exit plans (OEPs), consisting of specific strategies that promote drug safety for improved outcomes, closing an important prevention gap.

While some countries are developing guidelines for opioid analgesic deprescribing [35,36,37,38], a recent guideline summary identified a need for greater evidence on the effectiveness of current strategies to inform clinical practice [35]. Therefore, this systematic review aimed to identify and summarize published hospital-based OEPs, detailing their design, main components, and reported evidence of their effectiveness.

Methods

A systematic review was performed according to the PRISMA 2020 guidelines [39] and the SPICE (setting, population, intervention, comparison, evolution) [40] and PCC (population, concept, context) [41] frameworks to define the study environment. The search was conducted in PubMed and Embase using a distinct keyword search string developed with an information specialist (LB). Articles published from January 1, 2000 to June 3, 2024, that explored the discharge management of postoperative patients receiving opioid analgesics were considered eligible. For homogeneous interventional exposure, articles needed to focus on patients 18 years of age or older at discharge, excluding patients with special needs or implications for routine outpatient opioid use after surgery, such as cancer, end-of-life care, and substance use disorders. The articles needed to include an accessible tapering protocol. The full search strategy and list of eligibility criteria for the literature are detailed in the Supplement Tables 1, 2, and 3.

Two searches were conducted (SO, MR), one on April 27, 2023, and an update on June 4, 2024. The results were imported into Rayyan.ai for screening [42] and duplicates were removed. Two researchers (SO, MR) independently screened the abstracts and obtained full-text articles if the predefined eligibility criteria were met. Conflicts in screening were resolved through in-person discussions. If necessary, a third author (DS) was consulted. The Cochrane Effective Practice and Organization of Care Group [43] template was used for consistent and comprehensive data collection on study characteristics and measured intervention efficacy (SO, MR), reported as a percentage reduction in opioid dosage as morphine milligram equivalents (MME) when applicable.

Three reviewers (SO, DS, MR) appraised the quality of evidence of the included studies using the LEGEND (let evidence guide every new decision) evidence evaluation tool [44]. In the LEGEND, a numerical rating system based on the study design determines the basic grading. Indicators "a" and "b" differentiate the quality of evidence: "a" indicates high quality, while "b" indicates inconsistencies or insufficient quality of design [44]. Disagreements in grading were resolved during in-person discussions. If a reported study design was suspected to be incorrect, three reviewers (SO, DS, MR) collectively reclassified the study.

When applicable, two reviewers (MR, DS) independently applied the Revised Risk of Bias tool (RoB2) for randomized controlled trials (RCTs) [45] and the Risk Of Bias In Non-randomized Studies—of Interventions (ROBINS-I) tool for non-randomized studies [46] to identify potential biases and confounders, assessing the level of risk.

Results

Article selection

Figure 1 illustrates the screening and inclusion process [39, 47]. The initial systematic literature search identified 2,483 articles, and the updated search identified 102 articles (n = 2,585). The respective abstracts were screened, and 26 articles were deemed eligible for full-text screening. Eventually, eight articles from the full-text screening were included in the final analysis [48,49,50,51,52,53,54,55].

Fig. 1
figure 1

PRISMA flow diagram of screening and inclusion process [39, 47]

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Study characteristics

Table 1 provides an overview of the characteristics of the eight included studies. All the articles described studies conducted in North America, with 25% (N = 2) in Canada [48, 50] and 75% (N = 6) in the US [49, 51,52,53,54,55]. Half of the studies (N = 4) were quality improvement studies [51, 53,54,55] that were either uncontrolled and retrospective [53,54,55] or controlled and prospective [51]. Three were RCTs (37.5%; N = 3) [48,49,50], and one was a proposed OEP for patient services targeting postoperative pain [52]. For the latter, no conventional study design could be assigned. While the procedures varied, the studies predominantly investigated interventions within orthopedic departments, with total hip arthroplasty (THA) and total knee arthroplasty (TKA) being the most prevalent procedures (75%; N = 6) to involve patients in OEPs [48,49,50,51, 54, 55], followed by neurosurgery (12.5%; N = 1) [53]. The proposed OEP framework by Genord et al. [52] was considered applicable to orthopedic, neurosurgical, and colorectal surgery.

Table 1 Study characteristics of the included studies using the template provided by the Cochrane Effective Practice and Organization of Care Group [43] and quality assessment of different study types using the LEGEND (let evidence guide every new decision) evidence evaluation tool [44]. The numbers in the QA column represent the study design used, and the letters indicate that a is of good quality or b is of lesser quality
Full size table

The patient demographics varied largely within the study populations and the reported items due to differences in study design (Table 1). Across the studies, patients had a mean age between the mid-fifties and mid-sixties, with the lowest mean age being 40.2 years [48] and the highest being 67.0 years [53,54,55]. The gender distribution was rather balanced in three studies [48, 49, 51], whereas studies conducted in Veterans Affairs Facilities [53,54,55] predominantly included male patients, and the study by Singh et al. [50] predominantly included female patients. A history of substance abuse, financial stability, mood disorders, preoperative pain, or prior opioid use was reported by 75% of the studies [48, 49, 51, 53,54,55]. Most studies reported psychiatric comorbidities (62.5%; N = 5) [48, 49, 53,54,55]. This was either done by screening for anxiety and depressive disorders (25%; N = 2) [48, 49], or the screening and the exact entity were not specified [53,54,55]. Kukushliev et al. [54] were the only ones to report further comorbidities, such as cardiovascular, renal, or hepatic diseases or impairments.

