Association of diabetes mellitus with postoperative opioid consumption in patients undergoing primary unilateral hip arthroplasty

Regnier, S.; Khatibi, Bahareh; Gabriel, Rodney A.

doi:10.1177/1750458920903647

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…First, these populations have increased healthcare needs and are at higher risk of adverse results after surgery that might require opiates [22][23][24][25]. Second, many of these patients are taking medications or have metabolic derangements that can complicate perioperative pain management [26,27]. This medical complexity could impact their pain management and opioid risk.…”

Section: Discussionmentioning

confidence: 99%

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring

El Hajouji,

Sun,

Zammit

et al. 2023

PLoS Comput Biol

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Background Treatment of surgical pain is a common reason for opioid prescriptions. Being able to predict which patients are at risk for opioid abuse, dependence, and overdose (opioid-related adverse outcomes [OR-AE]) could help physicians make safer prescription decisions. We aimed to develop a machine-learning algorithm to predict the risk of OR-AE following surgery using Medicaid data with external validation across states. Methods Five machine learning models were developed and validated across seven US states (90–10 data split). The model output was the risk of OR-AE 6-months following surgery. The models were evaluated using standard metrics and area under the receiver operating characteristic curve (AUC) was used for model comparison. We assessed calibration for the top performing model and generated bootstrap estimations for standard deviations. Decision curves were generated for the top-performing model and logistic regression. Results We evaluated 96,974 surgical patients aged 15 and 64. During the 6-month period following surgery, 10,464 (10.8%) patients had an OR-AE. Outcome rates were significantly higher for patients with depression (17.5%), diabetes (13.1%) or obesity (11.1%). The random forest model achieved the best predictive performance (AUC: 0.877; F1-score: 0.57; recall: 0.69; precision:0.48). An opioid disorder diagnosis prior to surgery was the most important feature for the model, which was well calibrated and had good discrimination. Conclusions A machine learning models to predict risk of OR-AE following surgery performed well in external validation. This work could be used to assist pain management following surgery for Medicaid beneficiaries and supports a precision medicine approach to opioid prescribing.

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Section: Discussionmentioning

confidence: 99%

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring

El Hajouji,

Sun,

Zammit

et al. 2023

PLoS Comput Biol

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“…In a retrospective cohort of 583 patients undergoing elective cervical or lumbar spine surgery, diabetes was a significant preoperative risk factor for prolonged opioid use (POU) [10]. However, the analgesic effects of morphine for postoperative pain among patients with diabetes remains unclear [11][12][13][14]. Effective postoperative opioid prescribing for patients with diabetes is understudied at the population level leaving a gap in evidence and clinical guidelines.…”

Section: Introductionmentioning

confidence: 99%

Postoperative opioid prescribing patients with diabetes: Opportunities for personalized pain management

Zammit,

Coquet,

Hah

et al. 2023

PLoS ONE

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Background Opioids are commonly prescribed for postoperative pain, but may lead to prolonged use and addiction. Diabetes impairs nerve function, complicates pain management, and makes opioid prescribing particularly challenging. Methods This retrospective observational study included a cohort of postoperative patients from a multisite academic health system to assess the relationship between diabetes, pain, and prolonged opioid use (POU), 2008–2019. POU was defined as a new opioid prescription 3–6 months after discharge. The odds that a patient had POU was assessed using multivariate logistic regression controlling for patient factors (e.g., demographic and clinical factors, as well as prior pain and opiate use). Findings A total of 43,654 patients were included, 12.4% with diabetes. Patients with diabetes had higher preoperative pain scores (2.1 vs 1.9, p<0.001) and lower opioid naïve rates (58.7% vs 68.6%, p<0.001). Following surgery, patients with diabetes had higher rates of POU (17.7% vs 12.7%, p<0.001) despite receiving similar opioid prescriptions at discharge. Patients with Type I diabetes were more likely to have POU compared to other patients (Odds Ratio [OR]: 2.22; 95% Confidence Interval [CI]:1.69–2.90 and OR:1.44, CI: 1.33–1.56, respectively). Interpretation In conclusion, surgical patients with diabetes are at increased risk for POU even after controlling for likely covariates, yet they receive similar postoperative opiate therapy. The results suggest a more tailored approach to diabetic postoperative pain management is warranted.

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Association of diabetes mellitus with postoperative opioid consumption in patients undergoing primary unilateral hip arthroplasty

Cited by 2 publications

References 18 publications

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring

Prediction of opioid-related outcomes in a medicaid surgical population: Evidence to guide postoperative opiate therapy and monitoring

Postoperative opioid prescribing patients with diabetes: Opportunities for personalized pain management

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