What is the clinical evidence from this study?

Study design: Observational. Population: Ambulatory surgery (n=13700). Intervention: Machine learning models (Random Forest). Primary outcome: Prediction accuracy of categorized postoperative opioid requirements.

July 31, 2020Open Access

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

Key Result

A Random Forest machine learning model predicted categorized postoperative opioid requirements in ambulatory surgery patients with 72% accuracy using preoperative and intraoperative data.

Study Design

Type

Observational (n=13,700)

Multicenter

Structured PICO

Can machine learning models accurately predict postoperative opioid requirements in ambulatory surgery patients?

Population

13,700 adult patients (≥ 18 years) undergoing ambulatory surgery with general anesthesia between 2016-2018 at a single academic medical center. Mean age 51, 58% female.

Intervention

Machine learning models (Multinomial Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Extreme Gradient Boost) using preoperative and intraoperative features

Outcome

Prediction accuracy of categorized levels of postoperative opioid requirements (None/very low, low, medium, high) in Morphine Milligram Equivalents (MME)

Machine learning models, particularly Random Forest, can predict postoperative opioid requirements with 72% accuracy using preoperative and intraoperative data, potentially aiding in proactive acute pain management.

Limitations

Single center nature of data limits generalizability
Focused only on outpatients, excluding inpatients and those with regional blocks
Potential missing factors influencing opioid requirements not included in the dataset

Abstract

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.

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