Quality of the included studies

Table 1 reports the quality of evidence for each study. Three [49, 51, 55] studies were found to be of good quality. The studies by Hah et al. [49], Chen et al. [51], and Tamboli et al. [55] selected an appropriate study method for the research question. These reported statistically significant results while also describing the intervention, patient allocation, variables, and outcomes clearly. The remainder received lower quality ratings, mostly due to underreporting of important details such as intervention delivery and the randomization process.

Risk of bias

Figure 2 [56] visualizes the bias judgments. All studies had a moderate to high risk of bias. The RCT by Hah et al. [49] was the only RCT with good quality evidence and moderate bias. However, there were some concerns regarding deviations from the intended protocol intervention. Among the non-RCT studies, the studies by Chen et al. [51] and Tamboli et al. [55] were both high quality. However, there were moderate to serious concerns regarding confounding, participant selection, outcome measurement, and protocol deviations.

Fig. 2
figure 2

Visualization of the risk of bias assessments in the respective domains (D) [56] using the Revised Risk of Bias tool [45] for randomized controlled trials (RCTs) and the Risk Of Bias In Non-randomized Studies—of Interventions tool [46] for non-randomized studies (non-RCTs)

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Overview of interventions and outcome assessment

Table 2 provides the details of the intervention strategies. The most common (75%, N = 6) feature was an individualized tapering approach [50,51,52,53,54,55]. Tamboli et al., Joo et al., and Kukushliev et al. [53,54,55] used patients’ 24-h predischarge opioid utilization to generate a patient-specific tapering plan. In the pre-post design study by Chen et al. [51], the intervention was a model that converted 24-h predischarge opioid utilization to the preferred opioid analgesic for discharge and to the preferred tapering duration in days (0, 7, or 14 days) depending on the type of surgery. Singh et al. [50] assigned patients to risk groups for postoperative pain with risk group-specific tapers based on procedure type, which focused on postoperative patient satisfaction rather than on reducing the amount of opioids prescribed at discharge. Contrary to individualizing tapering regimens, Hah et al. [49] employed postoperative motivational interviewing to promote patients’ efforts toward medication adherence, opioid tapering, and pain management while closely monitoring pain outcomes and opioid-related adverse events.

Table 2 Summary of the interventions and details of the outcome assessments among the included articles using the template provided by the Cochrane Effective Practice and Organization of Care Group [43]
Full size table

The articles by Bérubé et al. [48, 57] and Genord et al. [52] describe combined interventions that extended beyond primarily comprising a tapering protocol (Table 2). Bérubé et al. [48, 57] emphasized educational interventions. Patients participated in face-to-face educational sessions prior to discharge and thereafter, focusing on multimodal pain management and guidance on opioid tapering. Pain levels and interference with daily life were closely assessed after hospital discharge and complemented with generic tapering recommendations. These efforts aimed to improve patients’ self-management. At discharge, patients received an educational pamphlet with the aforementioned information. Genord et al. [52] proposed a yet to be trialed three-phase OEP to support opioid cessation. The first phase, prior to discharge, will include interdisciplinary rounds to assess analgesic needs and discharge eligibility. In the second phase, patients receive discharge counseling and an individualized pain management plan. In the third and final phase after discharge, patients will undergo medication evaluations based on progress with the prescribed pain regimen, opioid discontinuation status, and opioid-related adverse events.

All the published OEPs were developed for standard opioid analgesics (Table 2) using various decreasing approaches. Most studies did not restrict inclusion based on opioid type. Chen et al. [51] provided opioid conversion factors to taper the preferred opioid, and Singh et al. [50] included a predefined set of opioids (hydromorphone, oxycodone/acetaminophen, tramadol/acetaminophen). Hah et al. [49] and Bérubé et al. [48] did not specify. Genord et al. [52] proposed an untrialed tapering regimen to be applicable to any opioid analgesic. The studies based on the tapering regimen by Tamboli et al. [53,54,55] (Table 2) focused specifically on oxycodone. The OEP regimens followed either a linear [50, 53,54,55], exponential [48, 49, 52], or logarithmic [51] reducing tapering approach. The duration was either fixed for the investigated patient population [52,53,54,55] or adapted to the type of procedure [50, 51], while Hah et al. [49] and Bérubé et al. [48] did not predetermine a day of opioid or tapering cessation.

