Opioid Reduction Strategy South Western Ontario

Opioid Use Clinical Trial

Official title:

Implementation of a Multi-faceted Opioid-Use Reduction Strategy for South Western Ontario: A Pragmatic Stepped-Wedge Cluster Randomized Trial

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT04944225
• Other study ID #: MN ORS
• Status: Not yet recruiting
• Phase: N/A
• Start date: November 1, 2024
• Est. completion date: March 1, 2025

Study information

• Verified date: February 2024
• Source: Lawson Health Research Institute
• Contact: Mahesh Nagappa, MD
• Phone: 5196588500
• Email: mahesh.nagappa[@]lhsc.on.ca
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

Pain is a major risk factor for chronic postoperative pain. Adequate perioperative pain relief is an important metric for patient satisfaction and to achieve good recovery outcomes. Opioids remain the primary systemic pharmacotherapy for intraoperative and postoperative analgesia, particularly for moderate to severe pain. When used judiciously, opioids are effective in reducing suffering and helping patients cope with postoperative pain. However, there are challenges - a) side effects can result in harm, like respiratory depression; b) over-reliance on opioids can increase drug dependency; c) over-prescription can encourage addiction, overdose and death, leading to a human and financial burden from both, an individual, and public health standpoint. Over-prescription of opioids for acute pain is strongly linked to patient morbidity and mortality. For example, a new opioid prescription raises the risk of lethal or non-lethal overdose, as well as the conversion from opioid-naive to chronic user. Canadian Institute of Health Information (CIHI), and Public Health Agency of Canada (PHAC) data emphasize the public health need to reduce reliance on opioids: "From January 2016 to June 2018, more than 9,000 Canadians died from apparent opioid related harms. In 2017, an average of 17 Canadians were hospitalized for opioid poisonings each day - an increase from 16 per day in 2016". Prescription opioid use appears to be an early driver of the current crisis. Given the local and national severity of the opioid crisis, there is need for a pragmatic, timely, and scalable intervention to reduce reliance on opioids as we strive to improve healthcare for patients and alleviate the economic burden on the medical system. This proposal for a stepped-wedge randomized trial of a multi-faceted opioid-use reduction strategy addresses key drivers of the opioid crisis and has the potential to reduce patient exposure to opioids and, thereby, improve morbidity and mortality. Hospitals involved in this study will all eventually participate in an opioid reduction strategy that will limit the access and prescription of opioids to surgical patients and will incorporate various opioid reduction strategies at both a patient and hospital level.

Description:

Proposed Trial Design The proposed prospective randomized trial will employ a stepped-wedge design, which is a variant of a cluster randomized trial. This is a pragmatic, two-armed, parallel group registry based, cluster RCT. This SWAHN associated hospitals trial will be embedded into routine care (new standard of care) with implementation of the intervention delivered by perioperative personnel rather than research staff. Patient characteristics and outcomes will be obtained from ICES administrative health care database. This pragmatic design allows broad inclusion of the south west LHIN hospitals and a large representative sample of perioperative patients that should yield highly generalizable findings. The hospitals will be randomized (1:1) with concealed allocation methods and hospitals will be notified of their group allocation by the study team 2 months before the intervention start date.

In this trial, the unit of randomization is the cluster (i,e each of the hospital) and the unit of analysis is the patient. The investigators chose a cluster randomized design to enhance intervention uptake and adherence (logistical convenience) and to minimize cross-group contamination. Perioperative patients typically receive all their intervention at the same centre, making this population suitable for cluster-level interventions. Delivery of the opioid reduction strategy intervention in this cluster trial follows what occurs in routine care, where the perioperative team in each hospital will be trained to follow the same protocol or policy for patients under their care.

This will entail the roll out of the intervention in a randomized fashion and "sequential crossover of clusters from control to intervention until all clusters are exposed". This type of trial is a pragmatic approach to investigate and implement a service delivery change, given that the intervention will occur at the institutional level and individual patients will not need to be randomized.

This multi-centre, stepped-wedge cluster- RCT is designed to evaluate the benefits of an opioid reduction strategy compared to standard practice as per local hospital protocol, in patients undergoing elective surgical procedures, who are prescribed opioids to control acute post- operative pain. In order to overcome data collection challenges, the investigators will collect the outcomes from all elective surgical patients (no exclusions) from electronic databases ICES for the pre- and post-intervention. Following a 2-month baseline period in which all sites use their usual standard-of-care opioid prescription, one of the randomly selected sites will begin implementing and following the opioid reduction strategy; other sites will be randomly added to the intervention group every two months until the strategy is in place at all the sites (total 12 months of data collection). One month before crossover to the phase of opioid reduction strategy at each site, all anesthesia and research staff at the hospital site will be trained by the study PI (Mahesh Nagappa) on the various intervention components, on implementing the opioid reduction strategy, and on collection of data.

