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術後オピオイド使用量を予測する機械学習モデルの開発：外来手術におけるオピオイドフリー手術を目指して

Savannah Renshaw1, Divyaam Satija1, Abdullah Aly2

  • 1Center for Abdominal Core Health, Department of Surgery, The Ohio State University Wexner Medical Center, Columbus, OH.

Journal of the American College of Surgeons
|January 27, 2026
PubMed
まとめ

機械学習モデルは、術後にオピオイドを必要とする患者を予測し、個別化された疼痛管理を支援します。このオピオイド節約戦略は、術後のオピオイド使用に関連するリスクを軽減します。

キーワード:
手術外来鎮痛薬機械学習オピオイド

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科学分野:

  • 麻酔科学;疼痛管理;ヘルスケアにおける機械学習

背景:

  • 術後のオピオイド使用は、依存および不正使用のリスクをもたらします。;効果的なオピオイド節約戦略の開発は、患者の安全にとって不可欠です。;オピオイド使用のリスクが高い患者を特定することは、個別化された疼痛管理にとって不可欠です。

研究 の 目的:

  • 外来手術におけるオピオイド節約レジメンを開発および評価すること。;術後のオピオイド使用量を予測する機械学習（ML）モデルを作成すること。;術後のオピオイド消費に関連する主な要因を特定すること。

主な方法:

  • イブプロフェンとアセトアミノフェンを交互に使用し、オキシコドンレスキュー用量を限定的に使用するToward Opioid-Free Ambulatory Surgery（TOFAS）プログラムを実施しました。;外来手術を受ける成人患者の術後のオピオイド使用量を予測するMLモデルを開発しました。;80/20の訓練テスト分割と10個のランダムシードを使用して、受信者操作特性曲線（AUC）下の面積でMLモデルを検証しました。

主要な成果:

  • 登録された223人の患者のうち42%がオピオイド処方箋を調剤し、中央値で4回使用されました。;MLモデルは、感度0.70、特異度0.68で、平均テストAUC 0.674を達成しました。;オピオイド使用の主な予測因子には、活動性がん、年齢、麻酔の種類、人種/民族、COPDの既往歴、術中合併症、術前のアセトアミノフェン使用、および疼痛の強さが含まれていました。

結論:

  • 開発されたMLモデルは、術後のオピオイド使用のリスクが高い患者を確実に特定します。;この予測能力は、外来設定における個別化されたオピオイド節約疼痛管理戦略をサポートします。;このモデルは、オピオイド依存および不正使用を潜在的に削減する、個別化された疼痛管理計画を容易にします。