Table 2 also provides an overview of the primary endpoints. Overall, six of the eight studies assessed the efficacy of OEPs on opioid reduction or pain [48, 49, 51, 53,54,55], of which four reported statistical significance [49, 51, 53, 55]. Tamboli et al., Joo et al., and Kukushliev et al. [53,54,55] demonstrated a decrease in the dosage of opioids as MME of 56% (630 vs 280 MME, p < 0.01) and 63% (900 vs 295 MME, p < 0.01) within six weeks of postoperative discharge in the preintervention period and postintervention period, respectively. Similarly, compared to the preintervention period, the approach by Chen et al. [51] resulted in a 24% reduction in the quantity of opioids consumed at discharge (427 vs. 326 MMEs, p < 0.001). After discharge, the authors reported the rate of opioid refills within 30 days (1.58 vs 1.71 mean number, p = 0.082) rather than reductions in MME. An RCT by Hah et al. [49] found that patients receiving motivational interviewing and opioid taper support were 62% more likely to return to baseline opioid use than patients in the standard care group (hazard ratio 1.62, 95% confidence interval 1.06–2.44). Detailed information on the intervention content and provider delivery is provided in Supplement Table 4.

Discussion

This systematic review identified and summarized eight published OEPs [48,49,50,51,52,53,54,55] from hospital settings, providing concepts for the development of novel OEPs in tertiary care settings. Despite the heterogeneity of the approaches investigated, all articles that reported hypothesis testing of their primary outcomes [48,49,50,51, 53,54,55] were successful in achieving either a reduction in opioids at or after discharge. While none of the studies had a low risk of bias, three were of high quality according to the LEGEND quality assessment tool. All good-quality studies [49, 51, 55] yielded statistically significant results, demonstrating that the use of OEPs could effectively reduce the quantity of opioids used at or after discharge. This review therefore highlights that the application of OEPs in clinical practice could be an important addition to reducing discharge opioid consumption.

In this review, no standard OEP approach was identified, as individualization of the intervention and tapering appeared to be integral to meeting a patient’s individual analgesic need during deprescribing. This finding is in line with current evidence-based guidelines [35, 58, 59], as factors such as preoperative opioid use, preexisting pain conditions, social status, psychological comorbidities, and procedure types greatly influence pain and the risk of prolonged opioid use [60,61,62,63]. Among the identified OEPs in this review, implemented strategies included procedure-specific risk groups [50], total 24-h predischarge opioid consumption [51,52,53,54,55], or common pain and withdrawal assessments combined with taper counseling [48, 49]. Using 24-h predischarge opioid consumption is the most common approach and is a time-saving and practical way to individualize tapering, as the need for analgesia typically decreases as patients recover from surgery. This method has limitations, notably, its inapplicability to patients with a shorter inpatient stay than 24 h. Additionally, a shorter postoperative stay can affect pain assessments, as the residual effects of anesthesia may not have fully dissipated [63, 64]. In contrast, Hah et al. [49] and Bérubé et al. [48] employed standardized tapering rates but still individualized the tapering by continuous and close patient contact through follow-ups. The repeated assessment of pain and withdrawal symptoms during follow-up sessions facilitated adjusting the tapering to the patients’ needs. As a result, this method appears to be suitable even for complex cases and ensures sustained positive patient outcomes. Finally, Hah et al. [49] halved the time to baseline opioid use, reflecting the success of such an approach. This approach is also promoted in the American Center for Disease Control guidelines, suggesting that patients with acute pain who receive opioids for a longer time should be evaluated with a two-week frequency [59].

Although, Singh et al. [50] did not assess the statistical significance of their intervention, the OEP included an interesting element of risk stratification in opioid tapering. They allocated patients to one of three risk groups according to procedure type and anticipated postoperative opioid use to prescribe the total number of opioid tablets. A large meta-analysis including 37 studies with 1,969,953 surgery and trauma patients showed that patient-specific opioid requirements were the risk factors with the strongest association with developing chronic opioid use [62]. The American Centers for Disease Control proposed a 6- to 15-day opioid prescription for musculoskeletal procedures [65]. While stratification by procedure type may facilitate the estimation of the ideal number of opioid tablets to be prescribed at discharge, it does not address individual analgesic needs such as patient-specific opioid requirements, which are captured by reviewing 24-h opioid use prior to discharge. It may be promising to combine elements of stratification according to procedure and risk by creating risk groups based on key risk factors for chronic pain and prolonged opioid use. Opioid quantities can be minimized using 24-h inpatient opioid consumption and further individualized by dividing patients into different risk groups: if two patients have the same 24-h inpatient opioid use but one patient is in a higher risk group, the higher risk patient would have a slower tapering rate and more intensive follow-up.