Twelve hospitals within the Southwestern Ontario Academic Hospital Network (SWAHN) will be invited to participate. London Health Sciences Centre and St. Joseph's Healthcare London will be the lead sites. For this study, we have selected the perioperative setting in which opioid analgesia prescribing is common during the hospital discharge.

Participants Recruited patients will be those receiving postoperative analgesia at participating study sites after undergoing an elective surgical procedure. Given that the intervention will be implemented on an institutional-wide level (e.g. via changing hospital order sets and protocols), there will be no need to randomize individual patients. Since the intervention is focused on opioid use post-discharge, intraoperative and post-operative pain management will not be specified in the opioid reduction strategy. In-hospital pain management will be at the discretion of the attending anesthesiologist, as described elsewhere in the literature.

Control group: Pain management during the standard of care phase In the standard of care phase, anesthetic and surgical care will be as per standard practice (according to local hospital protocol) for both the control and the intervention group. Generally, this means patients will be maintained on a more liberal opioid regime than in the opioid reduction strategy phase and will receive opioid and other medications for the acute postoperative pain. The choice of opioids will be at the discretion of the managing team. There will be a minimum 2-month baseline period before entry of the first randomized cluster to the intervention arm.

Intervention group: Pain management in the opioid reduction strategy phase The opioid reduction strategy, including the tools and locally contextualized approach to implementation, will be co-designed by a multi-disciplinary team of surgeons, anesthesiologists, pharmacists, and researchers with expertise in knowledge translation, together with patient representatives. To ensure indigenous perspectives are integrated, one of the patient representatives will be from the local indigenous community.

The intervention will involve a multi-faceted 3 component (please see the supplementary file) approach involving 1) opioid prescription caps (default maximum number of tablets for discharge prescriptions, as defined by evidence-based guidelines, such as https://michigan-open.org/prescribing-recommendations/ and https://www.hqontario.ca/evidence-to-improve-care/quality-standards/view-all-quality-standard s/opioid-prescribing-for-acute-pain, 2) patient education tools (e.g. What is a normal pain trajectory? How to manage the pain? Benefits and potential harms of pharmacologic analgesia. Non-pharmacologic analgesia management? What to do if pain is excessive?), 3) provider education tools (e.g. including procedure-specific evidence-based recommendations for multi-modal analgesia; comparison of local baseline prescribing patterns with exemplary prescribing patterns; defining targeted reduction if baseline prescribing is at odds with best evidence; review of best evidence about optimal analgesia perioperatively), and 4) bi-weekly cumulative prescriber feedback on opioid prescribing patterns post-intervention and until end-of-study.

The strategy for implementing each of these components will be contextualized in collaboration with the multidisciplinary team from each hospital prior to entry to the active intervention phase, through meetings with the SWAHN Opioid Choosing Wisely Committee, and with local meetings intensified during the first weeks of the active intervention.

Practical arrangements for allocating participants to trial groups Each hospital will be randomly assigned to one of the crossover dates prior to the start of the study by a statistician who is blinded to hospital identities, using a computer-generated list of random numbers.

Proposed methods for protecting against sources of bias It is not possible to blind clinicians or study staff to hospital allocation because the appropriate pain/opioid management must be transparently applied upon entry to the intervention phase of the study, and therefore the unit of randomization is at the hospital level. Outcome adjudicators will be blinded and patients will remain blinded. We anticipate low risk of selection bias at the patient level because all eligible patients will be enrolled in the study. From the investigators' previous experience, the investigators expect few (<5%) participants to refuse consent to use their data.

Clinicians will abide by the appropriate opioid reduction strategy algorithms. The duration of the opioid reduction strategy phase will vary at each hospital based on the randomization schedule, ranging from 4 to 12 months.

Proposed frequency and duration of follow up The investigators will collect data on patients from PODs 1 (or hospital discharge, whichever comes first), and POD 30 (or death, whichever comes first).

