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This summary is machine-generated.

Machine learning models show promise in predicting prolonged postoperative opioid use. These algorithms, particularly decision-tree-based ones, can aid clinicians in identifying at-risk patients.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Public Health

Background:

  • The US faces a severe opioid crisis, necessitating tools to predict prolonged postoperative opioid (PPO) use.
  • Machine learning (ML) offers potential solutions for identifying patients at risk of PPO.

Approach:

  • A systematic review of studies using ML to predict PPO in adult surgical patients was conducted.
  • Databases searched included PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science.
  • Fifteen studies meeting inclusion criteria were analyzed.

Key Points:

  • Decision-tree-based boosting algorithms and logistic regression were common ML models used.
  • Preoperative opioid use, depression, antidepressant use, and patient age were key predictors.
  • Models achieved AUCs ranging from 0.66 to 0.81.

Conclusions:

  • ML algorithms show potential as decision-support tools for predicting PPO.
  • Further validation is needed for clinical integration.
  • These tools could help mitigate the impact of the opioid pandemic.