Notably, this review focused on the application of OEPs in postoperative patients. In addition to chronic primary pain, noncancer postoperative patients are subject to the introduction of prescription opioid analgesics or to a higher dose than before admission [4,5,6,7,8,9]. Karmali et al. [66] showed that postoperative pain management is a key driver of long-term opioid use. Relevant predictors [60, 66, 67] for long-term opioid therapy, such as history of substance abuse, financial stability, mood disorders, preoperative pain, or preoperative opioid usage, were reported in almost all studies (75%; N = 6) [48, 49, 51, 53,54,55]. The study designs showed efficacy for surgical specialties associated with high invasiveness, such as orthopedic and spine surgery. For example, in orthopedic surgery, recommendations for the number of tablets range from 0 to 40 tablets of 5 mg oxycodone [68, 69]. This is equivalent to 0 to 300 MME. The described studies that measured the efficacy and the MME [51, 53,54,55] were approximately within the recommended postdischarge dose after the implementation of the tapering interventions. This suggests that tapering protocols have a positive influence on prescribing behavior toward guideline-recommended doses and that psychosocial aspects should be assessed. Thus, OEPs should be considered for implementation in “Enhanced Surgical Recovery” protocols as a valuable addition to patient safety, similar to opioid-free anesthesia [70]. These efforts may have a synergistic effect on opioid-sparing, as these have demonstrated in RCTs to reduce the requirement of postoperative analgesia [71,72,73].

There was a lack of high-quality studies, and none of the included OEPs were deemed to have a low risk of bias. Most articles lacked detailed information on the process, the rationale behind developing the tapering interventions, and consistent reporting of study endpoints. Bias concerns in RCTs mainly stemmed from randomization and intervention adherence. Some studies had predictable allocation [48] or lacked sequence information [50], while others poorly documented deviations from interventions [49, 50]. Adherence to tapering protocols was measured in only one non-RCT study [51]. It is inconclusive whether the steep logarithmic tapering method developed by Chen et al. [51] is superior to the slower linear tapering method developed by Tamboli et al. [55], or vice versa, in reducing opioid dose and improving rehabilitation outcomes. Future trials need to address these limitations and enhance the quality of the data by blinding outcome assessors. Further studies with a more rigorous study design are needed to validate the effectiveness of OEPs. The identified articles focused on the efficacy of their novel tools for assessing opioid-related outcomes, such as the number of opioid tablets taken, rather than on rehospitalization, or on extending the findings to a wider population.

The strengths of this review include the use of a robust keyword search string to screen two major medical publication platforms (PubMed and Embase). All identified articles were evaluated for quality of design and risk of bias to assess the validity of the findings. Ultimately, these findings help to reliably inform clinical practice and provide resources for the development of OEPs, allowing institutions to tailor tapering approaches to meet the needs of their patients. Limitations include the omission of articles published before 2000 and those not indexed in PubMed and Embase, including gray literature such as internal hospital guidelines and predischarge opioid-sparing protocols (e.g., enhanced recovery programs). Articles written in languages other than English or German were also excluded, as were those with inaccessible tapering protocols. Due to the eligibility criteria, the findings have limited applicability to patients with chronic opioid use and psychiatric disorders and no evidence for use in pediatrics.

Conclusions

Despite differences in the patient populations, the studies that evaluated efficacy found that the use of OEPs with tapering plans consistently reduced opioid consumption. The 24-h predischarge method provides a robust estimate of outpatient analgesic requirements, which can be complemented by risk group stratification for tapering speed. More rigorous studies are needed to assess the effectiveness of these tapering approaches on a larger scale.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

LEGEND:

Let Evidence Guide Every New Decision

MME:

Morphine milligram equivalents

OEP:

Opioid Exit Plan

PCC:

Population, Concept, Context

RCT:

Randomized Clinical Trial

RoB2:

Revised Risk of Bias tool

ROBINS-I:

Risk of Bias In Non-randomized Studies—of Interventions

SPICE:

Setting, Population, Intervention, Comparison, Evolution

THA:

Total Hip Arthroplasty

TKA:

Total Knee Arthroplasty

References

  1. Spencer M, Miniño A, Warner M. Drug Overdose Deaths in the United States, 2001–2021 [Internet]. National Center for Health Statistics (U.S.); 2022 [cited 2023 Oct 24]. Available from: https://stacks.cdc.gov/view/cdc/122556

  2. Understanding the Opioid Overdose Epidemic [Internet]. Center for Disease Control and Prevention; 2021 [cited 2023 Oct 24]. Available from: https://www.cdc.gov/opioids/basics/epidemic.html

  3. Gomes T, Men S, Campbell TJ, Tadrous M, Mamdani MM, Paterson JM, et al. Changing patterns of opioid initiation for pain management in Ontario, Canada: A population-based cross-sectional study. Chen RJ, editor. PLOS ONE. 2022;17(12):e0278508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Guy GP, Zhang K. Opioid prescribing by specialty and volume in the US. Am J Prev Med. 2018;55(5):e153–5.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Levy B, Paulozzi L, Mack KA, Jones CM. Trends in opioid analgesic-prescribing rates by specialty, U.S., 2007–2012. Am J Prev Med. 2015;49(3):409–13.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Regueras Escudero E, López Guzmán J. Prescriptions of opioid medicines in Spain between 2019 and 2020: what medical specialities are prescribing them and in what indications. Multidiscipl Pain J. 2021 [cited 2024 Jan 22]; Available from: https://www.mpainjournal.com/Documentos/ArticulosNew/01-Art-6-ING_3_1.pdf

  7. Kiang MV, Humphreys K, Cullen MR, Basu S. Opioid prescribing patterns among medical providers in the United States, 2003–17: retrospective, observational study. BMJ. 2020;29:l6968.

