Development of the Revised Opioid Risk Tool to Predict Opioid Use Disorder in Patients with Chronic Nonmalignant Pain

Cheatle, Martin D.; Compton, Peggy; Dhingra, Lara; Wasser, Tom; O’Brien, Charles P.

doi:10.1016/j.jpain.2019.01.011

Cited by 80 publications

(72 citation statements)

References 28 publications

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“…The predominance of patient factors (opioid use, chronic pain and psychological co‐morbidities) as predictors of persistent postoperative opioid use allows early identification of vulnerable individuals. Scoring systems have been used for patients with chronic pain to predict who may be at risk of opioid use disorder [38]. Similar tools may prove useful in the pre‐assessment setting to identify patients at risk, allowing targeted mitigation of modifiable factors, such as interventions to address negative psychology or coping strategies or weaning of pre‐operative opioids.…”

Section: Reducing the Risk: Recommendations And Rationalementioning

confidence: 99%

An international multidisciplinary consensus statement on the prevention of opioid‐related harm in adult surgical patients

et al. 2020

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Summary This international multidisciplinary consensus statement was developed to provide balanced guidance on the safe peri‐operative use of opioids in adults. An international panel of healthcare professionals evaluated the literature relating to postoperative opioid‐related harm, including persistent postoperative opioid use; opioid‐induced ventilatory impairment; non‐medical opioid use; opioid diversion and dependence; and driving under the influence of prescription opioids. Recommended strategies to reduce harm include pre‐operative assessment of the risk of persistent postoperative opioid use; use of an assessment of patient function rather than unidimensional pain scores alone to guide adequacy of analgesia; avoidance of long‐acting (modified‐release and transdermal patches) opioid formulations and combination analgesics; limiting the number of tablets prescribed at discharge; providing deprescribing advice; avoidance of automatic prescription refills; safe disposal of unused medicines; reducing the risk of opioid diversion; and better education of healthcare professionals, patients and carers. This consensus statement provides a framework for better prescribing practices that could help reduce the risk of postoperative opioid‐related harm in adults.

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Section: Reducing the Risk: Recommendations And Rationalementioning

confidence: 99%

An international multidisciplinary consensus statement on the prevention of opioid‐related harm in adult surgical patients

et al. 2020

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“…While several clinical decision support tools are available to predict individual patient risk for opioid‐related harms (eg, death, overdose, abuse), none are specifically designed to predict the probability of progression to LTO following incident opioid exposure. The lack of an appropriate prediction approach is an impediment to the goal of designing service interventions to reduce the number of patients who transition to long‐term use in the absence of a guideline concordant indication.…”

Section: Introductionmentioning

confidence: 99%

Utility of accumulated opioid supply days and individual patient factors in predicting probability of transitioning to long‐term opioid use: An observational study in the Veterans Health Administration

Hadlandsmyth

Mosher

Weg

et al. 2020

Pharmacology Res & Perspec

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Initial supply days dispensed to new users is strongly predictive of future long-term opioid use (LTO). The objective was to examine whether a model integrating additional clinical variables conferred meaningful improvement in predicting LTO, beyond a simple approach using only accumulated supply. Three cohorts were created using Veteran's Health Administration data based on accumulated supply days during the 90 days following opioid initiation: (a) <30 days, (b) ≥30 days, (c) ≥60 days. A base, unadjusted probability of subsequent LTO (days 91-365) was calculated for each cohort, along with an associated risk range based on midpoint values between cohorts. Within each cohort, log-binomial regression modeled the probability of subsequent LTO, using demographic, diagnostic, and medication characteristics. Each patient's LTO probability was determined using their individual characteristic values and model parameter estimates, where values falling outside the cohort's risk range were considered a clinically meaningful change in predictive value. Base probabilities for subsequent LTO and associated risk ranges by cohort were as follows: (a) 3.92% (0%-10.75%), (b) 17.59% (10.76%-28.05%), (c) 38.53% (28.06%-47.55%). The proportion of patients whose individual probability fell outside their cohort's risk range was as follows: 1.5%, 4.6%, and 9.2% for cohorts 1, 2, and 3, respectively. The strong relationship between accumulated supply days and future LTO offers an opportunity to leverage electronic healthcare records for decision support in preventing the initiation of inappropriate LTO through early intervention. More complex models are unlikely to meaningfully guide decision making beyond the single variable of accumulated supply days.

