Implementing an AI-enhanced clinical decision support system for Stenotrophomonas maltophilia: a survey-based randomized controlled trial of antibiotic precision and impact on survival
Abstract
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…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 800 participants or records.
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