Individualized Risk Assessment of Preoperative Opioid Use by Interpretable Neural Network Regression

• Activity Number: 168 - Risk analysis and related topics
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Risk Analysis
• Author(s): Yuming Sun* and Jian Kang and Chad Brummet and Yi Li
• Companies: Department of Biostatistics, University of Michigan, Ann Arbor and University of Michigan and Department of Anesthesiology, University of Michigan, Ann Arbor and University of Michigan
• Keywords: Deep neural network; Interpretable neural network regression; Preoperative opioid use

Abstract

As preoperative opioid use is associated with worse postoperative outcomes and increased postoperative healthcare utilization and expenditures, understanding the risk of preoperative opioid use helps establish effective opioid management for each patient. We propose a novel Interpretable Neural Network Regression (INNER), which combines the strengths of statistical and DNN models. We use INNER to conduct individualized risk assessment of preoperative opioid use. Intensive simulations and statistical analysis of 34,186 patients expecting surgery in the Analgesic Outcomes Study (AOS) show that, the proposed INNER accurately predicts the preoperative opioid use based on preoperative characteristics. It also estimates the patient-specific odds of opioid use without pain and the odds ratio of opioid use for one unit increase in the reported overall body pain, leading to more straightforward interpretations on opioid tendency compared to DNN. Our analysis identifies patient characteristics associated with the opioid tendency and is largely consistent with the previous findings, evidencing that INNER is a useful tool for individualized risk assessment of preoperative opioid use.