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Background  Procalcitonin is a biomarker that supports clinical decision-making on when to initiate and discontinue antibiotic therapy. Several cost (-effectiveness) analyses have been conducted on Procalcitonin-guided antibiotic stewardship, but none mainly based on US originated data.    Objective  To compare effectiveness and costs of a Procalcitonin-algorithm versus standard care to guide antibiotic prescription for patients hospitalized with a diagnosis of suspected sepsis or lower respiratory tract infection in the US.    Methods  A previously published health economic decision model was used to compare the costs and effects of Procalcitonin-guided care. The analysis considered the societal and hospital perspective with a time horizon covering the length of hospital stay. The main outcomes were total costs per patient, including treatment costs and productivity losses, the number of patients with antibiotic resistance or  C . difficile  infections, and costs per antibiotic day avoided.    Results  Procalcitonin -guided care for hospitalized patients with suspected sepsis and lower respiratory tract infection is associated with a reduction in antibiotic days, a shorter length of stay on the regular ward and the intensive care unit, shorter duration of mechanical ventilation, and fewer patients at risk for antibiotic resistant or  C . difficile  infection. Total costs in the Procalcitonin-group compared to standard care were reduced by 26.0% in sepsis and 17.7% in lower respiratory tract infection (total incremental costs of −$11,311 per patient and −$2,867 per patient respectively).    Conclusions  Using a Procalcitonin-algorithm to guide antibiotic use in sepsis and hospitalised lower respiratory tract infection patients is expected to generate cost-savings to the hospital and lower rates of antibiotic resistance and  C . difficile  infections.

plos one

The cost impact of PCT-guided antibiotic stewardship versus usual care for hospitalised patients with suspected sepsis or lower respiratory tract infections in the US: A health economic model analysis