Cost-Effectiveness Analysis of a Procalcitonin-Guided Decision Algorithm for Antibiotic Stewardship Using Real-World U.S. Hospital Data

Publications 4 Oct 2019 Panaxea |

Abstract

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.

Introduction

Sepsis and lower respiratory tract infections (LRTI) cause morbidity and mortality among hospitalized patients (Dellinger et al., 2013). Early diagnosis and appropriate antimicrobial therapy are vital in treatment of these patients (Carlet, 1999). However, overprescribing antibiotics can contribute to antibiotic resistance (ABR) and Clostridium difficile infections (CDI) (Schuetz et al., 2011; Wenzel and Edmond, 2000).

Guidance on when to initiate or terminate antibiotic therapy could aid reducing the overuse of antibiotics, and thereby reduce ABR and the number of CDI patients. Procalcitonin (PCT) is a biomarker that is able to provide guidance in clinical decision-making on antibiotic usage (Schuetz et al., 2015). PCT can distinguish bacterial from nonbacterial infections even in early stages of inflammation with good specificity (Póvoa and Salluh, 2012). Typically, within 3–4 h after onset of an inflammatory response PCT is elevated, after which it peaks at 14–25 h. With a half-life of ∼24 h, PCT decreases rapidly when the inflammatory response begins to resolve (Linscheid et al., 2003; Müller et al., 2001). PCT values can thus support clinical decision-making on antibiotic initiation and discontinuation.

PCT-guided antibiotic stewardship (ABS) was found to be safe and contribute to reducing the use of antibiotics (Albrich et al., 2012). However, PCT implementation comes at additional costs for the additional blood tests. Cost-effectiveness analyses (CEAs) for hospitalized sepsis and LRTI patients have shown that net savings in downstream costs offset the increased PCT testing costs (Harrison and Collins, 2015; Heyland et al., 2011; Kip et al., 2015; Mewes et al., 2019), while decreasing antibiotic resource utilization. The earlier CEAs were performed based on both European and U.S. data (Kip et al., 2015; Mewes et al., 2019).

To further support the adoption and uptake of PCT testing in the United States, there is a need to quantify the added value of PCT testing in a U.S. hospital setting in terms of cost-effectiveness. Therefore, this study reports on real-world data of a U.S. hospital to populate a previously published decision-tree model (Mewes et al., 2019) and performs a model-based analysis of the cost-effectiveness of a PCT algorithm versus standard care to guide antibiotic prescription for hospitalized sepsis and LRTI patients in a U.S. hospital setting.

Materials and Methods

A previously published decision-tree model (Kip et al., 2015; Mewes et al., 2019) (Fig. 1) was populated with real-world U.S. hospital data. PCT-guided antibiotic use was compared with standard care for sepsis and LRTI patients. In PCT-guided care, an algorithm was used to guide the decision on initiation (LRTI) and discontinuation of (both sepsis and LRTI) antibiotic therapy. Standard care included all usual care, except for the PCT algorithm on antibiotic initiation or discontinuation.

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