Sample size and power calculations As expected, the investigators found that the sample size calculation is very tricky for this prospective stepped wedge trial. The investigators were originally aiming for sample size calculations based on the Hussey & Hughes approach, for analysis with fixed time effects and random cluster effects. However, the investigators settled on estimating the outcome based on the available power, as the available sample size was very large (>8,000 per month). The investigators will co-opt the ICES statistical team for the analysis, along with one of local statistical experts. This way the investigators will navigate the layers of complexity to overcome the analysis plan. The methods for adjusting for confounders has evolved significantly in recent years. If the investigators consider that there are multiple levels of potential confounders, uneven contamination over time/cluster sizes, larger intracluster correlations, hospital level clustering, and physician level clustering, then the large number of patients across 12 hospitals (7000-8000/month), the investigators will likely have greater than 90% power to detect a 25% reduction in morphine equivalents, even with worst case scenario estimates for above mentioned clustering and confounders. Using hospital statistics data, we estimate that >8000 eligible patients would be registered across 12 SWAHN hospitals over 4 weeks, with a 25% effect size in the MME, and a between hospital coefficient of variation of 0.15. Assuming a constant case-load during the pandemic, independent hospital effects and a 5% significance level, the trial would have >90% power to detect a 25% reduction in MME. If the assumption of independent hospital effects was not met, and the 12 SWAHN hospital clusters functioned effectively as 12 large hospitals, power would be still >80%.

Analyses will be carried out using intention-to-treat, based on the date each hospital is assigned to cross over rather than the actual crossover date. With the patient as the unit of analysis, we will determine the effect of the intervention on primary and secondary outcomes. The investigators will use generalized linear mixed models (GLMM) with logit link for the binary outcome and random intercept to account for the clustering of patients within hospitals. The model will adjust for patient characteristics (e.g., age, obesity, OSA etc) and for time (secular trends). A time by treatment interaction will be tested. The effect of the intervention will be estimated by odds ratios (OR) and 95% confidence intervals (CI) in the final model. Secondary outcomes will be analyzed using GLMM for binary outcomes, linear mixed model for continuous outcome (assuming normal distribution) and survival analysis for length of hospital stay. Rate of missing data should be low (<5%) as outcomes are routinely collected to guide clinical care. Statistical significance will be defined as P<0.05 or 95% confidence intervals that exclude the null effect. All analyses will be conducted using the intention-to-treat approach. We will analyze the primary outcome (and all other continuous variables) using mixed-effects linear regression, with the intervention and time as fixed effects in the model.

A random intercept and slope for time defined at the cluster level will account for within-period and between-period intracluster correlations. Considering the relatively small number of clusters, the investigators will use the Kenward-Roger correction to avoid a potentially inflated type I error rate. The binary secondary outcomes will be analyzed using a similar approach, but with mixed-effects logistic regression models. Outcomes will also be stratified according major types of surgery. Statistical analyses will be performed using SAS and R statistical package.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 100000
• Est. completion date: March 1, 2025
• Est. primary completion date: January 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 19 Years and older

Eligibility

Inclusion Criteria:

• Over 18 years of age

• Undergoing elective surgery during the study period

Exclusion Criteria:

• None

Related Conditions & MeSH terms

• Opioid Use

Intervention

Other:
• Opioid Reduction Strategy
The intervention will involve a multi-faceted 3 component approach involving 1) opioid prescription caps (default maximum number of tablets for discharge prescriptions, as defined by evidence-based guidelines) 2) patient education tools (e.g. What is a normal pain trajectory? How to manage the pain? Benefits and potential harms of pharmacologic analgesia. Non-pharmacologic analgesia management? What to do if pain is excessive?), 3) provider education tools (e.g. including procedure-specific evidence-based recommendations for multi-modal analgesia; comparison of local baseline prescribing patterns with exemplary prescribing patterns; defining targeted reduction if baseline prescribing is at odds with best evidence; review of best evidence about optimal analgesia perioperatively), and 4) bi-weekly cumulative prescriber feedback on opioid prescribing patterns post-intervention and until end-of-study.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Lawson Health Research Institute

References & Publications (17)

Bachhuber MA, Nash D, Southern WN, Heo M, Berger M, Schepis M, Cunningham CO. Reducing the default dispense quantity for new opioid analgesic prescriptions: study protocol for a cluster randomised controlled trial. BMJ Open. 2018 Apr 20;8(4):e019559. doi: 10.1136/bmjopen-2017-019559. — View Citation

Bohnert ASB, Ilgen MA. Understanding Links among Opioid Use, Overdose, and Suicide. N Engl J Med. 2019 Jan 3;380(1):71-79. doi: 10.1056/NEJMra1802148. No abstract available. — View Citation

Canadian Centre on Substance Abuse and Addiction. Canadian Drug Summary: Prescription Opioids. 2017.

