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 Clostridium difficile 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 C. difficile 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.

中文翻译：

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使用真实的美国医院数据进行降钙素指导的抗生素管理决策算法的成本效益分析。

随着人工智能和机器学习的引入，医疗决策正在发生革命性变化。然而，使用生物标记物优化药物治疗的传统算法仍然很重要和必要。在这种情况下，败血症和下呼吸道感染（LRTI）的早期诊断和合理的抗菌治疗对于预防发病率和死亡率至关重要。在这项研究中，我们报告了使用基于降钙素（PCT）的决策算法来指导住院败血症和LRTI患者与标准治疗的抗生素处方的原始成本效益分析（CEA）。在使用实际的美国医院特定数据实施PCT指导的抗生素管理（ABS）之前和之后，我们使用决策树模型进行了CEA。CEA涵盖了社会和医院的观点，时间范围涵盖了住院时间。主要结果是每位患者的平均总费用，以及艰难梭菌和抗生素耐药性（ABR）感染的患者人数。我们发现，对于住院的败血症和LRTI患者，采用PCT决策算法进行医疗保健可以缩短住院时间，减少抗生素使用量，减少机械通气天数以及减少艰难梭菌和ABR感染患者的数量。PCT指导的医疗服务平均每位患者可为败血症节省25,611美元（比标准护理减少49％），为LRTI节省3630美元（减少23％）。总之，与美国医院环境中的标准护理相比，脓毒症和LRTI中ABS的PCT决策算法可能会节省成本。据我们所知，这是首次使用美国真实数据对PCT实施进行卫生经济学分析。我们建议，在PCT和其他决策算法越来越多地应用于精密治疗和循证医学的时代，在美国和全球其他地区进行将来的CEA研究是有必要的。