AI監視ツールが敗血症の予測に成功、命を救う(Study: AI Surveillance Tool Successfully Helps to Predict Sepsis, Saves Lives)

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2024-01-23 カリフォルニア大学サンディエゴ校(UCSD)

◆カリフォルニア大学サンディエゴ校の研究者らは、緊急治療室で人工知能(AI)モデルを使用して敗血症感染のリスクがある患者を素早く特定する試みに成功しました。
◆研究によれば、COMPOSERと呼ばれるAIアルゴリズムが17%の死亡率削減に貢献しました。このアルゴリズムは患者の150以上の変数を監視し、高い敗血症感染リスクを示す場合は看護スタッフに通知します。COMPOSERの導入前後で6,000人以上の患者入院を調査し、AIディープラーニングモデルを使用した初の研究であり、患者アウトカムの改善が報告されました。

<関連情報>

ディープラーニングによる敗血症予測モデルがケアの質と生存に与える影響 Impact of a deep learning sepsis prediction model on quality of care and survival

Aaron Boussina, Supreeth P. Shashikumar, Atul Malhotra, Robert L. Owens, Robert El-Kareh, Christopher A. Longhurst, Kimberly Quintero, Allison Donahue, Theodore C. Chan, Shamim Nemati & Gabriel Wardi
npj Digital Medicine  Published:23 January 2024
DOI:https://doi.org/10.1038/s41746-023-00986-6

AI監視ツールが敗血症の予測に成功、命を救う(Study: AI Surveillance Tool Successfully Helps to Predict Sepsis, Saves Lives)

Abstract

Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%–3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%–8.0%), and a 4% (95% CI, 1.1%–7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.

医療・健康
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