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BACKGROUND: Acute post-surgical pain is managed inadequately in many patients undergoing surgery. Several prognostic risk prediction models have been developed to identify patients at high risk of developing moderate to severe acute post-surgical pain. The aim of this systematic review was to describe and evaluate the methodological conduct of these prediction models. METHODS: We searched MEDLINE, EMBASE and CINAHL for studies of prognostic risk prediction models for acute post-surgical pain using predetermined criteria. Prediction model performance was evaluated according to discrimination and calibration. Adherence to TRIPOD guidelines was assessed. Risk of bias and applicability was independently assessed by two reviewers using the prediction model risk of bias assessment tool. RESULTS: We included 14 studies reporting on 17 prediction models. The most common predictors identified in final prediction models included age; surgery type; sex or gender; anxiety or fear of surgery; pre-operative pain intensity; pre-operative analgesic use; pain catastrophising; and expected surgical incision size. Discrimination, measured by the area under receiver operating characteristic curves or c-statistic, ranged from 0.61 to 0.83. Calibration was only reported for seven models. The median (IQR [range]) overall adherence rate to TRIPOD items was 62 (53-66 [47-72])%. All prediction models were at high risk of bias. CONCLUSIONS: Effective prediction models could support the prevention and treatment of acute post-surgical pain; however, existing models are at high risk of bias which may affect their reliability to inform practice. Consideration should be given to the goals, timing of intended use and desired outcomes of a prediction model before development.

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BACKGROUND: Treatment with opioids is a mainstay in perioperative pain management. While the leading treatment paradigm has been procedure-specific pain management, efforts regarding personalized pain treatment are increasing. The OPI•AID project aims to develop personalized algorithms for perioperative pain management, taking demographic, surgical, and anaesthesiologic factors into account. We will undertake five parallel reviews to illuminate current evidence on different aspects of individual responses to perioperative opioid treatment. METHODS: Inclusion of adult populations in English-written studies. Review-specific searches are developed for the following databases: CENTRAL, MEDLINE, Embase, clinicaltrials.gov, and clinicaltrial.eu. Two authors will independently screen citations, extract data, and assess the risks of bias in each review (QUIPS, PROBAST and RoB2, as relevant). CONCLUSION: These reviews will evaluate various aspects of perioperative opioid treatment, including individualized treatment strategies, selection of specific opioids, and individual patient responses. These will guide future development of a personalized perioperative opioid treatment algorithm (OPI•AID) that will be validated and tested clinically against standard of care.

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OBJECTIVE: To evaluate the effectiveness of a subanaesthetic single-dose ketamine (SDK) as an adjunct to opioids for acute pain in emergency department (ED) settings. DESIGN: Systematic review and meta-analysis. METHODS: A systematic search was performed in MEDLINE, Embase, Scopus and Web of Science through March 2022. Randomised controlled trials (RCTs) that investigated SDK as an adjunct to opioids in adult patients for any painful condition in ED settings were selected. Two reviewers screened studies, extracted data and assessed study quality. Data were pooled using random-effects models. The primary outcome was mean pain intensity score measured at baseline, >0-15 min, >15-30 min, >30-45 min, 60 min, 90 min and 120 min. Secondary outcomes included need for rescue analgesia, adverse events and patient satisfaction. Results were reported as mean differences (MDs) and risk ratios. Statistical heterogeneity was calculated using the I 2 statistic. RESULTS: Eight RCTs were included (n=903). Studies were judged to be at moderate to high risk of bias. Mean pain intensity scores were significantly lower 60 min after study drug administration favouring adjuvant SDK (MD -0.76; 95% CI -1.19 to -0.33), compared with opioids alone. There was no evidence of differences in mean pain intensity scores at any other time point. Patients who received adjuvant SDK were less likely to require rescue analgesia, no more likely to experience serious side effects and had higher satisfaction scores, compared with opioids alone. CONCLUSIONS: Available evidence suggests adjuvant SDK can have an effect on lowering pain intensity scores. Although reduction of pain scores was not clinically significant, the combination of reduced pain intensity and reduced opioid requirements suggest the results could be clinically important and support the potential utility of SDK as an adjunct to opioids to treat acute pain in adult ED patients. However, current evidence is limited and higher quality RCTs are needed. PROSPERO REGISTRATION NUMBER: CRD42021276708.

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INTRODUCTION: Acute post-operative pain treatment continues to pose a primary therapeutic challenge for clinicians, particularly in opioid-tolerant patients. The purpose of this study was to examine whether patients with a high total daily intake of long-acting opioids received the recommended PRN opioid doses in the first 24 hours after surgery. METHODS: This was a retrospective cohort study using a comprehensive hospital database at the Aarhus University Hospital, Denmark. Patients who received both slow-release long-acting and PRN opioids within the first 24 hours after inpatient surgery were included. Patients were defined as having a high intake if they received slow-release opioids ≥ 60 mg morphine equivalents (MME). RESULTS: In total, 199 patients (14%) received a 24-hour dose of slow-release long-acting opioids ≥ 60 MME and 1,247 patients (86%) received less-than 60 MME. The median age was 64 years (interquartile range: 49-74 years); 54% were female. Patients in the ≥ 60 MME group were less likely to receive the recommended first PRN dose (at least 10% of total 24-hour dose) within the first 24 hours after surgery (79 patients; proportion = 0.40, 95% confidence interval (CI): 0.33-0.47 versus 129 patients; proportion = 0.10, 95% CI: 0.09-0.12, p less-than 0.001). CONCLUSIONS: Our results suggest that patients with a high intake of long-acting opioids may be more likely to receive PRN opioid doses that are not sufficiently adjusted to their total intake during the first 24 hours after inpatient surgery than patients with a lower intake of long-acting opiods. FUNDING: none. TRIAL REGISTRATION: The study was approved by the AUH Ethical Committee and by the Danish Data Protection Agency (ID 1-16-02-341-21).

