Prediction of Future Chronic Opioid Use Among Hospitalized Patients

Calcaterra, Susan L.; Scarbro, Sharon; Hull, Madelyne; Forber, Alyssa; Binswanger, Ingrid A.; Colborn, Kathryn

doi:10.1007/s11606-018-4335-8

Cited by 47 publications

(80 citation statements)

References 49 publications

(57 reference statements)

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“…Other significant predictors of long‐term opioid prescribing include longer cancer survivorship, later years of diagnosis, female sex, urban residence, lung cancer diagnosis, disability as the original reason for Medicare enrollment, Medicaid eligibility, and one or more comorbidities. Similar to prior studies, a history of depression and drug abuse also predicted prolonged opioid prescribing . Cancer diagnosis at an older age, Hispanic ethnicity, and being opioid naïve strongly predicted lower odds of opioid prescribing among older cancer survivors.…”

Section: Discussionsupporting

confidence: 74%

“…Similar to prior studies, a history of depression and drug abuse also predicted prolonged opioid prescribing. [27][28][29] Cancer diagnosis at an older age, Hispanic ethnicity, and being opioid naïve strongly predicted lower odds of opioid prescribing among older cancer survivors. Among opioidnaïve cancer survivors, diagnosis from 2004 to 2008 was the strongest predictor of long-term opioid use, while the strongest predictor for all cancer survivors was a history of drug abuse.…”

Section: Discussionmentioning

confidence: 99%

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Long‐Term Opioid Therapy in Older Cancer Survivors: A Retrospective Cohort Study

Shah

Chou

Kuo

et al. 2019

J American Geriatrics Society

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OBJECTIVES To examine the rates and predictors of long‐term opioid therapy in older cancer survivors. DESIGN Retrospective cohort study. SETTING Texas, United States. PARTICIPANTS Cancer survivors (5 years or more postcancer diagnosis) diagnosed from 1995 to 2008 and who were also Medicare Parts A, B, and D beneficiaries. MEASUREMENTS We used Medicare Part D event data to calculate the proportion of cancer survivors with a prolonged opioid prescription (90‐day or more supply of opioids/year). Adjusted odds ratios were calculated to identify predictors of prolonged opioid prescribing. All analyses were repeated with a subcohort of opioid‐naïve cancer survivors. RESULTS The rate of prolonged opioid therapy for cancer patients diagnosed in 2008 was 7.1% prior to cancer diagnosis; it rose to 9.8% within a year of cancer treatments, and to 13.3% at 5 years postdiagnosis. The rate at the sixth year varied by cancer sites: 19.4% in lung cancer and 9.6% in prostate cancer. Among opioid‐naïve survivors, the rate increased from 1.4% to 7.1%, from 5 to 18 years postcancer diagnosis. Cancer survivors diagnosed in 2004 to 2008 had higher rates of opioid prescribing compared to those diagnosed in 1995 to 1998 and 1999 to 2003. Years since diagnosis, a later year of diagnosis, female sex, urban location, lung cancer diagnosis, disability as reason for Medicare entitlement, Medicaid eligibility, one or more comorbidity, and history of depression or drug abuse were predictors of prolonged opioid therapy. Among opioid‐naïve cancer survivors, diagnosis in 2004 to 2008 was the strongest predictor, while a history of drug abuse was the strongest predictor for all the survivors. CONCLUSION The rates of prolonged opioid prescribing for older cancer survivors remained high at 5 or more years after cancer diagnosis. Our findings have potential to inform the development of clinical guidelines and public policy to ensure safer and more effective pain treatment in older cancer survivors. J Am Geriatr Soc 67:945–952, 2019.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Long‐Term Opioid Therapy in Older Cancer Survivors: A Retrospective Cohort Study

Shah

Chou

Kuo

et al. 2019

J American Geriatrics Society

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“…Multiple demographic and clinical factors were associated with increased risk of chronic opioid use after hysterectomy, including older age, abdominal or laparoscopic/robotic hysterectomy, comorbidities, tobacco use, substance use disorders, certain pain conditions, and use of prescription psychiatric medication use (Table ). Our findings were consistent with other studies which showed that these factors increase the risk of chronic opioid use after major cardiac, thoracic, abdominal and pelvic procedures, or specific surgeries, including cesarean delivery, hysterectomy, hip or knee arthroplasty, spine, or bariatric surgeries . These findings underscore the importance of considering the individual patient's pain management needs; risk factors for opioid misuse at the time of opioid prescribing after hysterectomy and other surgical procedures; and importance of medical care, monitoring, and follow up postoperatively.…”

Section: Discussionsupporting

confidence: 91%

“…For many women, their first exposure to prescription opioids often occurs during the postoperative period, which makes this a potential target for strategies to reduce the risk of chronic opioid use . Several observational studies suggest that surgery is a risk factor for chronic opioid use . Two studies have examined the relationship between the initial opioid prescribing characteristics and chronic opioid use in the postoperative setting and arrived at contradictory conclusions .…”

Section: Introductionmentioning

confidence: 99%

Chronic opioid use in women following hysterectomy: Patterns and predictors

Wen

Kogut

Aroke

et al. 2020

Pharmacoepidemiology and Drug

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Background: Most women are prescribed an opioid after hysterectomy. The goal of this study was to determine the association between initial opioid prescribing characteristics and chronic opioid use after hysterectomy.Methods: This study included women enrolled in a commercial health plan who had a hysterectomy between 1 July 2010 and 31 March 2015. We used trajectory models to define chronic opioid use as patients with the highest probability of having an opioid prescription filled during the 6 months post-surgery. A multivariable logistic regression was applied to examine the association between initial opioid dispensing (amount prescribed and duration of treatment) and chronic opioid use after adjusting for potential confounders.Results: A total of 693 of 50 127 (1.38%) opioid-naïve women met the criteria for chronic opioid use following hysterectomy. The baseline variables and initial opioid prescription characteristics predicted the pattern of long-term opioid use with moderate discrimination (c statistic = 0.70). Significant predictors of chronic opioid use included initial opioid daily dose (≥60 MME vs <40 MME, aOR: 1.43, 95% CI:

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“…Log‐binomial regression was used to model risk for subsequent LTO with an array of independent variables including sociodemographic characteristics, medical diagnoses, and prescription medications that are potentially associated with long‐term use and commonly query‐able within electronic medical records . Diagnoses were identified by ICD‐9 and ICD‐10 codes from outpatient encounters during the year prior to opioid initiation.…”

Section: Methodsmentioning

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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Prediction of Future Chronic Opioid Use Among Hospitalized Patients

Cited by 47 publications

References 49 publications

Long‐Term Opioid Therapy in Older Cancer Survivors: A Retrospective Cohort Study

Long‐Term Opioid Therapy in Older Cancer Survivors: A Retrospective Cohort Study

Chronic opioid use in women following hysterectomy: Patterns and predictors

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

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