    Article  Google Scholar 

  8. Ringwalt C, Gugelmann H, Garrettson M, Dasgupta N, Chung AE, Proescholdbell SK, et al. Differential prescribing of opioid analgesics according to physician specialty for medicaid patients with chronic noncancer pain diagnoses. Pain Res Manag. 2014;19(4):179–85.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Weiner SG, Baker O, Rodgers AF, Garner C, Nelson LS, Kreiner PW, et al. Opioid prescriptions by specialty in Ohio, 2010–2014. Pain Med. 2018;19(5):978–89.

    Article  PubMed  Google Scholar 

  10. Chenaf C, Kaboré JL, Delorme J, Pereira B, Mulliez A, Zenut M, et al. Prescription opioid analgesic use in France: trends and impact on morbidity-mortality. Eur J Pain. 2019;23(1):124–34.

    Article  CAS  PubMed  Google Scholar 

  11. Schubert I, Ihle P, Sabatowski R. Increase in Opiate Prescription in Germany Between 2000 and 2010. Dtsch Ärztebl Int. 2013 [cited 2024 Jan 19]; Available from: https://www.aerzteblatt.de/10.3238/arztebl.2013.0045

  12. Musazzi UM, Rocco P, Brunelli C, Bisaglia L, Caraceni A, Minghetti P. Do laws impact opioids consumption? A breakpoint analysis based on Italian sales data. J Pain Res. 2018;11:1665–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wagemaakers FN, Hollingworth SA, Kreijkamp-Kaspers S, Tee EHL, Leendertse AJ, Van Driel ML. Opioid analgesic use in Australia and The Netherlands: a cross-country comparison. Int J Clin Pharm. 2017;39(4):874–80.

    Article  PubMed  Google Scholar 

  14. Kalkman GA, Kramers C, Van Dongen RT, Van Den Brink W, Schellekens A. Trends in use and misuse of opioids in the Netherlands: a retrospective, multi-source database study. Lancet Public Health. 2019;4(10):e498–505.

    Article  PubMed  Google Scholar 

  15. Dzierżanowski T, Ciałkowska-Rysz A. Accessibility of opioid analgesics and barriers to optimal chronic pain treatment in Poland in 2000–2015. Support Care Cancer. 2017;25(3):775–81.

    Article  PubMed  Google Scholar 

  16. Agencia Espanola de Medicamentos y Productos Sanitarios. Utilización de medicamentos opioides en España durante el periodo 2008–2015 [Internet]. 2017 Feb [cited 2024 Jan 19] p. 3. (Informe De Utilización De Medicamentos). Report No.: U/OPI/V1/13022017. Available from: https://www.aemps.gob.es/medicamentosUsoHumano/observatorio/docs/opioides-2008-2015.pdf

  17. Jani M, Birlie Yimer B, Sheppard T, Lunt M, Dixon WG. Time trends and prescribing patterns of opioid drugs in UK primary care patients with non-cancer pain: A retrospective cohort study. Song Z, editor. PLOS Med. 2020;17(10):e1003270.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nissen SK, Pottegård A, Ryg J. Trends of opioid utilisation in Denmark: a nationwide study. Drugs - Real World Outcomes. 2019;6(4):155–64.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zin CS, Chen L-C, Knaggs RD. Changes in trends and pattern of strong opioid prescribing in primary care. Eur J Pain. 2014;18(9):1343–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wertli MM, Reich O, Signorell A, Burgstaller JM, Steurer J, Held U. Changes over time in prescription practices of pain medications in Switzerland between 2006 and 2013: an analysis of insurance claims. BMC Health Serv Res. 2017;17(1):167.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kurdi A. Opioids and gabapentinoids utilisation and their related-mortality trends in the United Kingdom primary care setting, 2010–2019: a cross-national, population-based comparison study. Front Pharmacol. 2021;14(12):732345.

    Article  Google Scholar 

  22. Burgstaller JM, Held U, Signorell A, Blozik E, Steurer J, Wertli MM. Increased risk of adverse events in non-cancer patients with chronic and high-dose opioid use—A health insurance claims analysis. Suppiah V, editor. PLOS ONE. 2020;15(9):e0238285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Gjersing L, Amundsen E. Increasing trend in accidental pharmaceutical opioid overdose deaths and diverging overdose death correlates following the opioid prescription policy liberalization in Norway 2010–2018. Int J Drug Policy. 2022;108:103785.

