A Machine-Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery.

PURPOSE: To develop a machine-learning algorithm and clinician-friendly tool predicting the likelihood of prolonged opioid use (>90 days) following hip arthroscopy. METHODS: The Military Data Repository was queried for all adult patients undergoing arthroscopic hip surgery between 2012 and 2017. Demographic, health history, and prescription records were extracted for all included patients. Opioid use was divided into preoperative use (30-365 days before surgery), perioperative use (30 days before surgery through 14 days after surgery), postoperative use (14-90 days after surgery), and prolonged postoperative use (90-365 days after surgery). Six machine-learning algorithms (Naïve Bayes, Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Elastic Net Regularization, and artificial neural network) were developed. Area under the receiver operating curve and Brier scores were calculated for each model. Decision curve analysis was applied to assess clinical utility. Local-Interpretable Model-Agnostic Explanations were used to demonstrate factor weights within the selected model. RESULTS: A total of 6,760 patients were included, of whom 2,762 (40.9%) filled at least 1 opioid prescription >90 days after surgery. The artificial neural network model showed superior discrimination and calibration with area under the receiver operating curve = 0.71 (95% confidence interval 0.68-0.74) and Brier score = 0.21 (95% confidence interval 0.20-0.22). Postsurgical opioid use, age, and preoperative opioid use had the most influence on model outcome. Lesser factors included the presence of a psychological comorbidity and strong history of a substance use disorder. CONCLUSIONS: The artificial neural network model shows sufficient validity and discrimination for use in clinical practice. The 5 identified factors (age, preoperative opioid use, postoperative opioid use, presence of a mental health comorbidity, and presence of a preoperative substance use disorder) accurately predict the likelihood of prolonged opioid use following hip arthroscopy. LEVEL OF EVIDENCE: III, retrospective comparative prognostic trial.

Hemodynamic Effects of Cardiovascular Medications in a Normovolemic and Hemorrhaged Yorkshire-cross Swine Model.

The Yorkshire-cross swine model is a valuable translational model commonly used to study cardiovascular physiology and response to insult. Although the effects of vasoactive medications have been well described in healthy swine, the effects of these medications during hemorrhagic shock are less studied. In this study, we sought to expand the utility of the swine model by characterizing the hemodynamic changes that occurred after the administration of commonly available vasoactive medications during euvolemic and hypovolemic states. To this end, we anesthetized and established femoral arterial, central venous, and pulmonary arterial access in 15 juvenile Yorkshire-cross pigs. The pigs then received a series of rapidly metabolized but highly vasoactive medications in a standard dosing sequence. After completion of this sequence, each pig underwent a 30-mL/kg hemorrhage over 10 min, and the standard dosing sequence was repeated. We then used standard sta- tistical techniques to compare the effects of these vasoactive medications on a variety of hemodynamic parameters between the euvolemic and hemorrhagic states. All subjects completed the study protocol. The responses in the hemorrhagic state were often attenuated or even opposite of those in the euvolemic state. For example, phenylephrine decreased the mean arterial blood pressure during the euvolemic state but increased it in the hemorrhagic state. These results clarify previously poorly defined responses to commonly used vasoactive agents during the hemorrhagic state in swine. Our findings also demonstrate the need to consider the complex and dynamic physiologic state of hemorrhage when anticipating the effects of vasoactive drugs and planning study protocols.