Application of Data Science to Quantify the Effect of Propofol Infusion on Postoperative Nausea and Vomiting.

The effectiveness of propofol infusion on postoperative nausea and vomiting (PONV) is poorly understood in relation to various patient and procedure characteristics. This retrospective cohort study aimed to quantify the effectiveness of propofol infusion when administered either via total intravenous administration (TIVA) or combined intravenous anesthesia (CIVA) with inhalational agents on PONV. The relationship between propofol infusion and PONV was characterized controlling for patient demographics, procedure characteristics, PONV risk factors, and antiemetic drugs in adult patients (age ≥18 years) undergoing general anesthesia. Learned coefficients from multivariate regression models were reported as "lift" which represents the percentage change in the base likelihood of observing PONV if a variable is present versus absent. In a total of 41,490 patients, models showed that propofol infusion has a naive effect on PONV with a lift of -41% (P < .001) when using TIVA and -17% (P < .001) when using CIVA. Adding interaction terms to the model resulted in the loss of statistical significance in these relationships (lift of -30%, P = .23, when using TIVA, and -42%, P = .36, when using CIVA). It was further found that CIVA/TIVA are ineffective in short cases (CIVA * short surgery duration: lift = 49%, P < .001 and TIVA * short surgery duration: lift = 56%, P < .001).

Machine learning approach to predict postoperative opioid requirements in ambulatory surgery patients.

Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years) undergoing ambulatory surgery between the years 2016-2018. The data, comprising of patient, procedure and provider factors that could influence postoperative pain and opioid requirements, was randomly split into training (80%) and validation (20%) datasets. Machine learning models of different classes were developed to predict categorized levels of postoperative opioid requirements using the training dataset and then evaluated on the validation dataset. Prediction accuracy was used to differentiate model performances. The five types of models that were developed returned the following accuracies at two different stages of surgery: 1) Prior to surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 67%, Neural Network: 30%, Random Forest: 72%, Extreme Gradient Boost: 71% and 2) End of surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 63%, Neural Network: 32%, Random Forest: 72%, Extreme Gradient Boost: 70%. Analyzing the sensitivities of the best performing Random Forest model showed that the lower opioid requirements are predicted with better accuracy (89%) as compared with higher opioid requirements (43%). Feature importance (% relative importance) of model predictions showed that the type of procedure (15.4%), medical history (12.9%) and procedure duration (12.0%) were the top three features contributing to model predictions. Overall, the contribution of patient and procedure features towards model predictions were 65% and 35% respectively. Machine learning models could be used to predict postoperative opioid requirements in ambulatory surgery patients and could potentially assist in better management of their postoperative acute pain.