    Article  PubMed  Google Scholar 

  24. Hooijman MF, Martinez-De La Torre A, Weiler S, Burden AM. Opioid sales and opioid-related poisonings in Switzerland: A descriptive population-based time-series analysis. Lancet Reg Health - Eur. 2022;20:100437.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bickel W, Stitzer M, Liebson I, Bigelow GE. Acute physical dependence in man: Effects of naloxone after brief morphine exposure. J Pharmacol Experiment Therapeut. 1988;244(1):126–32.

  26. Kirby KC, Stitzer ML. Opioid physical dependence development in humans: effect of time between agonist pretreatments. Psychopharmacology. 1993;112(4):511–7.

    Article  CAS  PubMed  Google Scholar 

  27. Heishman S, Stitzer M, Bigelow G, Liebson I. Acute opioid physical dependence in humans: Effect of varying the morphine-naloxone interval. J Pharmacol Experiment Therapeut. 1989;250(2):485–91.

  28. Sandbrink F, Uppal R. The time for opioid stewardship is now. Jt Comm J Qual Patient Saf. 2019;45(1):1–2.

    PubMed  Google Scholar 

  29. The Lancet Regional Health – Americas. Opioid crisis: addiction, overprescription, and insufficient primary prevention. Lancet Reg Health - Am. 2023;23:100557.

    PubMed  PubMed Central  Google Scholar 

  30. Schug SA. Opioid stewardship can reduce inappropriate prescribing of opioids at hospital discharge. Med J Aust. 2020;213(9):409–10.

    Article  PubMed  Google Scholar 

  31. Michalowski A, Boateng S, Fraser MR, Levine RL. Developing a Culture of Opioid Stewardship: The Pennsylvania Example. In: A public health guide to ending the opioid epidemic [Internet]. Oxford University Press; 2019 [cited 2024 Mar 14]. p. 307–22. Available from: https://academic.oup.com/book/32440/chapter/268773901

  32. Khorfan R, Shallcross ML, Yu B, Sanchez N, Parilla S, Coughlin JM, et al. Preoperative patient education and patient preparedness are associated with less postoperative use of opioids. Surgery. 2020;167(5):852–8.

    Article  PubMed  Google Scholar 

  33. Kushner BS, Tan WH, Sehnert M, Jordan K, Aft R, Silviera M, et al. Assessment of postoperative opioid stewardship using a novel electronic-based automated text and phone messaging platform. Surgery. 2021;169(3):660–5.

    Article  PubMed  Google Scholar 

  34. Coulson EE, Kral LA. The Clinical Pharmacist’s Role in Perioperative Surgical Pain Management. J Pain Palliat Care Pharmacother. 2020;34(3):120–6.

    Article  PubMed  Google Scholar 

  35. Langford AV, Lin CC, Bero L, Blyth FM, Doctor J, Holliday S, et al. Clinical practice guideline for deprescribing opioid analgesics: summary of recommendations. Med J Aust. 2023;219(2):80–9.

    Article  PubMed  Google Scholar 

  36. Hamilton M, Kwok WS, Hsu A, Mathieson S, Gnjidic D, Deyo R, et al. Opioid deprescribing in patients with chronic noncancer pain: a systematic review of international guidelines. Pain. 2023;164(3):485–93.

    Article  PubMed  Google Scholar 

  37. Sullivan MD. Opioid deprescribing guidelines and consumer preferences: betwixt and between. Pain. 2021;162(11):2625–6.

    Article  PubMed  Google Scholar 

  38. Nelson LS, Mazer-Amirshahi M, Perrone J. Opioid Deprescribing in Emergency Medicine—A Tool in an Expanding Toolkit. JAMA Netw Open. 2020;3(3):e201129.

    Article  PubMed  Google Scholar 

  39. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol. 2021;134:103–12.

    Article  PubMed  Google Scholar 

  40. Booth A. Clear and present questions: formulating questions for evidence based practice. Cleyle S, editor. Libr Hi Tech. 2006;24(3):355–68.

    Article  Google Scholar 

  41. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth. 2020;18(10):2119–26.

    Article  PubMed  Google Scholar 

  42. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Cochrane Effective Practice and Organisation of Care (EPOC), editor. Good practice data extraction form, EPOC resources for review authors, 2017 [Internet]. epoc.cochrane.org/epoc-resources-review-authors; [cited 2023 Jun 20]. Available from: https://epoc.cochrane.org/sites/epoc.cochrane.org/files/public/uploads/Resources-for-authors2017/good_practice_data_extraction_form.doc

  44. Clark E, Burkett K, Stanko-Lopp D. Let Evidence Guide Every New Decision (LEGEND): an evidence evaluation system for point-of-care clinicians and guideline development teams. J Eval Clin Pract. 2009;15(6):1054–60.

