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Area of Science:

  • Medical informatics
  • Artificial intelligence in healthcare
  • Pain management and pharmacology

Background:

  • Opioid overprescription post-surgery is a significant public health concern.
  • Accurate prediction of post-discharge opioid consumption is crucial for optimizing pain management and reducing misuse.
  • Existing methods for risk stratification may not fully capture the complexity of opioid use patterns.

Purpose of the Study:

  • To develop and validate a tabular foundation model for predicting the risk of post-discharge opioid consumption in surgical patients.
  • To assess the model's performance in both internal and external validation cohorts.
  • To estimate the potential impact of implementing such a predictive model on global opioid prescribing rates.

Main Methods:

  • A tabular foundation model was trained and validated on a large surgical cohort (n=4267) from the 'Opioid PrEscRiptions and usage After Surgery' study.
  • Internal validation used an 80:20 training/test split, while external validation involved a separate cohort of general surgery patients (n=826).
  • Model performance was evaluated using the area under the receiver operator curve (AUC) and Brier scores.

Main Results:

  • The model achieved an AUC of 0.84 (95% CI 0.81-0.88) in internal testing and 0.77 (95% CI 0.74-0.80) in external validation.
  • Brier scores were 0.13 (95% CI 0.12-0.14) internally and 0.19 (95% CI 0.17-0.2) externally, indicating good predictive accuracy.
  • Patients with <50% predicted risk consumed a median of 0 oral morphine equivalents in the first week post-surgery.
  • Global opioid prescriptions could be reduced by 4.5% without increasing severe pain, according to counterfactual modeling.

Conclusions:

  • A tabular foundation model effectively predicts post-discharge opioid consumption risk in surgical patients.
  • The model demonstrates robust performance across internal and external validation datasets.
  • Implementation of this AI tool holds significant potential for reducing global opioid prescriptions and mitigating opioid-related harms while maintaining adequate pain control.