Association of Obesity With Prescription Opioids for Painful Conditions in Patients Seeking Primary Care in the US

Stokes, Andrew; Lundberg, Dielle J.; Sheridan, Bethany; Hempstead, Katherine; Morone, Natalia E.; Lasser, Karen E.; Trinquart, Ludovic; Neogi, Tuhina

doi:10.1001/jamanetworkopen.2020.2012

Cited by 25 publications

(8 citation statements)

References 52 publications

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“…Having prior obesity among the factors that decrease the chance of OD and prior weight loss among those that increases the chance may seem unexpected. Although many studies suggest that there is an association between obesity and chronic opioid prescription and use through the association of excess weight and chronic pain [ 48 , 49 ], a 2018 study [ 50 ] reveals an inverse relationship between obesity prevalence and opioid dependence, and [ 51 ] characterises weight loss as an alert for substance use problems which seems in line with our findings.…”

Section: Resultssupporting

confidence: 89%

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

et al. 2022

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Background Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Results Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. Conclusions The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk.

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Section: Resultssupporting

confidence: 89%

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

et al. 2022

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“…Each item is scored from 0 to 4 or 0 to 5, with a total score derived from summing all 11 items and a maximum score of 47. Generally, scores are interpreted as mild (between 5 and 12), moderate (between 13 and 24), moderately severe [25][26][27][28][29][30][31][32][33][34][35] and severe [35]. Although the COWS is widely used and considered well-validated and reliable, there is no established minimal clinically important difference (MCID), although some suggest that a COWS score reduction of 15% or greater for an individual is clinically significant [28,29].…”

Section: Methodsmentioning

confidence: 99%

“…Compared to a prior analysis of opioid withdrawal symptoms in a sample of participants that did not appear to be using fentanyl [33], peak SOWS and COWS scores in the present sample were several points higher. Specifically, this earlier analysis of 103 indi- and may relate to increased risks for joint and back pain [35]. Little…”

Section: Analyses Of Bmimentioning

confidence: 99%

Fentanyl withdrawal: Understanding symptom severity and exploring the role of body mass index on withdrawal symptoms and clearance

et al. 2022

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Background and Aims Fentanyl is a highly lipophilic mu opioid receptor agonist, increasingly found in heroin and other drug supplies, that is contributing to marked increases in opioid‐related overdose and may be complicating treatment of opioid use disorder (OUD). This study aimed to measure the influence of body mass index (BMI) on fentanyl withdrawal and clearance. Design, Setting, Participants This secondary analysis, from a 10‐day inpatient study on the safety and efficacy of sublingual dexmedetomidine for opioid withdrawal, includes participants with OUD (n = 150) recruited from three sites in New York, New Jersey and Florida, who were maintained on oral morphine (30 mg four times per day) for 5 days before starting study medication. Most participants (n = 118) tested positive for fentanyl on admission to the inpatient unit. Measurements Urine toxicology and opioid withdrawal symptoms [Clinical Opioid Withdrawal Scale (COWS) and Short Opiate Withdrawal Scale (SOWS)] were assessed daily. The present analysis includes data on opioid withdrawal from days 1–5 of stabilization and urine toxicology data from days 1–10. Findings Fentanyl status at admission was not significantly associated with COWS or SOWS scores after adjusting for sex, site and polysubstance use. Participants classified as overweight or obese (n = 66) had significantly higher odds of testing positive for fentanyl across days 1–10 [odds ratio (OR) = 1.65; P < 0.01] and higher SOWS maximum scores across morphine stabilization (P < 0.05) compared to those with a healthy BMI (n = 68). Conclusions Among inpatients with opioid use disorder, fentanyl status does not appear to be statistically significantly associated with Clinical Opioid Withdrawal Scale and Short Opiate Withdrawal Scale mean and maximum scores. High body mass index status (overweight or obese) appears to be an important predictor of slower fentanyl clearance and higher Short Opiate Withdrawal Scale maximum scores across the inpatient period than lower body mass index status.

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“…Despite this clinical evidence, it remains unclear whether these metabolic effects result from the medications or the lifestyle changes related to being in the program. Alternatively, obesity itself is associated with chronic pain and discomfort, leading to an increased use of opioids and other pain-controlling medications (23,24).…”

Section: Metabolic Syndrome In Adults With Opioid Use Disordermentioning

confidence: 99%

Aberrant Feeding and Growth in Neonates With Prenatal Opioid Exposure: Evidence of Neuromodulation and Behavioral Changes

Yen

Maron

2022

Front. Pediatr.

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Opioid use disorder (OUD) among pregnant women over the last decade has led to more than a fivefold increase in the number of neonates born with withdrawal signs known as Neonatal Abstinence Syndrome (NAS) or Neonatal Opioid Withdrawal Syndrome (NOWS). The impact of prenatal opioid exposure on these neonates remains a public health and research priority due to both its short and long-term effects on offspring. Among the adverse long-term effects associated with OUD is a metabolic syndrome with accompanying cardiovascular comorbidities. The susceptibility to metabolic diseases may begin as early as conception. Neonates born in a setting of prenatal opioid exposure are known to have aberrant early growth, e.g., lower birth weight and smaller head size, and dysregulated feeding behavior that ranges from feeding difficulty to hyperphagia which may predispose these neonates to metabolic syndrome in adulthood. However, studies on this topic are lacking. In this article, we describe the reported association between OUD and metabolic syndrome in adults, animal data linking opioid receptors with the development of diet-induced obesity, the inflammatory modulation of opioids and finally, neonatal salivary transcriptomic data from our laboratory that highlighted the sex-specific impact of opioids on the hypothalamic and reward receptors that regulate feeding behavior in opioid-exposed neonates. There is a great need for future research linking opioids with epigenetic and gene expression changes, as well as neuromodulatory effects in the developing brain, that may underlie the dysregulated feeding, growth, and long-term metabolic and cardiovascular risks for these neonates.

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Association of Obesity With Prescription Opioids for Painful Conditions in Patients Seeking Primary Care in the US

Cited by 25 publications

References 52 publications

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

Using machine learning to study the effect of medication adherence in Opioid Use Disorder

Fentanyl withdrawal: Understanding symptom severity and exploring the role of body mass index on withdrawal symptoms and clearance

Aberrant Feeding and Growth in Neonates With Prenatal Opioid Exposure: Evidence of Neuromodulation and Behavioral Changes

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