    Article  PubMed  Google Scholar 

  45. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;28:l4898.

    Article  Google Scholar 

  46. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;12:i4919.

    Article  Google Scholar 

  47. Haddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Syst Rev. 2022;18(2):e1230.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bérubé M, Dupuis S, Leduc S, Roy I, Turcotte V, Côté C, et al. Tapering opioid prescription program for high-risk trauma patients: a pilot randomized controlled trial. Pain Manag Nurs. 2022;23(2):142–50.

    Article  PubMed  Google Scholar 

  49. Hah JM, Trafton JA, Narasimhan B, Krishnamurthy P, Hilmoe H, Sharifzadeh Y, et al. Efficacy of motivational-interviewing and guided opioid tapering support for patients undergoing orthopedic surgery (MI-Opioid Taper): A prospective, assessor-blind, randomized controlled pilot trial. EClinicalMedicine. 2020;28:100596.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Singh S, Clarke C, Lawendy AR, Macleod M, Sanders D, Tieszer C. First Place: A prospective, randomized controlled trial of the impact of written discharge instructions for postoperative opioids on patient pain satisfaction and on minimizing opioid risk exposure in orthopaedic surgery. Curr Orthop Pract. 2018;29(4):292–6.

    Article  Google Scholar 

  51. Chen EY, Betancourt L, Li L, Trucks E, Marcantonio A, Tornetta P. Standardized, patient-specific, postoperative opioid prescribing after inpatient orthopaedic surgery. J Am Acad Orthop Surg. 2020;28(7):e304–18.

    Article  PubMed  Google Scholar 

  52. Genord C, Frost T, Eid D. Opioid exit plan: A pharmacist’s role in managing acute postoperative pain. J Am Pharm Assoc. 2017;57(2):S92–8.

    Article  Google Scholar 

  53. Joo SS, Hunter OO, Tamboli M, Leng JC, Harrison TK, Kassab K, et al. Implementation of a patient-specific tapering protocol at discharge decreases total opioid dose prescribed for 6 weeks after elective primary spine surgery. Reg Anesth Pain Med. 2020;45(6):474–8.

    Article  PubMed  Google Scholar 

  54. Kukushliev VV, Sherman KA, Kurylo CM, Ortmann SD, Scheidt RA, Scheidt KB. Tapered dose postoperative opioid prescriptions following inpatient total hip and knee arthroplasty: quality improvement study and retrospective review. J Arthroplasty. 2023;38(2):239–44.

    Article  PubMed  Google Scholar 

  55. Tamboli M, Mariano ER, Gustafson KE, Briones BL, Hunter OO, Wang RR, et al. A multidisciplinary patient-specific opioid prescribing and tapering protocol is associated with a decrease in total opioid dose prescribed for six weeks after total hip arthroplasty. Pain Med. 2020;21(7):1474–81.

    Article  PubMed  Google Scholar 

  56. McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Synth Methods [Internet]. n/a(n/a). Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jrsm.1411

  57. Bérubé M, Deslauriers V, Leduc S, Turcotte V, Dupuis S, Roy I, et al. Feasibility of a tapering opioids prescription program for trauma patients at high risk of chronic consumption (TOPP-trauma): protocol for a pilot randomized controlled trial. Pilot Feasibility Stud. 2019;5(1):67.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Royal College of Physicians. Medicines associated with dependence or withdrawal symptoms: safe prescribing and withdrawal management for adults [Internet]. National Institute for Health and Care Excellence; 2022. Available from: https://www.nice.org.uk/guidance/ng215/chapter/Recommendations#reviewing-a-dependence-forming-medicine-or-antidepressant

  59. Dowell D, Ragan KR, Jones CM, Baldwin GT, Chou R. CDC Clinical practice guideline for prescribing opioids for pain — United States, 2022. MMWR Recomm Rep. 2022;71(3):1–95.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Stark N, Kerr S, Stevens J. Prevalence and predictors of persistent post-surgical opioid use: a prospective observational cohort study. Anaesth Intensive Care. 2017;45(6):700–6.

    Article  CAS  PubMed  Google Scholar 

  61. Lawal OD, Gold J, Murthy A, Ruchi R, Bavry E, Hume AL, et al. Rate and risk factors associated with prolonged opioid use after surgery: a systematic review and meta-analysis. JAMA Netw Open. 2020;3(6):e207367.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Mohamadi A, Chan JJ, Lian J, Wright CL, Marin AM, Rodriguez EK, et al. Risk factors and pooled rate of prolonged opioid use following trauma or surgery: a systematic review and meta-(regression) analysis. J Bone Jt Surg. 2018;100(15):1332–40.

    Article  Google Scholar 

  63. Lappalainen E, Ruohoaho U, Kokki H, Aaltomaa S, Anttila M, Gissler M, et al. Postoperative pain in a prospectively assessed surgical short-stay cohort: a subgroup analysis. Acta Anaesthesiol Scand. 2022;66(10):1193–201.