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ABSTRACT: Chronic postsurgical pain (CPSP) affects an estimated 10% to 50% of adults depending on the type of surgical procedure. Clinical prediction models can help clinicians target preventive strategies towards patients at high risk for CPSP. Therefore, the objective of this systematic review was to identify and describe existing prediction models for CPSP in adults. A systematic search was performed in MEDLINE, Embase, PsychINFO, and the Cochrane Database of Systematic Reviews in March 2020 for English peer-reviewed studies that used data collected between 2000 and 2020. Studies that developed, validated, or updated a prediction model in adult patients who underwent any surgical procedure were included. Two reviewers independently screened titles, abstracts, and full texts for eligibility; extracted data; and assessed risk of bias using the Prediction model Risk of Bias Assessment Tool. The search identified 2037 records; 28 articles were reviewed in full text. Fifteen studies reporting on 19 prediction models were included; all were at high risk of bias. Model discrimination, measured by the area under receiver operating curves or c-statistic, ranged from 0.690 to 0.816. The most common predictors identified in final prediction models included preoperative pain in the surgical area, preoperative pain in other areas, age, sex or gender, and acute postsurgical pain. Clinical prediction models may support prevention and management of CPSP, but existing models are at high risk of bias that affects their reliability to inform practice and generalizability to wider populations. Adherence to standardized guidelines for clinical prediction model development is necessary to derive a prediction model of value to clinicians.

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BACKGROUND: The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada. METHODS: We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020. RESULTS: Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded. CONCLUSIONS: Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

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BACKGROUND: Long-term prescription opioid use has been associated with adverse health outcomes, including opioid use disorder (OUD). We examined a population of opioid naïve individuals who initiated prescription opioids for non-cancer pain and investigated the associations between opioid prescription characteristics at initiation and time to treated OUD. METHODS: We conducted a retrospective population-based cohort study in Ontario, Canada among opioid naïve individuals aged 15 years and older dispensed an opioid for non-cancer pain between 2013 and 2016. We used the Narcotic Monitoring System to abstract opioid dispensing data. A multivariable Cox regression model was used to examine the association between average daily dose and time to treated OUD. RESULTS: We identified 1,607,659 opioid-naïve individuals who initiated a prescription opioid within the study period. The incidence of treated OUD within the study period was 86 cases per 100,000 person-years. Compared to an average daily dose of <20 morphine milligrams equivalent (MME), higher average daily doses at initiation were associated with greater hazard of treated OUD, 20-50 MME (HR 1.11, 95% CI: 1.02, 1.21), >50-90 MME (HR 1.29, 95% CI: 1.16, 1.44), >90-150 MME (HR 1.29, 95% CI: 1.06, 1.56), >150-200 MME (HR 2.49, 95% CI: 1.54, 4.03) and >200 MME (HR 4.15, 95% CI: 2.89, 5.97). Long-acting formulations and days' supply ≥11 days were also associated with greater hazard of treated OUD. CONCLUSION: Prescription opioid characteristics at initiation are associated with risk of treated OUD, identifying potentially important and modifiable risk factors among people initiating opioids for non-cancer pain.

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The rate at which opioids are being prescribed and the safety of prescription opioid use are serious and ongoing problems for individuals, communities and health systems across Canada. In 2011, a coroner's inquest was held in Brockville, Ontario, Canada, to examine the issue of prescription opioid diversion and abuse. Following the inquest, the jury provided 48 recommendations pertaining to prescribing and monitoring of opioids. The ensuing discussion highlights health policy gaps that remain to be addressed seven years after the inquest, in educational resource-sharing, high-dose prescribing, development and use of abuse-resistant formulations and coordination and monitoring of policy interventions.

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Over the past decade, Canada and the United States have been facing an epidemic of harms from prescription opioids. More recently, opioid-naïve individuals have been exposed to illicit opioids through adulterated combination products. This has resulted in sudden surges of opioid-related mortality. A proactive public health solution is needed to prevent further death. We propose examining these surges in opioid overdoses as outbreaks and investigating them in a similar way to an outbreak of an infectious disease. An epidemiologic investigation model for opioid overdose outbreaks, that could be modified by other public health agencies, is discussed.

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Given the steady rise of overdose morbidity and mortality in North America, and increasing frequency of sudden clusters of non-fatal and fatal overdoses in other jurisdictions, regional preparedness plans to respond effectively to clusters of overdoses may reduce the impact of such events on the population. On the 27th of February 2017 in Kingston, Ontario, KFL&A Public Health, in collaboration with public health partners, hosted a full-day workshop involving table-top exercises and discussions for service partners on how to prepare for, respond to, and manage a mass-casualty event secondary to opioid overdose in Southeastern Ontario. The workshop assisted in identifying the various challenges faced by service partners, provided an understanding of the roles and responsibilities of partner agencies, and helped to determine next steps in preparation to address a mass opioid overdose situation at the local level. This report suggests key roles and responsibilities of partners involved in responding to a mass-casualty event secondary to opioid overdose, recommendations to address the feedback and challenges raised throughout the workshop, and a protocol to help determine when to activate an Incident Management System (IMS).