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Background <p>The World Health Organization has identified <i>Stenotrophomonas maltophilia</i> (SM) as a high-risk antibiotic-resistant pathogen. Notably, determining the effectiveness of current antibiotics against SM is challenging, leading to improper therapy and the spread of resistance. This study assessed how an artificial intelligence-clinical decision support system (AI-CDSS) utilizing mass spectrometry data to predict resistance enhances prescribing decisions and boosts survival.</p> Methods <p>This randomized controlled trial (ISRCTN16278872) involved 400 healthcare professionals, with 1,600 SM infections randomized in a 1:1 ratio to either standard practice (control, n = 800) or an AI-CDSS predicting resistance 1&#xa0;day earlier (intervention, n = 800). Outcomes were assessed by healthcare professionals using structured surveys on days 3, 5, 7, and 14 after treatment initiation. Patient mortality was analyzed over a 14-day follow-up period.</p> Results <p>The AI-CDSS group demonstrated significantly higher confidence (<i>p</i> &lt; 0.001) in antibiotic prescription, decision-making efficiency, and appropriate antibiotic selection across all time points. Mortality was lower in the AI-CDSS group (92/800, 11.5%) than in the control group (121/800, 15.1%) (<i>p</i> = 0.03). Effective antibiotic choices and reliance on the AI-CDSS during the critical early stages of treatment contributed to improved patient outcomes.</p> Conclusions <p>Implementation of the AI-CDSS in a clinical trial setting enhances prescribing confidence, improves decision-making and antibiotic selection, reduces mortality, and demonstrates clinical potential.</p> Trial registration <p>ISRCTN, ISRCTN16278872. Registered 28 June 2024, <a href="https://www.isrctn.com/ISRCTN16278872">https://www.isrctn.com/ISRCTN16278872</a>.</p>

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Implementing an AI-enhanced clinical decision support system for Stenotrophomonas maltophilia: a survey-based randomized controlled trial of antibiotic precision and impact on survival

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Tai-Han Lin,
  • Hsing-Yi Chung,
  • Ming-Jr Jian,
  • Chih-Kai Chang,
  • Cherng-Lih Perng,
  • Feng-Yee Chang,
  • Yuan-Hao Chen,
  • Hung-Sheng Shang

摘要

Background

The World Health Organization has identified Stenotrophomonas maltophilia (SM) as a high-risk antibiotic-resistant pathogen. Notably, determining the effectiveness of current antibiotics against SM is challenging, leading to improper therapy and the spread of resistance. This study assessed how an artificial intelligence-clinical decision support system (AI-CDSS) utilizing mass spectrometry data to predict resistance enhances prescribing decisions and boosts survival.

Methods

This randomized controlled trial (ISRCTN16278872) involved 400 healthcare professionals, with 1,600 SM infections randomized in a 1:1 ratio to either standard practice (control, n = 800) or an AI-CDSS predicting resistance 1 day earlier (intervention, n = 800). Outcomes were assessed by healthcare professionals using structured surveys on days 3, 5, 7, and 14 after treatment initiation. Patient mortality was analyzed over a 14-day follow-up period.

Results

The AI-CDSS group demonstrated significantly higher confidence (p < 0.001) in antibiotic prescription, decision-making efficiency, and appropriate antibiotic selection across all time points. Mortality was lower in the AI-CDSS group (92/800, 11.5%) than in the control group (121/800, 15.1%) (p = 0.03). Effective antibiotic choices and reliance on the AI-CDSS during the critical early stages of treatment contributed to improved patient outcomes.

Conclusions

Implementation of the AI-CDSS in a clinical trial setting enhances prescribing confidence, improves decision-making and antibiotic selection, reduces mortality, and demonstrates clinical potential.

Trial registration

ISRCTN, ISRCTN16278872. Registered 28 June 2024, https://www.isrctn.com/ISRCTN16278872.