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

Alban Zammit, Tina Hernandez-Boussard

Abstract:

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.

Author summary
The opioid epidemic continues in the United States and prescription opioids contribute to opioid-related overdose deaths. The study aimed to develop a machine-learning algorithm to predict the risk of opioid-related adverse outcomes (OR-AE) following surgery using Medicaid data with external validation across seven US states. This retrospective study of 96,974 patients presents seven machine learning models to predict opioid-related adverse outcomes (OAO) following surgery. The best predictive performance was achieved by the random forest model, with an AUC of 0.877. Patients with depression, diabetes, or obesity had higher rates of OR-AE. The model was well calibrated and had good discrimination, with an opioid disorder diagnosis prior to surgery being the most important feature. This work provides evidence that an automated risk prediction can potentially guide postoperative opiate therapy and monitoring.