Panaxea B.V., Amsterdam, the Netherlands.
Department of Clinical Pharmacy and Laboratory Services, Pocahontas, Five Rivers Medical Center, Arkansas.
OMICS. 2019 Oct;23(10):508-515. doi: 10.1089/omi.2019.0113. Epub 2019 Sep 11.
Medical decision-making is revolutionizing with the introduction of artificial intelligence and machine learning. Yet, traditional algorithms using biomarkers to optimize drug treatment continue to be important and necessary. In this context, early diagnosis and rational antimicrobial therapy of sepsis and lower respiratory tract infections (LRTI) are vital to prevent morbidity and mortality. In this study we report an original cost-effectiveness analysis (CEA) of using a procalcitonin (PCT)-based decision algorithm to guide antibiotic prescription for hospitalized sepsis and LRTI patients versus standard care. We conducted a CEA using a decision-tree model before and after the implementation of PCT-guided antibiotic stewardship (ABS) using real-world U.S. hospital-specific data. The CEA included societal and hospital perspectives with the time horizon covering the length of hospital stay. The main outcomes were average total costs per patient, and numbers of patients with and antibiotic resistance (ABR) infections. We found that health care with the PCT decision algorithm for hospitalized sepsis and LRTI patients resulted in shorter length of stay, reduced antibiotic use, fewer mechanical ventilation days, and lower numbers of patients with and ABR infections. The PCT-guided health care resulted in cost savings of $25,611 (49% reduction from standard care) for sepsis and $3630 (23% reduction) for LRTI, on average per patient. In conclusion, the PCT decision algorithm for ABS in sepsis and LRTI might offer cost savings in comparison with standard care in a U.S. hospital context. To the best of our knowledge, this is the first health economic analysis on PCT implementation using U.S. real-world data. We suggest that future CEA studies in other U.S. and worldwide settings are warranted in the current age when PCT and other decision algorithms are increasingly deployed in precision therapeutics and evidence-based medicine.
人工智能和机器学习的引入正在彻底改变医学决策。然而,使用生物标志物来优化药物治疗的传统算法仍然是重要且必要的。在这种情况下,早期诊断和合理的脓毒症和下呼吸道感染(LRTI)的抗菌治疗对于预防发病率和死亡率至关重要。在本研究中,我们报告了一项使用降钙素原(PCT)为基础的决策算法指导住院脓毒症和 LRTI 患者抗生素处方与标准治疗相比的成本效益分析(CEA)的原始结果。我们使用决策树模型在实施 PCT 指导的抗生素管理(ABS)前后进行了 CEA,使用真实世界的美国特定医院数据。CEA 包括社会和医院视角,时间范围涵盖住院时间。主要结果是每位患者的平均总成本和具有 和抗生素耐药(ABR)感染的患者数量。我们发现,对于住院脓毒症和 LRTI 患者,使用 PCT 决策算法进行医疗保健可缩短住院时间,减少抗生素使用,减少机械通气天数,并降低 和 ABR 感染患者的数量。PCT 指导的医疗保健可使脓毒症患者的平均每人节省 25611 美元(比标准护理降低 49%),LRTI 患者节省 3630 美元(降低 23%)。总之,在脓毒症和 LRTI 中,与标准护理相比,PCT 决策算法用于 ABS 可能具有成本效益。据我们所知,这是使用美国真实世界数据进行的关于 PCT 实施的首个健康经济学分析。我们建议,在当前 PCT 和其他决策算法在精准治疗和循证医学中越来越多地被采用的时代,在美国和全球范围内的其他环境中进行未来的 CEA 研究是有必要的。