Canadian Institute for Health Information. Opioid-Related Harms in Canada, December 2018

CIHI Opioid Prescribing in Canada: How Are Practices Changing? (cihi.ca) Ottawa. 2019

Cozowicz C, Chung F, Doufas AG, Nagappa M, Memtsoudis SG. Opioids for Acute Pain Management in Patients With Obstructive Sleep Apnea: A Systematic Review. Anesth Analg. 2018 Oct;127(4):988-1001. doi: 10.1213/ANE.0000000000003549. — View Citation

Gupta K, Nagappa M, Prasad A, Abrahamyan L, Wong J, Weingarten TN, Chung F. Risk factors for opioid-induced respiratory depression in surgical patients: a systematic review and meta-analyses. BMJ Open. 2018 Dec 14;8(12):e024086. doi: 10.1136/bmjopen-2018-024086. — View Citation

Gupta K, Prasad A, Nagappa M, Wong J, Abrahamyan L, Chung FF. Risk factors for opioid-induced respiratory depression and failure to rescue: a review. Curr Opin Anaesthesiol. 2018 Feb;31(1):110-119. doi: 10.1097/ACO.0000000000000541. — View Citation

Hemming K, Haines TP, Chilton PJ, Girling AJ, Lilford RJ. The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ. 2015 Feb 6;350:h391. doi: 10.1136/bmj.h391. No abstract available. — View Citation

Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007 Feb;28(2):182-91. doi: 10.1016/j.cct.2006.05.007. Epub 2006 Jul 7. — View Citation

Kharasch ED, Brunt LM. Perioperative Opioids and Public Health. Anesthesiology. 2016 Apr;124(4):960-5. doi: 10.1097/ALN.0000000000001012. No abstract available. — View Citation

Nagappa M, Weingarten TN, Montandon G, Sprung J, Chung F. Opioids, respiratory depression, and sleep-disordered breathing. Best Pract Res Clin Anaesthesiol. 2017 Dec;31(4):469-485. doi: 10.1016/j.bpa.2017.05.004. Epub 2017 May 22. — View Citation

Ontario Agency for Health Protection and Promotion (Public Health Ontario); Office of the Chief Coroner; Ontario Forensic Pathology Service; Ontario Drug Policy Research Network. Opioid mortality surveillance report: Anal opioid-related deaths Ontario July 2017-June 2018 Toronto, Queen's Print Ontario 2019

Opioids. Special Advisory Committee on the Epidemic of Opioid Overdoses. National report: Apparent opioid-related deaths in Canada (January 2016 to March 2019). Web Based Report. Ottawa Public Heal Agency Canada; Sept 2019 https//health-infobase.canada.ca/datalab/national-surveillance-opioid-mortality.html

Rennert L, Heo M, Litwin AH, De Gruttola V. Accounting for external factors and early intervention adoption in the design and analysis of stepped-wedge designs: Application to a proposed study design to reduce opioid-related mortality. medRxiv [Preprint]. 2020 Jul 29:2020.07.26.20162297. doi: 10.1101/2020.07.26.20162297. — View Citation

Shah A, Hayes CJ, Martin BC. Characteristics of Initial Prescription Episodes and Likelihood of Long-Term Opioid Use - United States, 2006-2015. MMWR Morb Mortal Wkly Rep. 2017 Mar 17;66(10):265-269. doi: 10.15585/mmwr.mm6610a1. — View Citation

Subramani Y, Nagappa M, Wong J, Patra J, Chung F. Death or near-death in patients with obstructive sleep apnoea: a compendium of case reports of critical complications. Br J Anaesth. 2017 Nov 1;119(5):885-899. doi: 10.1093/bja/aex341. — View Citation

* Note: There are 17 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	total morphine milliequivalents (MME)	The total morphone milliequivalents (MME) prescribed for each patient will be recorded by extracting this data from the ICES administrative healthcare database.	Time of discharge up to 30 days postoperatively
Secondary	Number of serious opioid-related events	The aggregate of over-dose related death, cardiac arrest, respiratory depression, naloxone use, or opioid-related ED visit. Data will be extracted from the ICES administrative healthcare database.	Time of discharge up to 30 days postoperatively
Secondary	Readmission to hospital	Instances patients may have been readmitted to hospital up to 90 days after discharge.Data will be extracted from the ICES administrative healthcare database.	Time of discharge up to 90 days postoperatively
Secondary	Time of discharge	The point post-operatively that the patient is discharged from hospital.Data will be extracted from the ICES administrative healthcare database.	Up to 90 days post operatively
Secondary	Conversion from opioid naïve to chronic user	Measurement of how many patients are continuing to use opioids 90 post operatively.Data will be extracted from the ICES administrative healthcare database.	90 days post operatively
Secondary	Total opioid community exposure	Morphine equivalents discharged to community via postoperative prescriptions.Data will be extracted from the ICES administrative healthcare database.	90 days post operatively
Secondary	composite outcome of "days alive, out-of-hospital, and opioid-free at 90 days	This is a composite outcome that simultaneously incorporates implications of death, readmissions, and opioid-use, and allows for comparison between intervention and control groups. Describes the numbers of patients who are alive, out of hospital and opioid free 90 days post operatively.Data will be extracted from the ICES administrative healthcare database.	90 days post operatively