Gomez de la Torre Juan C, Frenkel Ari, Chavez-Lencinas Carlos, Rendon Alicia, Cáceres José Alonso, Alvarado Luis, Hueda-Zavaleta Miguel
Clinical Laboratory Roe, Lima 15076, Peru.
Arkstone Medical Solutions, Boca Raton, FL 33428, USA.
Life (Basel). 2025 May 27;15(6):864. doi: 10.3390/life15060864.
Bloodstream infections continue to pose a serious global health threat due to their high morbidity and mortality, further worsened by rising antimicrobial resistance and delays in starting targeted therapy. This study assesses the accuracy and timeliness of therapeutic recommendations produced by an artificial intelligence (AI)-driven and machine-learning (ML) clinical decision support system (CDSS), comparing results based on molecular diagnostics alone with those that combine molecular and phenotypic data (standard cultures).
In a prospective cross-sectional study conducted in Lima, Peru, 117 blood cultures were analyzed using FilmArray/GeneXpert for molecular identification and MALDI-TOF/VITEK 2.0 for phenotypic profiling. The AI/ML-based CDSS provided treatment recommendations in two formats, which were assessed for concordance and turnaround time.
Therapeutic recommendations showed 80.3% consistency between data types, with 86.3% concordance in pathogen and resistance detection. Notably, molecular-only recommendations were delivered 29 h earlier than those incorporating phenotypic data. Escherichia coli was the most frequently isolated pathogen, with a 95% concordance in suggested therapy. A substantial agreement was observed in treatment consistency (Kappa = 0.80).
These findings highlight the potential of using AI-powered CDSS in conjunction with molecular diagnostics to accelerate clinical decision-making in bacteremia, supporting more timely interventions and improved antimicrobial stewardship. Further research is warranted to assess scalability and impact across diverse clinical settings.
血流感染因其高发病率和死亡率，持续对全球健康构成严重威胁，而日益增加的抗菌药物耐药性和启动靶向治疗的延迟使情况进一步恶化。本研究评估了由人工智能（AI）驱动和机器学习（ML）的临床决策支持系统（CDSS）所产生的治疗建议的准确性和及时性，比较了仅基于分子诊断的结果与结合分子和表型数据（标准培养）的结果。
在秘鲁利马进行的一项前瞻性横断面研究中，使用FilmArray/GeneXpert进行分子鉴定，使用基质辅助激光解吸电离飞行时间质谱/MicroScan WalkAway 进行表型分析，对117份血培养进行了分析。基于AI/ML的CDSS以两种格式提供治疗建议，并对其一致性和周转时间进行了评估。
治疗建议在数据类型之间显示出80.3%的一致性，在病原体和耐药性检测方面的一致性为86.3%。值得注意的是，仅基于分子的建议比纳入表型数据的建议提前29小时提供。大肠杆菌是最常分离出的病原体，在建议治疗方面的一致性为95%。在治疗一致性方面观察到高度一致性（Kappa = 0.80）。
这些发现突出了结合使用由人工智能驱动的CDSS和分子诊断来加速菌血症临床决策的潜力，支持更及时的干预措施和改善抗菌药物管理。有必要进行进一步研究以评估其在不同临床环境中的可扩展性和影响。
