Procalcitonin and Lung Ultrasonography Guided Antibiotherapy in Emergency Departments

Overview

Acute respiratory infections are a common reason of attendance at emergency departments. It is also the main reason of unnecessary antibiotic prescription. Antibiotics save lives, but can also directly harm patients by causing antibiotic-associated adverse events. Antibiotic use is directly related to resistance, which is one of the major threats of our century. In addition, some microorganisms live in and on the human body and promote many aspects of our health. Antibiotic treatment can disturb those microorganisms and therefore have long-lasting negative effects on our health. Unfortunately, it is difficult to differentiate between viral infections, which usually heal spontaneously, and bacterial pneumonia, which needs antibiotics treatment. This is one of the reasons of this over-prescribing of antibiotics. This project aims to reduce widespread use of antibiotics in the emergency department through a new diagnostic strategy of bacterial pneumonia. This strategy includes sequential use of well-known techniques: a clinical score, lung ultrasound and finally a biomarker, procalcitonin. The latter tends to be higher in bacterial infections. The combination of these different tests improves the diagnostic process and allows improved use of targeted antibiotics, with the ultimate goal of better patient management. The study will compare the antibiotic prescription rate and the clinical course of patients managed using this new diagnostic approach with those managed as usual. The project will also evaluate the acceptability and feasibility of this strategy and its cost-effectiveness. These two aspects are essential for a wider implementation of this innovative diagnostic approach and decrease antibiotic resistance.

Background Community-acquired lower respiratory tract infections (LRTI) are one of the most common motivations for emergency department (ED) consultations and stands as the leading cause of inappropriate antibiotic prescription. Besides the side effects, antibiotic overuse alters the microbiome and generates antibiotic resistance. When assessing patients with LRTIs, the challenge for ED physicians is to identify those with community-acquired pneumonia (CAP) of bacterial origin, who will most likely benefit from antibiotics. The low diagnostic accuracy of existing tools, as well as the poor adherence of clinicians to test guidance are leading causes of inappropriate antibiotic use. Several diagnostic tests can assist in identifying patients with LRTI who require antibiotics. Clinical prediction score can refine the probability of CAP. Lung ultrasound (LUS) has a better diagnostic performance than chest X-ray, the historic reference imaging modality to consolidation in ED. LUS is performed quickly at the bedside without radiation. Procalcitonin (PCT) is a host inflammatory biomarker which tends to be higher in bacterial infections. PCT can be used safely to guide antibiotics use, while its impact on prescription is controversial. None of these tools on its own is sufficient to optimize antibiotic prescription, while a combined approach could better guide clinicians. Rationale The investigators propose to evaluate the use of a decision support tool to guide antibiotics use in the ED as the summative value of LUS with PCT remains unknown in this setting. Pragmatic stepped-wedge cluster-randomized controlled clinical trial investigating a new algorithm combining a clinical score, LUS and PCT results (The PLUS algorithm) for the management of LRTIs among adults in EDs. The unit of randomization will be the ED. Primary safety objective To demonstrate non-inferiority of the intervention in terms of clinical failure by day 28. Co-primary efficacy objective To show a 15% reduction in the proportion of patients with LRTIs prescribed an antibiotic by day 28 in the intervention group compared with the usual care group. Secondary objectives 1. To compare the quality of life (bothersomeness of CAP-related symptoms) on day 7, day 28 and day 90 between patients in the intervention and control groups. 2. To evaluate the acceptability and feasibility of the intervention through the identification of barriers and facilitators in patients and physicians. 3. To assess the incremental cost-effectiveness of the intervention as compared to usual care using a within-trial (short-term), and a model-based (long-term) economic evaluation. 4. To develop an advanced automatic LUS image analysis method using machine learning to assist in LUS diagnosis and risk stratification.