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Objective: To examine the rate of new and persistent opioid use after endocrine surgery operations Summary of Background Data: A global epidemic of opioid misuse and abuse has been evolving over the past 2 decades with opioid use among surgical patients being a particularly difficult problem. Minimal data exists regarding opioid misuse after endocrine surgical operations. Methods: A retrospective cohort study using the MarketScan identified adult patients who underwent thyroidectomy, parathyroidectomy, neck dissections for thyroid malignancy, and adrenalectomy from 2008 to 2017. Persistent opioid use was defined as receipt of !1 opioid prescription 90-180 days postop with no intervening procedures or anesthesia. Multivariable models were used to examine associations between clinical characteristics and any use and new persistent use of opioids. Results: A total of 259,115 patients were identified; 54.6% of opioid naı ¨ve patients received a perioperative opioid prescription. Fulfillment of this prescription was associated with malignant disease, greater extent of surgery, younger age, residence outside of the Northeast, and history of depression or substance abuse. The rate of new persistent opioid use was 7.4%. A lateral neck dissection conferred the highest risk for persistent opioid use (P < 0.01). Persistent opioid use was also associated with older age, Medicaid coverage, residency outside of the Northeast, increased medical co-morbidities, a history of depression, anxiety, substance use disorder, and chronic pain (all P < 0.01). Importantly, the risk for persistent opioid use increased with higher doses of total amount of opioids prescribed. Conclusions: The rate of new, persistent opioid use after endocrine surgery operations is substantial but may be mitigated by decreasing the number of postoperative opioids prescribed.

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Most models for predicting malignant pancreatic intraductal papillary mucinous neoplasms were developed based on logistic regression (LR) analysis. Our study aimed to develop risk prediction models using machine learning (ML) and LR techniques and compare their performances. This was a multinational, multi-institutional, retrospective study. Clinical variables including age, sex, main duct diameter, cyst size, mural nodule, and tumour location were factors considered for model development (MD). After the division into a MD set and a test set (2:1), the best ML and LR models were developed by training with the MD set using a tenfold cross validation. The test area under the receiver operating curves (AUCs) of the two models were calculated using an independent test set. A total of 3,708 patients were included. The stacked ensemble algorithm in the ML model and variable combinations containing all variables in the LR model were the most chosen during 200 repetitions. After 200 repetitions, the mean AUCs of the ML and LR models were comparable (0.725 vs. 0.725). The performances of the ML and LR models were comparable. The LR model was more practical than ML counterpart, because of its convenience in clinical use and simple interpretability.