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“…In specialized clinics in the non-cancer setting, the routine assessment of pain and addiction patients is common (Manchikanti and Singh, 2008; Gilson and Kreis, 2009). Assessments used in these non-cancer clinics include the patient self-administered SOAPP (patient-administered; sensitivity, 91%; specificity, 69%), the ORT (patient-administered), the CAGE-AID questionnaire (clinician- or patient-administered; 93% sensitivity; 76% specificity), and the Diagnosis, Intractability, Risk, and Efficacy (DIRE) inventory (clinician-administered; 94% sensitivity; 87% specificity; Ewing, 1984; Akbik et al, 2006; Belgrade et al, 2006; Kim et al, 2016; Cheatle et al, 2019). Tools for risk for NMOU assessment in patients already on long-term opioid therapy include the Current Opioid Misuse Measure (patient-administered; sensitivity, 77%; specificity, 68%; Butler et al, 2007), the Pain Medication Questionnaire (patient-administered; sensitivity, 92%; specificity, 80%; Adams et al, 2004), and the Addiction Behavior Checklist (clinician-administered; sensitivity, 88%; specificity, 86%; Wu et al, 2006).…”

Section: Discussionmentioning

confidence: 99%

The development of a nomogram to determine the frequency of elevated risk for non-medical opioid use in cancer patients

et al. 2020

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Objective Non-medical opioid use (NMOU) is a growing crisis. Cancer patients at elevated risk of NMOU (+risk) are frequently underdiagnosed. The aim of this paper was to develop a nomogram to predict the probability of +risk among cancer patients receiving outpatient supportive care consultation at a comprehensive cancer center. Method 3,588 consecutive patients referred to a supportive care clinic were reviewed. All patients had a diagnosis of cancer and were on opioids for pain. All patients were assessed using the Edmonton Symptom Assessment Scale (ESAS), Screener and Opioid Assessment for Patients with Pain (SOAPP-14), and CAGE-AID (Cut Down-Annoyed-Guilty-Eye Opener) questionnaires. “+risk” was defined as an SOAPP-14 score of ≥7. A nomogram was devised based on the risk factors determined by the multivariate logistic regression model to estimate the probability of +risk. Results 731/3,588 consults were +risk. +risk was significantly associated with gender, race, marital status, smoking status, depression, anxiety, financial distress, MEDD (morphine equivalent daily dose), and CAGE-AID score. The C-index was 0.8. A nomogram was developed and can be accessed at https://is.gd/soappnomogram. For example, for a male Hispanic patient, married, never smoked, with ESAS scores for depression = 3, anxiety = 3, financial distress = 7, a CAGE score of 0, and an MEDD score of 20, the total score is 9 + 9+0 + 0+6 + 10 + 23 + 0+1 = 58. A nomogram score of 58 indicates the probability of +risk of 0.1. Significance of results We established a practical nomogram to assess the +risk. The application of a nomogram based on routinely collected clinical data can help clinicians establish patients with +risk and positively impact care planning.

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Development of the Revised Opioid Risk Tool to Predict Opioid Use Disorder in Patients with Chronic Nonmalignant Pain

Cited by 80 publications

References 28 publications

An international multidisciplinary consensus statement on the prevention of opioid‐related harm in adult surgical patients

An international multidisciplinary consensus statement on the prevention of opioid‐related harm in adult surgical patients

Utility of accumulated opioid supply days and individual patient factors in predicting probability of transitioning to long‐term opioid use: An observational study in the Veterans Health Administration

The development of a nomogram to determine the frequency of elevated risk for non-medical opioid use in cancer patients

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