    Article  PubMed  Google Scholar 

  64. Luo J, Min S. Postoperative pain management in the postanesthesia care unit: an update. J Pain Res. 2017;10:2687–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Scully RE, Schoenfeld AJ, Jiang W, Lipsitz S, Chaudhary MA, Learn PA, et al. Defining optimal length of opioid pain medication prescription after common surgical procedures. JAMA Surg. 2018;153(1):37.

    Article  PubMed  Google Scholar 

  66. Karmali RN, Bush C, Raman SR, Campbell CI, Skinner AC, Roberts AW. Long-term opioid therapy definitions and predictors: A systematic review. Pharmacoepidemiol Drug Saf. 2020;29(3):252–69.

    Article  PubMed  Google Scholar 

  67. Papadomanolakis-Pakis N, Haroutounian S, Christiansen CF, Nikolajsen L. Prediction of chronic postsurgical pain in adults: a protocol for multivariable prediction model development. BMJ Open. 2021;11(12):e053618.

    Article  PubMed Central  Google Scholar 

  68. The NO PAin Investigators, Duong A, Ponniah AK, VanDeCapelle C, Mossuto F, Romeril E, et al. Effect of a postoperative multimodal opioid-sparing protocol vs standard opioid prescribing on postoperative opioid consumption after knee or shoulder arthroscopy: a randomized clinical trial. JAMA. 2022;328(13):1326.

    Article  PubMed Central  Google Scholar 

  69. Atwood K, Shackleford T, Lemons W, Eicher JL, Lindsey BA, Klein AE. Postdischarge opioid use after total hip and total knee arthroplasty. Arthroplasty Today. 2021;7:126–9.

    Article  PubMed  PubMed Central  Google Scholar 

  70. McLott J, Stahel PF. Opioid-free anesthesia: the next frontier in surgical patient safety. Patient Saf Surg. 2022;16(1):38 s13037-022-00346–5.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Rani US, Panda NB, Chauhan R, Mahajan S, Kaloria N, Tripathi M. Comparison of the effects of opioid-free anesthesia (OFA) and opioid-based anesthesia (OBA) on postoperative analgesia and intraoperative hemodynamics in patients undergoing spine surgery: A prospective randomized double-blind controlled trial. Saudi J Anaesth. 2024;18(2):173–80.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Vishnuraj KR, Singh K, Sahay N, Sinha C, Kumar A, Kumar N. Opioid-free anesthesia using a combination of ketamine and dexmedetomidine in patients undergoing laparoscopic cholecystectomy: a randomized controlled trial. Anesth Pain Med. 2024;19(2):109–16.

    Article  Google Scholar 

  73. Hu Y, Zhang QY, Qin GC, Zhu GH, Long X, Xu JF, et al. Balanced opioid-free anesthesia with lidocaine and esketamine versus balanced anesthesia with sufentanil for gynecological endoscopic surgery: a randomized controlled trial. Sci Rep. 2024;14(1):11759.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to the Chemistry | Biology | Pharmacy Information Center of ETH Zurich for their continuing support.

Funding

Open access funding provided by Swiss Federal Institute of Technology Zurich This study was not supported by external funding.

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Author notes

  1. Marcel Rainer and Sarah Maleika Ommerli contributed equally as co-first authors.

Authors and Affiliations

  1. Institute of Pharmaceutical Sciences, ETH Zurich, Vladimir-Prelog Weg 1-5/10, 8093, Zurich, Switzerland

    Marcel Rainer, Sarah Maleika Ommerli, Andrea Michelle Burden & Dominik Stämpfli

  2. Hospital Pharmacy, Department Medical Services, Kantonsspital Baden, Im Ergel, 5404, Baden, Switzerland

    Marcel Rainer & Dominik Stämpfli

  3. Chemistry | Biology | Pharmacy Information Center, ETH Zurich, Vladimir-Prelog Weg 10, 8093, Zurich, Switzerland

    Leo Betschart

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  1. Marcel Rainer
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Contributions

CRediT author contributions: MR: Methodology, Formal analysis, Investigation, Data curation, Writing – Original draft, Writing – Review and Editing, Visualization. SO: Formal analysis, Investigation, Writing – Original draft, Visualization, Writing – Review and Editing. DS: Conceptualization, Methodology, Supervision, Writing – Review and Editing. AB: Conceptualization, Methodology, Writing – Review and Editing. LB: Methodology, Writing – Review and Editing

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Correspondence to Dominik Stämpfli.

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Rainer, M., Ommerli, S.M., Burden, A.M. et al. Opioid exit plans for tapering postoperative pain control in noncancer patients: a systematic review. Patient Saf Surg 18, 25 (2024). https://doi.org/10.1186/s13037-024-00408-w

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Keywords

Patient Safety in Surgery

ISSN: 1754-9493